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NEW ROBOT: SOLAR PANEL DEPLOYER 🌞 San Francisco Gritt (a.k.a. Gritt AI) is probably one of the hottest robotics startups you have never heard of. Founded in 2023 by two CMU roboticists, it came out of stealth on July 21, 2026 with a $26M Series A ,led by Obvious... Ventures . Gritt does not make robots. It bolts an off-the-shelf Kawasaki robotic arm onto heavy equipment for construction, and runs its own AI to unload utility-scale solar panels. They also carry them, and set them onto metal racking with sub-millimeter precision. A human then has to fasten them. It replaces the manual overhead lifting of ~100-lb glass panels on solar farms. Solar is for now their only market, but they will expand to data centers (of course) and other large infrastructures. Solar was chosen first because it's the most factory-like task on an outdoor site. Gritt is making the bet on buying, while most of its competitors are building the robots. Their own capex is therefore near-zero, and moves the scaling constraint to ops crews and software -> it does not make it necessarily easier! All depends where your strengths lie. Gritt has currently two systems deployed in the field, over signed contracts to install ~2.8–3 GW of solar over 18 months. They plan to reach 48 systems within six months -> a ~24× deployment ramp in half a year! Gritt says the first skills took weeks to train but rebar tying took a single day on the same software pipeline after the solar work. That's a ~10–30× drop in per-skill training cost! And goes against the idea that deployment data is near-worthless, since novelty is the scarce input. Also worth mentioning: Gritt has a Chinese competitors, Trinabot. Trinabot is vertically integrated: tied to Trina, a giant Chinese solar-panel maker. Make the panel and the robot that installs it, while Gritt's hardware-agnostic. Interesting to see another example where China integrates manufacturing plus robot, and the US startup goes capex-light software on rented equipment.show more

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You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 просмотров • 9 дней назад

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 →

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 просмотров • 1 месяц назад

How this isn’t being covered by our local media is beyond me. How this isn’t NATIONAL NEWS is beyond me. Here you have an entire county, two towns, and a nonprofit suing to protect itself from authoritarian solar overreach out of Albany. This same county, the Town of Glen, and GlenFARMLand also sued a few months ago over Mill Point Solar. That is 7 total lawsuits out of one county in the span of 3 months in an effort to protect itself. Why do we all need to sue? ORES was created in a way where it supersedes local zoning and moratoriums, as well as our home rule that is granted to New York citizens in our state constitution. It does it all to forcibly site wind and solar against the will of the rural residents, town supervisors, and county attorneys. And legacy media wonders why it’s dying in our world today. What a joke.

How this isn’t being covered by our local media is beyond me. How this isn’t NATIONAL NEWS is beyond me. Here you have an entire county, two towns, and a nonprofit suing to protect itself from authoritarian solar overreach out of Albany. This same county, the Town of Glen, and GlenFARMLand also sued a few months ago over Mill Point Solar. That is 7 total lawsuits out of one county in the span of 3 months in an effort to protect itself. Why do we all need to sue? ORES was created in a way where it supersedes local zoning and moratoriums, as well as our home rule that is granted to New York citizens in our state constitution. It does it all to forcibly site wind and solar against the will of the rural residents, town supervisors, and county attorneys. And legacy media wonders why it’s dying in our world today. What a joke.

Alex Fasulo

15,148 просмотров • 3 месяцев назад

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

AI just hit a wall that no amount of money can move. The planet itself. There is not enough power, water, or land on Earth to build the data centers the AI race now demands. So the most valuable bet in artificial intelligence is no longer a chip company or a model. It is a rocket company. The plan is to leave. In January, SpaceX filed with the FCC to launch up to 1 million solar-powered data center satellites into orbit. In February it bought xAI, the maker of Grok, folding an entire frontier AI lab into a rocket company in the largest corporate merger ever recorded. On June 8 it unveiled the AI1, a compute satellite with a 70-meter wingspan, wider than a Boeing 747, powered by the sun, cooled by the vacuum of space, and wired to the ground through Starlink. Four days later it went public in the largest IPO in history, near 1.77 trillion dollars, touched 2.1 trillion on its first day, raised close to 86 billion, and made one man the first trillionaire alive. Now read the direction of that merger, because it is the whole story. A rocket company bought the AI lab. Not the reverse. For three years everyone assumed the constraint on AI was chips, or data, or talent. It is none of them anymore. It is energy and heat and dirt. The head of Anthropic said his company grew faster than the exponential, 80 times in a single year, and that is exactly why it ran out of compute. The answer was not to build more data centers in Virginia. It was to leave the atmosphere, where the sun never sets and a solar panel does five times the work. The moat in artificial intelligence is no longer the model. It is the launch. And the first rent is already being paid. A rival lab, Anthropic, is reported to be sending roughly 1.25 billion dollars a month to Musk for compute. Google near 920 million. If intelligence moves to orbit, the company that owns the only affordable road there becomes the landlord of the next layer of the internet, the way one bookstore became the landlord of the cloud. The merger is the proof of concept. The IPO is the war chest. Those monthly checks are the lease. Here is the part the price tag does not want you to read. Close to a trillion dollars of that valuation rests on orbital data centers that do not yet exist, and on a chip factory, Terafab, that SpaceX's own public filing calls a general framework with no binding deal, one that may not achieve commercial viability. Musk said it on camera. This is not a promise. The largest IPO ever written is priced on a future the filing itself cannot verify. The other side is just as real. Compute in orbit costs about four times what it costs on the ground today, and the curve may not cross for fifteen years. The machines that print the chips are backordered for years. Shedding heat in a vacuum at this scale has never been done. Musk's timelines have a long history of meaning later. And Bezos is racing the same orbit with a constellation of 51,600 satellites of his own. But strip it all away and the trade underneath is one sentence. Earth has run out of room for intelligence, and whoever owns the road off the planet owns whatever gets built next. Call it the most expensive science fiction ever sold, or the first time the map of the internet pointed up.

Shanaka Anslem Perera ⚡

54,516 просмотров • 3 месяцев назад

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

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 просмотров • 1 месяц назад

"PRICE IS WHAT YOU PAY. VALUE IS WHAT YOU GET." I keep buying $Kekec and I have a strong conviction. Here's Why: While the market is down, and Kekec is declining with it, there are data points that few are considering. Kekec borned in October and since then has been posting a different and original 30-second video every day, which I find extremely funny. For the past couple of months, they have also been posting daily on Instagram, and the attention on Kekec (which doesn't present itself on social media as a memecoin) is growing, moreover, it's increasing exponentially. The number of followers is increasing by about 500-1000 a day. This is largely due to the fact that they are not just focused on the main account but have several others that post reels and redirect to the main one. In short, an excellent strategy to keep growing more and more. Instagram link: Guess What? Not only are the followers increasing, but the team's workload is also growing. In fact, for a little over a month, they have also started pushing on YouTube, and the data here is promising as well. YouTube link: If we want to make a comparison, we can take Pudgy Penguins as an example, which has shown it can reach millions and millions of users without mentioning that they are a WEB3 company that owns an NFT collection. Or, if we want to be more appropriate by comparing one memecoin to another, we could take PONKE. Thanks to the use of social media and the quality of their content, they managed to achieve incredible numbers, which then translated into an increase in the coin's price. Kekec came before PONKE, but that doesn't necessarily mean it's better than PONKE. I believe PONKE is unbeatable in terms of content, but I want to make you reflect on an important point. PONKE came after KEKEC, and after PONKE's success, many coins have emerged trying to imitate it. One of KEKEC's strengths, in my opinion, is precisely the fact that it leverages social media without being a copy-paste. Instead, it is a unique meme derived from a 90's film, and it uses a unique form of content. In short, KEKEC > KEKEC and no one else. I want to conclude by suggesting you follow them on Instagram and evaluate not only the exponential growth of their followers day by day but also observe how the views of each reel increase accordingly. Pay special attention to the comments. Many of the people commenting have no idea what it is, and you can see from the comments how Kekec generates particular emotions in people—strange but still emotions. Personally, I believe that when something is unique and even very strange, it needs time to be adopted. However, once it happens, it usually explodes and spreads like never before. A few days ago, a Kekec video was posted by a very popular meme page. They probably don't know what Kekec is about but thought the video could spark interest among their followers. How many other pages will do the same? Lastly, but not least, I want to point out how Kekec maintains a good market cap despite everything that has happened in the crypto world since October 2023. As far as I know and have personally observed, everything is extremely organic. There is no cabal behind it, and the quality is not reflected in a single jpeg but in work that has been ongoing daily for months. Every day they work harder, and the quality of their videos grows as well. I have no affiliations with the team, but I believe that Kekec truly deserves more in this world where we push celebrity or cabal-backed coins to hundreds of millions in market cap. I keep buying because the numbers suggest so. Don't just evaluate the chart (price), evaluate the data (value). BÂLKÂN DWÂRF

"PRICE IS WHAT YOU PAY. VALUE IS WHAT YOU GET." I keep buying $Kekec and I have a strong conviction. Here's Why: While the market is down, and Kekec is declining with it, there are data points that few are considering. Kekec borned in October and since then has been posting a different and original 30-second video every day, which I find extremely funny. For the past couple of months, they have also been posting daily on Instagram, and the attention on Kekec (which doesn't present itself on social media as a memecoin) is growing, moreover, it's increasing exponentially. The number of followers is increasing by about 500-1000 a day. This is largely due to the fact that they are not just focused on the main account but have several others that post reels and redirect to the main one. In short, an excellent strategy to keep growing more and more. Instagram link: Guess What? Not only are the followers increasing, but the team's workload is also growing. In fact, for a little over a month, they have also started pushing on YouTube, and the data here is promising as well. YouTube link: If we want to make a comparison, we can take Pudgy Penguins as an example, which has shown it can reach millions and millions of users without mentioning that they are a WEB3 company that owns an NFT collection. Or, if we want to be more appropriate by comparing one memecoin to another, we could take PONKE. Thanks to the use of social media and the quality of their content, they managed to achieve incredible numbers, which then translated into an increase in the coin's price. Kekec came before PONKE, but that doesn't necessarily mean it's better than PONKE. I believe PONKE is unbeatable in terms of content, but I want to make you reflect on an important point. PONKE came after KEKEC, and after PONKE's success, many coins have emerged trying to imitate it. One of KEKEC's strengths, in my opinion, is precisely the fact that it leverages social media without being a copy-paste. Instead, it is a unique meme derived from a 90's film, and it uses a unique form of content. In short, KEKEC > KEKEC and no one else. I want to conclude by suggesting you follow them on Instagram and evaluate not only the exponential growth of their followers day by day but also observe how the views of each reel increase accordingly. Pay special attention to the comments. Many of the people commenting have no idea what it is, and you can see from the comments how Kekec generates particular emotions in people—strange but still emotions. Personally, I believe that when something is unique and even very strange, it needs time to be adopted. However, once it happens, it usually explodes and spreads like never before. A few days ago, a Kekec video was posted by a very popular meme page. They probably don't know what Kekec is about but thought the video could spark interest among their followers. How many other pages will do the same? Lastly, but not least, I want to point out how Kekec maintains a good market cap despite everything that has happened in the crypto world since October 2023. As far as I know and have personally observed, everything is extremely organic. There is no cabal behind it, and the quality is not reflected in a single jpeg but in work that has been ongoing daily for months. Every day they work harder, and the quality of their videos grows as well. I have no affiliations with the team, but I believe that Kekec truly deserves more in this world where we push celebrity or cabal-backed coins to hundreds of millions in market cap. I keep buying because the numbers suggest so. Don't just evaluate the chart (price), evaluate the data (value). BÂLKÂN DWÂRF

m0ment0

133,250 просмотров • 2 лет назад

🚨 CHINA IS RACING TO BUILD A SPACE SOLAR POWER PLANT THAT COULD BEAM ELECTRICITY FROM ORBIT TO EARTH. Scientists at Xidian University have successfully tested a ground-based system that can wirelessly transmit kilowatt-level power over 100 meters using microwaves. Their ultimate goal is far more ambitious: placing large solar power stations in geostationary orbit (36,000 km up), where sunlight is available 24/7 with no weather or atmosphere blocking it. The project, called Zhuri (“chasing the sun”), uses mirrors to concentrate sunlight onto solar panels, converts the electricity into microwaves, and beams it down to a receiving antenna (rectenna) on Earth. Why this matters: • In space, solar energy is up to 6 times more efficient than on Earth because there’s no night, clouds, or atmospheric filtering • A single large space solar station could theoretically generate gigawatts of continuous clean power enough for millions of homes • The team has already proven the system can beam power to multiple moving targets at once • China is now among the world leaders in this technology, alongside the US and Japan The deeper implication: Space-based solar power has been a dream for decades because it could provide truly baseload renewable energy. While the technical and financial challenges are enormous (building massive structures in orbit, precise microwave beaming, and safety), steady progress like this brings the concept closer to reality. If successful, it could fundamentally change how humanity generates and distributes energy moving power collection off the planet entirely. Near-term applications could include wirelessly charging satellites or powering future lunar bases. Do you think space-based solar power will become a major energy source in the coming decades, or will it stay too expensive and complex? Follow for more frontier energy and space technology developments.

🚨 CHINA IS RACING TO BUILD A SPACE SOLAR POWER PLANT THAT COULD BEAM ELECTRICITY FROM ORBIT TO EARTH. Scientists at Xidian University have successfully tested a ground-based system that can wirelessly transmit kilowatt-level power over 100 meters using microwaves. Their ultimate goal is far more ambitious: placing large solar power stations in geostationary orbit (36,000 km up), where sunlight is available 24/7 with no weather or atmosphere blocking it. The project, called Zhuri (“chasing the sun”), uses mirrors to concentrate sunlight onto solar panels, converts the electricity into microwaves, and beams it down to a receiving antenna (rectenna) on Earth. Why this matters: • In space, solar energy is up to 6 times more efficient than on Earth because there’s no night, clouds, or atmospheric filtering • A single large space solar station could theoretically generate gigawatts of continuous clean power enough for millions of homes • The team has already proven the system can beam power to multiple moving targets at once • China is now among the world leaders in this technology, alongside the US and Japan The deeper implication: Space-based solar power has been a dream for decades because it could provide truly baseload renewable energy. While the technical and financial challenges are enormous (building massive structures in orbit, precise microwave beaming, and safety), steady progress like this brings the concept closer to reality. If successful, it could fundamentally change how humanity generates and distributes energy moving power collection off the planet entirely. Near-term applications could include wirelessly charging satellites or powering future lunar bases. Do you think space-based solar power will become a major energy source in the coming decades, or will it stay too expensive and complex? Follow for more frontier energy and space technology developments.

TheNewPhysics

15,361 просмотров • 3 месяцев назад

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Furniture assembly is the task everyone name-drops and nobody actually attempts at real scale. Every demo I have seen is a scaled down IKEA leg or a single arm on a toy chair. This paper does it properly, real scale, bimanual, up to 7 subtasks and 1,550 control steps per episode, and it is validated on a real Kinova Gen3, not just in sim. That real-robot number is the one that matters: only a 16 percent drop on the hardest task going from simulation to hardware. That is a small enough gap to take seriously, and it did not happen by accident. They built a VR teleoperation rig specifically for coordinated dual-arm collection, because generic single-arm teleop setups do not capture the coordination real assembly needs, and the model predicts a continuous progress signal alongside the action chunk rather than a discrete subtask label, letting it auto-transition and catch drift before it compounds into total failure. The simulation ablation is what got them there, 48 to 80 percent over baselines, with another 21 points from their perception and control design study alone, but that is groundwork, not the headline. Watch the video, there is a clip of the robot misgrasping the seat panel, reopening the gripper, and regrasping on its own. That is not scripted recovery behaviour, it emerged from training, and it emerged on hardware. Excellent work from the team from Mitsubishi Electric Research Laboratories, with Oxford and UNC Chapel Hill Clinical Laboratory Science. Video and project page in comments. #Robotics #Manipulation #VLA

Stephen James

14,952 просмотров • 2 месяцев назад

$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 $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.$

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 says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.

Future Memo

18,925 просмотров • 13 дней назад

$🚨SPACEX DIDN'T ACQUIRE CURSOR FOR TALENT A rocket company buying software for the largest startup acquisition in history Here's what almost nobody realizes: They didn't buy a talent or a coding app. They bought the data underneath it. Cursor's users generate ~150 million lines of code a day. Every keystroke is a record of how real engineers build and fix software The exact raw material to train an AI that can code. You can't buy that on a market. You have to own the thing that produces it. So SpaceX bought a permanent data firehose for Grok. The product is almost a side effect. The entire $60B was covered by a fraction of their market-cap gains since the IPO This is a catch-up bet with Anthropic and openAI You see how they are slowly building it don't you?$

🚨SPACEX DIDN'T ACQUIRE CURSOR FOR TALENT A rocket company buying software for the largest startup acquisition in history Here's what almost nobody realizes: They didn't buy a talent or a coding app. They bought the data underneath it. Cursor's users generate ~150 million lines of code a day. Every keystroke is a record of how real engineers build and fix software The exact raw material to train an AI that can code. You can't buy that on a market. You have to own the thing that produces it. So SpaceX bought a permanent data firehose for Grok. The product is almost a side effect. The entire $60B was covered by a fraction of their market-cap gains since the IPO This is a catch-up bet with Anthropic and openAI You see how they are slowly building it don't you?

Frogify

35,571 просмотров • 1 месяц назад

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

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

RoboHub🤖

16,818 просмотров • 4 месяцев назад

China now owns the world's largest and most tunnel boring machines (TBMs). 70% of the world's total are made in China. But this dominance is hard-earned. In 1997, to build the Qinling Tunnel, China paid Germany RMB 700 million for just 2 SECONDHAND TBMs. 700 million is an entire region’s tax revenue back then. But the 700 million didn't include support. China also had to hire German engineers for €800/day (≈RMB 6,800), while the average Chinese worker earned less than RMB 4,000 a year. Back then, the global market was locked by the US, Japan, and Germany. China was being robbed, simply because it couldn't produce its own. In 2002, China began the painful journey of self-development of TBM, which has over 30,000 precision parts. After 6 years, in 2008, China's first homegrown TBM was born. In a 2012 head-to-head test, it matched the best in the world. Today, Chinese TBMs lead the world. Dignity is earned. This is just an example in TBM. In humanoid robots, in electric cars, in solar farms, in drones...China has plenty of examples that make the country strong again.

China now owns the world's largest and most tunnel boring machines (TBMs). 70% of the world's total are made in China. But this dominance is hard-earned. In 1997, to build the Qinling Tunnel, China paid Germany RMB 700 million for just 2 SECONDHAND TBMs. 700 million is an entire region’s tax revenue back then. But the 700 million didn't include support. China also had to hire German engineers for €800/day (≈RMB 6,800), while the average Chinese worker earned less than RMB 4,000 a year. Back then, the global market was locked by the US, Japan, and Germany. China was being robbed, simply because it couldn't produce its own. In 2002, China began the painful journey of self-development of TBM, which has over 30,000 precision parts. After 6 years, in 2008, China's first homegrown TBM was born. In a 2012 head-to-head test, it matched the best in the world. Today, Chinese TBMs lead the world. Dignity is earned. This is just an example in TBM. In humanoid robots, in electric cars, in solar farms, in drones...China has plenty of examples that make the country strong again.

Li Zexin 李泽欣

865,666 просмотров • 1 месяц назад

Why Having a Satellite Network is So Important In 2023, the Russians were about to close a deal with a certain North Korean MLRS, but the deal fell through exactly because the North Koreans, +20 years ago, had revolted against Russian opposition to their nuclear program and switched from GLONASS to BeiDou. Only few years ago, the NK returned to Glonass and started converting their equipment to work with dual GNSS guidance. But why did the deal sour? Because the Chinese did not authorize the use of their satellites in the Ukraine conflict. The same problem occurred with the Belarusian POLONEZ MLRS, which uses missiles based on Chinese technology. Today, Iran has switched from GPS to BeiDou, aiming for greater resistance to jammers and integration with Chinese systems. This shows that a missile program is much more than the missiles themselves. It is necessary to have one's own constellation, even if it is strictly military and regionalized, with resources for monitoring and target acquisition, in addition to the ability to deal with jammers and spoofers. When I mentioned Iran and BeiDou, it is the beginning and serves as a gateway that enables integration with Chinese networks, provided the Chinese decide to allow it. However, it is essentially a massive gateway, with numerous smaller, more specialized sub-channels operating underneath. For targeting moving objects, Iran would need to receive data from the Guowang or Yaogan networks. In the last six months, the Chinese could have provided partial integration. At this stage, since the Iranians are conducting their ISR primarily with drones, I believe the Chinese are not sharing data from the LEO satellites in those networks. Iran also has its own satellites, but the Chinese network is far more mature and likely equipped with a wide array of integration and data-sharing tools. While the Chinese are providing intelligence to Iran, I believe they are prudent in doing so to preserve the relationships they have built with other Arab states. At this moment, I believe that the sharing of intelligence from satellites isn’t in real time. For a country like Iran, it is crucial to have GNSS independence with its own program. The same applies to other medium-sized countries, which need at least an LEO constellation capable of providing the minimum ISR, and this is linked to security, but also a series of other factors. I'll give a practical example here. Drones usually lose link with 50% of their range in the Amazon due to weather conditions. With an LEO constellation, this would not occur. Today, to have independence, a constellation project is necessary.

Why Having a Satellite Network is So Important In 2023, the Russians were about to close a deal with a certain North Korean MLRS, but the deal fell through exactly because the North Koreans, +20 years ago, had revolted against Russian opposition to their nuclear program and switched from GLONASS to BeiDou. Only few years ago, the NK returned to Glonass and started converting their equipment to work with dual GNSS guidance. But why did the deal sour? Because the Chinese did not authorize the use of their satellites in the Ukraine conflict. The same problem occurred with the Belarusian POLONEZ MLRS, which uses missiles based on Chinese technology. Today, Iran has switched from GPS to BeiDou, aiming for greater resistance to jammers and integration with Chinese systems. This shows that a missile program is much more than the missiles themselves. It is necessary to have one's own constellation, even if it is strictly military and regionalized, with resources for monitoring and target acquisition, in addition to the ability to deal with jammers and spoofers. When I mentioned Iran and BeiDou, it is the beginning and serves as a gateway that enables integration with Chinese networks, provided the Chinese decide to allow it. However, it is essentially a massive gateway, with numerous smaller, more specialized sub-channels operating underneath. For targeting moving objects, Iran would need to receive data from the Guowang or Yaogan networks. In the last six months, the Chinese could have provided partial integration. At this stage, since the Iranians are conducting their ISR primarily with drones, I believe the Chinese are not sharing data from the LEO satellites in those networks. Iran also has its own satellites, but the Chinese network is far more mature and likely equipped with a wide array of integration and data-sharing tools. While the Chinese are providing intelligence to Iran, I believe they are prudent in doing so to preserve the relationships they have built with other Arab states. At this moment, I believe that the sharing of intelligence from satellites isn’t in real time. For a country like Iran, it is crucial to have GNSS independence with its own program. The same applies to other medium-sized countries, which need at least an LEO constellation capable of providing the minimum ISR, and this is linked to security, but also a series of other factors. I'll give a practical example here. Drones usually lose link with 50% of their range in the Amazon due to weather conditions. With an LEO constellation, this would not occur. Today, to have independence, a constellation project is necessary.

Patricia Marins

19,508 просмотров • 7 месяцев назад

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

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

Teslaconomics

25,494 просмотров • 6 месяцев назад

Elon Musk is building a data center that no grid has to power and no county has to approve. Musk: “Think of it as a rack of compute in space.” Orbital hardware has always been custom. Each satellite is its own program, its own team, its own decade. SpaceX’s AI1 satellites are copies of each other. Each is built around a single Nvidia NVL72, the same rack-scale architecture running the largest data centers on the ground, redesigned to fly. Space stops being a program and becomes an inventory. Designing for orbit made the rack simpler and cheaper than the ground version, so SpaceX plans to run the space design in terrestrial data centers too. Racks alone have never made a data center. A thousand of them in a building with no network between them is a thousand computers. The interconnect is what turns them into one machine. Training a frontier model means moving enormous volumes of data between processors without pause, and the whole system runs at the speed of its slowest link. That is why the fabric between chips is fought over as hard as the chips themselves. Musk: “And then you can connect these racks of compute to either each other by the laser links, or directly to the Starlink constellation.” Light moves through glass at about two thirds of its speed in a vacuum, and fiber optic cable is glass. Every backbone route and undersea cable on Earth pays that tax. A laser between two satellites pays nothing. Over long distances, an orbital link arrives sooner than the same run in fiber. Orbit was supposed to be a trade, unlimited power in exchange for a worse network. The network is faster. The compute plugs into a delivery layer that is already finished. Thousands of satellites are up there right now carrying laser links, terminating at ground stations and dishes across most of the planet. The last mile was built years before the thing it delivers existed. Anyone else attempting this needs the rocket, the satellite bus, the solar manufacturing, the laser mesh, the ground network, and a supply line to a chipmaker. SpaceX had five of them before the project had a name. None of it was built for this. Starlink existed to sell internet subscriptions. The lasers existed so those subscriptions would work over open ocean. Starship existed for Mars. Nobody planned this. It assembled itself out of problems solved for other reasons. That is what twenty years of hard engineering leaves behind. Every previous expansion of human capability ran on a resource sitting inside somebody’s borders. Coal, oil, water, land. Sunlight in orbit belongs to nobody and never runs out. Compute still has an address. A building in Virginia, a substation, a county that had to approve it, a grid connection someone waited four years for. He is describing a version that only has an altitude.

Elon Musk is building a data center that no grid has to power and no county has to approve. Musk: “Think of it as a rack of compute in space.” Orbital hardware has always been custom. Each satellite is its own program, its own team, its own decade. SpaceX’s AI1 satellites are copies of each other. Each is built around a single Nvidia NVL72, the same rack-scale architecture running the largest data centers on the ground, redesigned to fly. Space stops being a program and becomes an inventory. Designing for orbit made the rack simpler and cheaper than the ground version, so SpaceX plans to run the space design in terrestrial data centers too. Racks alone have never made a data center. A thousand of them in a building with no network between them is a thousand computers. The interconnect is what turns them into one machine. Training a frontier model means moving enormous volumes of data between processors without pause, and the whole system runs at the speed of its slowest link. That is why the fabric between chips is fought over as hard as the chips themselves. Musk: “And then you can connect these racks of compute to either each other by the laser links, or directly to the Starlink constellation.” Light moves through glass at about two thirds of its speed in a vacuum, and fiber optic cable is glass. Every backbone route and undersea cable on Earth pays that tax. A laser between two satellites pays nothing. Over long distances, an orbital link arrives sooner than the same run in fiber. Orbit was supposed to be a trade, unlimited power in exchange for a worse network. The network is faster. The compute plugs into a delivery layer that is already finished. Thousands of satellites are up there right now carrying laser links, terminating at ground stations and dishes across most of the planet. The last mile was built years before the thing it delivers existed. Anyone else attempting this needs the rocket, the satellite bus, the solar manufacturing, the laser mesh, the ground network, and a supply line to a chipmaker. SpaceX had five of them before the project had a name. None of it was built for this. Starlink existed to sell internet subscriptions. The lasers existed so those subscriptions would work over open ocean. Starship existed for Mars. Nobody planned this. It assembled itself out of problems solved for other reasons. That is what twenty years of hard engineering leaves behind. Every previous expansion of human capability ran on a resource sitting inside somebody’s borders. Coal, oil, water, land. Sunlight in orbit belongs to nobody and never runs out. Compute still has an address. A building in Virginia, a substation, a county that had to approve it, a grid connection someone waited four years for. He is describing a version that only has an altitude.

Dustin

13,444 просмотров • 1 месяц назад

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Alright, now that we know what an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 просмотров • 10 месяцев назад

JUST IN: Bank of America just told its clients to take profits. About 70% of its bear-market signals are flashing, a level it typically reaches only near market tops. Weeks earlier, BofA's own fund manager survey showed the largest one-month jump into stocks ever recorded, with cash down to 3.9%, under the 4% line the bank treats as a sell signal. Read those together. Investors made their biggest dash into equities in the survey's history at almost the exact moment BofA's own indicators say the top is near. But the number that should actually stop you is buried in the note, and almost nobody is quoting it. The companies driving this entire rally, the AI hyperscalers, are on track to spend nearly 100% of their operating cash flow on capex by year-end. In 2023 that figure was 40%. Sit with that. Big tech used to throw off cash and hand it back through buybacks, which lifted the stocks. Now it is pouring almost every dollar it generates into chips and data centers. BofA notes buybacks have slowed and cash conversion has flat-lined. The engine of the rally is consuming the fuel that powered the stocks. It is the same $725 billion build that companies are now blaming for layoffs. The whole market is priced on one bet, and that bet has grown large enough to eat the cash that used to support the share prices. This is not a crash call. BofA's year-end target is 7,100, about 4% below today, and the median outcome after this cash signal since 2011 has been a 1% dip, not a collapse. The posts screaming sell everything are wrong. The real message is quieter. You are being paid less and less to stay, while the engine runs hotter and hotter.

JUST IN: Bank of America just told its clients to take profits. About 70% of its bear-market signals are flashing, a level it typically reaches only near market tops. Weeks earlier, BofA's own fund manager survey showed the largest one-month jump into stocks ever recorded, with cash down to 3.9%, under the 4% line the bank treats as a sell signal. Read those together. Investors made their biggest dash into equities in the survey's history at almost the exact moment BofA's own indicators say the top is near. But the number that should actually stop you is buried in the note, and almost nobody is quoting it. The companies driving this entire rally, the AI hyperscalers, are on track to spend nearly 100% of their operating cash flow on capex by year-end. In 2023 that figure was 40%. Sit with that. Big tech used to throw off cash and hand it back through buybacks, which lifted the stocks. Now it is pouring almost every dollar it generates into chips and data centers. BofA notes buybacks have slowed and cash conversion has flat-lined. The engine of the rally is consuming the fuel that powered the stocks. It is the same $725 billion build that companies are now blaming for layoffs. The whole market is priced on one bet, and that bet has grown large enough to eat the cash that used to support the share prices. This is not a crash call. BofA's year-end target is 7,100, about 4% below today, and the median outcome after this cash signal since 2011 has been a 1% dip, not a collapse. The posts screaming sell everything are wrong. The real message is quieter. You are being paid less and less to stay, while the engine runs hotter and hotter.

Shanaka Anslem Perera ⚡

17,270 просмотров • 3 месяцев назад

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,177,703 просмотров • 2 месяцев назад