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Robotics has transformed welding speed. Precision is great, but speed now matters just as much. And conventional TIG still lags behind... Balancing quality with speed, especially on pipe, pressure vessel, and roll welding projects. This solution helps change that. It delivers TIG-level quality with: ✅ Up to 300% faster... show more

Ilir Aliu

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Robot dogs are now welders! 👨🏼‍🏭 Path Robotics launches a mobile welding robot on quadruped platform, called Rove. This quadruped robotic welding system is paired with their Obsidian physical AI model. Obsidian already delivers autonomous, adaptive welding inside fixed cells. Rove brings intelligent welding to the part rather than moving the part to a fixed workstation. Shipbuilding, heavy construction, and large-scale fabrication share a constraint, massive assemblies and workpieces that cannot be moved or fixtured inside welding cells. These conditions have limited welding automation in environments that need it most, just as skilled welder shortages reach critical levels. Legged robots have long been considered too unstable for precision welding. Rove changes that. Obsidian provides the perception and adaptability needed to operate in high-variability environments. Saronic Technologies, a leader in autonomous maritime vessels, is one of the first early adopters, integrating Rove into its physical AI-powered shipbuilding operations in Franklin, Louisiana. 🇺🇸 Welding requires millimeter-level precision on a moving quadruped platform, considered impossible until AI-based perception and control made it feasible. 🔥 Andrew Lonsberry gotta be honest, but a welding robot dog wasn't on bingo card for 2026. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Robot dogs are now welders! 👨🏼‍🏭 Path Robotics launches a mobile welding robot on quadruped platform, called Rove. This quadruped robotic welding system is paired with their Obsidian physical AI model. Obsidian already delivers autonomous, adaptive welding inside fixed cells. Rove brings intelligent welding to the part rather than moving the part to a fixed workstation. Shipbuilding, heavy construction, and large-scale fabrication share a constraint, massive assemblies and workpieces that cannot be moved or fixtured inside welding cells. These conditions have limited welding automation in environments that need it most, just as skilled welder shortages reach critical levels. Legged robots have long been considered too unstable for precision welding. Rove changes that. Obsidian provides the perception and adaptability needed to operate in high-variability environments. Saronic Technologies, a leader in autonomous maritime vessels, is one of the first early adopters, integrating Rove into its physical AI-powered shipbuilding operations in Franklin, Louisiana. 🇺🇸 Welding requires millimeter-level precision on a moving quadruped platform, considered impossible until AI-based perception and control made it feasible. 🔥 Andrew Lonsberry gotta be honest, but a welding robot dog wasn't on bingo card for 2026. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

10,227 Aufrufe • vor 4 Monaten

A simple CNC machine. Wood. A rotating tool. And yet the pattern shifts from circles to perfect hexagons with a level of accuracy most factories still struggle to hit. I worked five years in manufacturing at Siemens, and this is the part people often overlook. The real magic is not new or loud. It is stability. Repeatability. Tool paths that were tuned forever. Precision that just keeps working. Robotics is in a hype cycle right now, but manufacturing has delivered quiet perfection long before anyone talked about foundation models. Sometimes the most interesting engineering is the part that does not ask for attention. —- Weekly robotics and AI insights. Subscribe free:

A simple CNC machine. Wood. A rotating tool. And yet the pattern shifts from circles to perfect hexagons with a level of accuracy most factories still struggle to hit. I worked five years in manufacturing at Siemens, and this is the part people often overlook. The real magic is not new or loud. It is stability. Repeatability. Tool paths that were tuned forever. Precision that just keeps working. Robotics is in a hype cycle right now, but manufacturing has delivered quiet perfection long before anyone talked about foundation models. Sometimes the most interesting engineering is the part that does not ask for attention. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

164,829 Aufrufe • vor 9 Monaten

These machines don’t look like they build the future of AI. But they do. What you are seeing is not an industrial robot just move. It is part of the process that makes the world’s most advanced chips possible. TRUMPF laser systems are used to manufacture key components for ASML’s EUV lithography machines. ❗️Without this step, modern AI hardware would simply not exist. • They machine parts with nanometer-level precision • They enable the optics and components needed for EUV lithography • Their stability directly affects chip yield and quality • No TRUMPF lasers, no ASML EUV. No EUV, no advanced AI chips. It is easy to focus on GPUs and models. But the real bottleneck sits much deeper in the industrial stack. ——- Weekly robotics and AI insights. Subscribe free:

These machines don’t look like they build the future of AI. But they do. What you are seeing is not an industrial robot just move. It is part of the process that makes the world’s most advanced chips possible. TRUMPF laser systems are used to manufacture key components for ASML’s EUV lithography machines. ❗️Without this step, modern AI hardware would simply not exist. • They machine parts with nanometer-level precision • They enable the optics and components needed for EUV lithography • Their stability directly affects chip yield and quality • No TRUMPF lasers, no ASML EUV. No EUV, no advanced AI chips. It is easy to focus on GPUs and models. But the real bottleneck sits much deeper in the industrial stack. ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

43,033 Aufrufe • vor 11 Tagen

These machines don’t look like they build the future of AI. But they do. What you are seeing is not an industrial robot just move. It is part of the process that makes the world’s most advanced chips possible. TRUMPF laser systems are used to manufacture key components for ASML’s EUV lithography machines. ❗️Without this step, modern AI hardware would simply not exist. • They machine parts with nanometer-level precision • They enable the optics and components needed for EUV lithography • Their stability directly affects chip yield and quality • No TRUMPF lasers, no ASML EUV. No EUV, no advanced AI chips. It is easy to focus on GPUs and models. But the real bottleneck sits much deeper in the industrial stack. —- Weekly robotics and AI insights. Subscribe free:

These machines don’t look like they build the future of AI. But they do. What you are seeing is not an industrial robot just move. It is part of the process that makes the world’s most advanced chips possible. TRUMPF laser systems are used to manufacture key components for ASML’s EUV lithography machines. ❗️Without this step, modern AI hardware would simply not exist. • They machine parts with nanometer-level precision • They enable the optics and components needed for EUV lithography • Their stability directly affects chip yield and quality • No TRUMPF lasers, no ASML EUV. No EUV, no advanced AI chips. It is easy to focus on GPUs and models. But the real bottleneck sits much deeper in the industrial stack. —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

99,899 Aufrufe • vor 7 Monaten

$Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:$

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 Aufrufe • vor 9 Monaten

Last night, China Central Television (CCTV) aired its 2026 Chinese New Year Gala celebrating the Year of the Horse. The show featured a wide range of performances, including Unitree Robotics humanoid robots performing martial arts in sync with human dancers. Just a year ago, Unitree’s robots appeared at the same gala, but their movements looked stiff and mechanical. This year, they were noticeably more fluid and coordinated — a remarkable improvement, even if they’re still likely operating under some level of remote supervision. When it comes to humanoid robotics, most of the visible momentum today seems to be coming from the U.S. and China. Companies like Tesla (with Optimus) and Boston Dynamics in the U.S., alongside rapidly advancing Chinese firms, dominate the headlines. So what happened to Europe and Japan? Japan was once seen as the global leader, especially with Honda’s ASIMO and SoftBank Robotics’ humanoid projects. However, ASIMO was retired, and much of Japan’s robotics focus shifted toward industrial automation and service robots rather than full-scale general-purpose humanoids. Europe, meanwhile, remains strong in industrial robotics, research, and precision engineering — with players like ABB and KUKA — but hasn’t pushed aggressively into commercial humanoid platforms at the same scale or speed as the U.S. and China. In short, it’s less that Europe and Japan disappeared, and more that the center of gravity in humanoid robotics — especially AI-driven, general-purpose humanoids — has shifted toward U.S.–China competition. Whether that gap widens or narrows will depend on breakthroughs in embodied AI, cost reduction, and real-world deployment over the next few years.

Last night, China Central Television (CCTV) aired its 2026 Chinese New Year Gala celebrating the Year of the Horse. The show featured a wide range of performances, including Unitree Robotics humanoid robots performing martial arts in sync with human dancers. Just a year ago, Unitree’s robots appeared at the same gala, but their movements looked stiff and mechanical. This year, they were noticeably more fluid and coordinated — a remarkable improvement, even if they’re still likely operating under some level of remote supervision. When it comes to humanoid robotics, most of the visible momentum today seems to be coming from the U.S. and China. Companies like Tesla (with Optimus) and Boston Dynamics in the U.S., alongside rapidly advancing Chinese firms, dominate the headlines. So what happened to Europe and Japan? Japan was once seen as the global leader, especially with Honda’s ASIMO and SoftBank Robotics’ humanoid projects. However, ASIMO was retired, and much of Japan’s robotics focus shifted toward industrial automation and service robots rather than full-scale general-purpose humanoids. Europe, meanwhile, remains strong in industrial robotics, research, and precision engineering — with players like ABB and KUKA — but hasn’t pushed aggressively into commercial humanoid platforms at the same scale or speed as the U.S. and China. In short, it’s less that Europe and Japan disappeared, and more that the center of gravity in humanoid robotics — especially AI-driven, general-purpose humanoids — has shifted toward U.S.–China competition. Whether that gap widens or narrows will depend on breakthroughs in embodied AI, cost reduction, and real-world deployment over the next few years.

Ray

23,455 Aufrufe • vor 6 Monaten

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 Aufrufe • vor 1 Jahr

Mbappe does not have the IQ to play football “very” effectively without his speed. Calm, and don’t drag me yet. Do you know why Messi is still effective at his age despite being slower? It’s because he has learned how to play football without having to run too much. At some point, Mbappe’s speed will reduce, but he doesn’t have that much IQ to survive so much without it. On dead ball situations where he doesn’t have to run, just watch, he’s less effective. He can’t drop a mad line-breaking pass, he can’t wiggle around players. I mean, anything that doesn’t involve his speed, he’s less effective. Of course he’s done a lot without his speed, but he’s less potent, I hope you don’t misunderstand this part. Mbappe is deadliest on transition. Same with Barcola and Dembele. If a team is in shape, these guys are not effective, e.g Paraguay…you can also see the France team, they don’t intend to keep the ball, their only plan is to wait for you to lose it and they play it to those 4 guys, you are finished. Please listen to Thierry Henry. He said he was so quick and relied on his speed, a coach had to tell him at some point to play without his speed. This made him think more and play more with his brain. As Mbappe grows, his speed will slow down, he still has more than 10 years to play, and I hope he can learn that part where he can be effective without speed. Cristiano scored more after 30 years old when his speed had reduced. I hope you guys get my point, I know many will still misunderstand it.

Mbappe does not have the IQ to play football “very” effectively without his speed. Calm, and don’t drag me yet. Do you know why Messi is still effective at his age despite being slower? It’s because he has learned how to play football without having to run too much. At some point, Mbappe’s speed will reduce, but he doesn’t have that much IQ to survive so much without it. On dead ball situations where he doesn’t have to run, just watch, he’s less effective. He can’t drop a mad line-breaking pass, he can’t wiggle around players. I mean, anything that doesn’t involve his speed, he’s less effective. Of course he’s done a lot without his speed, but he’s less potent, I hope you don’t misunderstand this part. Mbappe is deadliest on transition. Same with Barcola and Dembele. If a team is in shape, these guys are not effective, e.g Paraguay…you can also see the France team, they don’t intend to keep the ball, their only plan is to wait for you to lose it and they play it to those 4 guys, you are finished. Please listen to Thierry Henry. He said he was so quick and relied on his speed, a coach had to tell him at some point to play without his speed. This made him think more and play more with his brain. As Mbappe grows, his speed will slow down, he still has more than 10 years to play, and I hope he can learn that part where he can be effective without speed. Cristiano scored more after 30 years old when his speed had reduced. I hope you guys get my point, I know many will still misunderstand it.

TobyWrites

1,235,695 Aufrufe • vor 1 Monat

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Teleoperation today relies on WebRTC. That works for Zoom. Not for robotics. Is that statement provocative? WebRTC works for video calls. But not really for robots. I’m teleoperating from SF → London using Adamo’s stack. Zero buffering. No lag smoothing… just real-time control. Because WebRTC apps add buffers to handle: • packet loss • network jitter That’s fine when you’re talking. Not so much when you’re controlling a robot. Even small delays kill precision. So instead of adapting WebRTC… Adamo built a custom protocol + dedicated global infrastructure. Without buffering or artificial delay. A stable, real-time connection across the Atlantic. This is what people underestimate about robotics: Models are just one piece of the puzzle. But looking at the entire stack, there is so much more. And Sometimes you can’t reuse what exists. You have to build it from the ground up. Thank you, Sam Considine for letting me try, it nice seeing you during GTC last week! 👋 ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

21,319 Aufrufe • vor 4 Monaten

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu Wang) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Are you kidding me??? It grasps multiple objects with different ways, all at once with… a single hand??? No pauses. 1x speed. GENE-26.5 is Genesis AI’s robotics-native multimodal foundation model. It’s trained on 200,000+ hours of real human hand data (motion, force, touch) and runs on a 54-DoF bimanual system: Scaling that human data 4x lifted real-robot success rates from 16.6% to 65.6% on long-horizon dexterous tasks!! Same model weights, zero fine-tuning for this exact sequence. You know those tiny coordinated movements you do without thinking…? Robots couldn’t reliably do that before. Now they can. Today. This is the video you’ll send to my friends, outside of our bubble, when they say “robots are still just demos.” Congrats to the entire team around Zhou Xian! Credit: Seen at Zu Wang (Zu Wang) Genesis official announcement for the full story + longer demo: ——— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

204,476 Aufrufe • vor 3 Monaten

Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️

Revolutionizing Bladder Cancer Diagnosis with ZayaAI We heard you Zayans, From our last videos we understood you all want a much easier to understand explanation of our AI models. So let's dive into the latest breakthrough in AI-powered healthcare, our Bladder Cancer Detection Model. This isn't just another AI tool, it's a game-changer for pathologists. Precision, Speed and Seamless Integration: Traditional diagnosis of urothelial carcinoma (bladder cancer) is often slow and prone to errors. Our AI model automatically detects low and high-grade tumors, along with lymphovascular invasion, with unmatched accuracy and speed. That means faster results, fewer mistakes and a more efficient workflow for medical professionals. Why This Matters: 1.Faster Diagnoses → AI speeds up the process, allowing pathologists to focus on critical cases. 2.Higher Accuracy → Reduces human error and reliance on costly, time-consuming tests. 3.Seamless Integration → No need for complex system overhauls. ZayaAI fits right into existing workflows. The Future of AI in Healthcare: Embracing our AI models isn’t just about using new technology, it’s an investment in a future where healthcare is more precise, efficient and patient-focused. Pathologists get to make faster, more informed decisions and ultimately, patient outcomes improve. This is how AI is redefining medical diagnostics and can help save lives. DeSci + AI = $ZAI ! 🔬⚕️

ZayaAI

18,015 Aufrufe • vor 1 Jahr

Polymarket added fees so most arbitrage bots died. Devs who spent months building strategies watched their edge disappear in one update. But a small group figured out how to stay profitable anyway. This wallet is making $27,000 every single day right now. $743,000 in 35 days. 31,566 predictions. Fully automated. And it is running the exact same strategy i broke down in my previous article. His wallet: < Here is exactly how it works and why fees didn't kill it: The bot trades crypto Up/Down markets across BTC, ETH, SOL and XRP simultaneously. Not randomly. With surgical precision on timing and entry price. Look at the realized moves: Bitcoin position turned $2,300 into $8,260. XRP position turned $10,105 into $22,100. Ethereum position turned $1,347 into $10,950. Those are not lucky trades. That is a system firing correctly at scale. The reason fees didn't destroy this bot is the same reason i explained before. Pure speed arbitrage bots died because their edge was margin - and fees ate the margin completely. This bot doesn't rely on tiny spread captures. It combines pair-sum arbitrage with precise entry timing to find windows where combined price is cheap enough that fees still leave meaningful profit on the table. The math only works at specific entry prices and specific timing windows. Most bots can't find those windows fast enough. This one can because the infrastructure is fast enough to compete. $27,000 per day from a strategy that survived the fee update while everyone else shut down. That is not luck. That is being technically ahead of the competition. The gap between bots that died and bots that kept printing was never the strategy. It was execution quality and infrastructure speed.

Polymarket added fees so most arbitrage bots died. Devs who spent months building strategies watched their edge disappear in one update. But a small group figured out how to stay profitable anyway. This wallet is making $27,000 every single day right now. $743,000 in 35 days. 31,566 predictions. Fully automated. And it is running the exact same strategy i broke down in my previous article. His wallet: < Here is exactly how it works and why fees didn't kill it: The bot trades crypto Up/Down markets across BTC, ETH, SOL and XRP simultaneously. Not randomly. With surgical precision on timing and entry price. Look at the realized moves: Bitcoin position turned $2,300 into $8,260. XRP position turned $10,105 into $22,100. Ethereum position turned $1,347 into $10,950. Those are not lucky trades. That is a system firing correctly at scale. The reason fees didn't destroy this bot is the same reason i explained before. Pure speed arbitrage bots died because their edge was margin - and fees ate the margin completely. This bot doesn't rely on tiny spread captures. It combines pair-sum arbitrage with precise entry timing to find windows where combined price is cheap enough that fees still leave meaningful profit on the table. The math only works at specific entry prices and specific timing windows. Most bots can't find those windows fast enough. This one can because the infrastructure is fast enough to compete. $27,000 per day from a strategy that survived the fee update while everyone else shut down. That is not luck. That is being technically ahead of the competition. The gap between bots that died and bots that kept printing was never the strategy. It was execution quality and infrastructure speed.

Punisher

34,222 Aufrufe • vor 3 Monaten

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

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

RoboHub🤖

60,151 Aufrufe • vor 4 Monaten

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

That's sick! 🤯 Genesis AI simulates robots playing yo-yo! 🪀 Genesis AI just open-sourced Genesis World 1.0, and it might be one of the most important infrastructure releases in robotics this year. Robotics is still bottlenecked by the 1× speed of the physical world. Every model needs to be tested on real hardware, slowly, expensively, with limited coverage. Genesis World 1.0 from Genesis AI flips that equation: One hour in reality becomes 100 days in simulation. That turns a wall-clock bottleneck into a compute problem. And compute problems are solvable. The technical stack they rebuilt from scratch is serious: → GPU-accelerated cross-platform compiler via Quadrants, 10x faster launch time and up to 4.6x runtime vs the initial Genesis release → Penetration-free multi-physics contact solvers, the thing that makes simulation actually trustworthy → Unified rigid AND deformable physics in a single engine → Nyx, a high-performance path-traced rendering engine purpose-built for physical AI The sim-to-real gap has historically been the graveyard of robotics research. Policies that work beautifully in simulation fall apart on real hardware. Genesis World 1.0 is a direct attack on that problem. And it's fully open-source. The companies that master simulation infrastructure will train better robots faster than anyone else. Find it here: Genesis World 1.0: Quadrants: Nyx: Theophile Gervet, Zhou Xian congrats! 👏🏼 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,061 Aufrufe • vor 2 Monaten

Every marketing leader I talk to is facing the same challenge: Scale 📶 The demand for content has exploded, but the systems behind it haven’t kept up. Teams are expected to launch more campaigns, in more markets, with more data, all while maintaining brand integrity and speed. Chat-based AI tools help individuals move faster. But they don’t solve the harder problem: scaling content creation across markets and audiences. Over the past year, we’ve seen the limits of chat-based AI inside large organizations. 📢 Today, we’re introducing ⊞ Jasper Grid ⊞ — the backbone of Content Pipelines. ⊞ Grid is a unified, no-code system that lets AI agents plan, produce, and optimize content at scale. It transforms structured inputs like briefs, data, and brand guidelines into launch-ready assets with speed and precision. Under the hood, Grid runs on a network of modular AI agents, a resilient scheduler, and deep integrations with the tools marketers already use — all governed by brand guardrails so teams can move fast without losing control. 🏆 The result: faster time to market, consistent brand execution, and true operational scale. AI is becoming infrastructure. The next generation of enterprise marketing will run on systems built for speed, precision, and trust. Proud of the Jasper team and our early customers who helped shape this moment.

Timothy Young

1,307,286 Aufrufe • vor 9 Monaten

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

Japan Just Built a HouseBot You Control Without Speaking and It Changes Everything! Donut Robotics has officially unveiled its first bipedal humanoid, Cinnamon 1, and instead of focusing on louder voices or bigger motors, the company went in the opposite direction. Silence. Cinnamon 1 introduces what Donut Robotics calls Silent Gesture Control, a system that allows the humanoid to be guided using simple hand and finger movements rather than spoken commands. This approach feels especially well suited for real world environments where traditional voice control falls apart. Busy factory floors. Construction sites filled with constant noise. Even quiet indoor settings where voice commands feel awkward or intrusive. It also opens the door for far more accessible human robot interaction, particularly for users with impairments. While the current Cinnamon 1 hardware is built on an OEM platform, the intelligence driving it is where Donut Robotics is placing its long term bet. The team is actively developing custom Vision Language Action AI that allows the robot to interpret what it sees, understand intent, and respond with physical action. The goal is not just smarter robots, but robots that feel more natural. Even more ambitious is the company’s plan for full domestic production. Donut Robotics has stated its intention to localize both manufacturing and AI development in Japan, reinforcing the country’s reputation for precision engineering and thoughtful robotics design. If timelines hold, Cinnamon 1 units are expected to begin deployment in factories and construction environments by the end of 2026. That puts this humanoid squarely in the category of near term reality rather than distant concept. The takeaway is simple but important. As humanoid robots move out of labs and into daily work environments, the winners may not be the loudest or flashiest machines. They may be the ones that understand us without a word being spoken.

The AI Robot Guy on X

257,928 Aufrufe • vor 6 Monaten

✨ A dream I had finally came true: I can now chat directly with my sites to build any feature or fix any bug just via Telegram I've been playing with OpenClaw for 3 weeks now and it's great but I was always too scared to run it on any production server And I was right a bit as Marc Köhlbrugge was able to hack it by social engineering and acting as if it was me, and with enough tries it believed him, and was able to modify the server, change SSH keys etc. of course I had it isolated properly on its own VPS and it didn't touch anything sensitive (as it should!) Marc then reported that bug to Peter Steinberger 🦞 who patched it fast But I wanted to try something more basic and simple, and I think maybe more secure: to just connect Claude Code on my server to Telegram which would be hard locked to only messages from me So I installed claude-code-telegram by Richard Atkinson on the server and run it as a system daemon and it works really well The cool thing is that I was already using Telegram for server errors like this: > Photo AI - ❌ Random credits giveaway failed (Attempt 30/30) with an exception: SQLSTATE[HY000]: General error: 5 database is locked So now I can just reply, "Ok fix this", and Claude Code on the server in production will try (and probably succeed) in fixing it In the video below I asked it to make show [🌳 Parks ] on the map by default on load, it did that, then I reloaded the page and it instantly worked One thing it still needs is sending actual messages while it's doing stuff which OpenClaw does really well, it's annoying to just wait while it says "Working..." but that's probably next

✨ A dream I had finally came true: I can now chat directly with my sites to build any feature or fix any bug just via Telegram I've been playing with OpenClaw for 3 weeks now and it's great but I was always too scared to run it on any production server And I was right a bit as Marc Köhlbrugge was able to hack it by social engineering and acting as if it was me, and with enough tries it believed him, and was able to modify the server, change SSH keys etc. of course I had it isolated properly on its own VPS and it didn't touch anything sensitive (as it should!) Marc then reported that bug to Peter Steinberger 🦞 who patched it fast But I wanted to try something more basic and simple, and I think maybe more secure: to just connect Claude Code on my server to Telegram which would be hard locked to only messages from me So I installed claude-code-telegram by Richard Atkinson on the server and run it as a system daemon and it works really well The cool thing is that I was already using Telegram for server errors like this: > Photo AI - ❌ Random credits giveaway failed (Attempt 30/30) with an exception: SQLSTATE[HY000]: General error: 5 database is locked So now I can just reply, "Ok fix this", and Claude Code on the server in production will try (and probably succeed) in fixing it In the video below I asked it to make show [🌳 Parks ] on the map by default on load, it did that, then I reloaded the page and it instantly worked One thing it still needs is sending actual messages while it's doing stuff which OpenClaw does really well, it's annoying to just wait while it says "Working..." but that's probably next

@levelsio

642,596 Aufrufe • vor 6 Monaten

We’ve been saying that OctaRender is the rendering breakthrough every artist has been waiting for, but today, we MEASURE that. We don’t just build a feature-rich platform — we build something that performs. Our goal is for artists to get results as quickly as possible, without compromising on quality. We are continually improving, optimizing, and benchmarking against other platforms. Here's an example of a render comparison between The Render platform and OctaRender. You can check out the comparison in render speeds in the video. Render Info: - Scene: 100 frames at 1024 samples - Rendered on: $RENDER (left) using their Tier 2 (highest speed level) and on OctaRender (right) Notes: The render on OctaRender uses Cycles, while on Render, it uses Octane. Both engines are comparable in terms of rendering capabilities. OctaRender with Cycles maintains compositing nodes.

We’ve been saying that OctaRender is the rendering breakthrough every artist has been waiting for, but today, we MEASURE that. We don’t just build a feature-rich platform — we build something that performs. Our goal is for artists to get results as quickly as possible, without compromising on quality. We are continually improving, optimizing, and benchmarking against other platforms. Here's an example of a render comparison between The Render platform and OctaRender. You can check out the comparison in render speeds in the video. Render Info: - Scene: 100 frames at 1024 samples - Rendered on: $RENDER (left) using their Tier 2 (highest speed level) and on OctaRender (right) Notes: The render on OctaRender uses Cycles, while on Render, it uses Octane. Both engines are comparable in terms of rendering capabilities. OctaRender with Cycles maintains compositing nodes.

OctaSpace

16,475 Aufrufe • vor 1 Jahr