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This guy connected a computer vision model to dual robotic manipulators on his desk and the system now folds shirts in 47 seconds per garment without any human intervention after loading Automated laundry folding is one of those problems that sounds trivial until you realize fabric has no rigid...

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Blaze

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24,799 просмотров • 3 месяцев назад •via X (Twitter)

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Robotics is almost solved! Pun intended. Sunday published a "Solve" standard. They declared the scope (garment types, lighting, surfaces, starting configurations) and adaptation cost (zero—no fine-tuning per home) before evaluating. The same model weights run everywhere. This might be the differentiation between a good demo and reliable work of robots. They presented ACT-2, a robot foundation model that folds laundry at 99.1% success across unseen homes. Different houses, lighting, bed types, garment sizes. According to them scaling pre-training closes the generalization gap. Train on massive, high-quality human data. When you do that right, what you learn in-house actually transfers to unseen homes. Even wilder is that the model learns new folding techniques from a single example and generalizes them to new environments. Memo goes to homes later this year. Would you adopt this cutie? 🥹 Read more here: Great work Tony Zhao and team! 🧡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
1:21

Robotics is almost solved! Pun intended. Sunday published a "Solve" standard. They declared the scope (garment types, lighting, surfaces, starting configurations) and adaptation cost (zero—no fine-tuning per home) before evaluating. The same model weights run everywhere. This might be the differentiation between a good demo and reliable work of robots. They presented ACT-2, a robot foundation model that folds laundry at 99.1% success across unseen homes. Different houses, lighting, bed types, garment sizes. According to them scaling pre-training closes the generalization gap. Train on massive, high-quality human data. When you do that right, what you learn in-house actually transfers to unseen homes. Even wilder is that the model learns new folding techniques from a single example and generalizes them to new environments. Memo goes to homes later this year. Would you adopt this cutie? 🥹 Read more here: Great work Tony Zhao and team! 🧡 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

37,023 просмотров • 1 месяц назад

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.
5:58:26

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

230,988 просмотров • 4 месяцев назад

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:
0:29

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

42,864 просмотров • 6 месяцев назад

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”
1:03

🚨David Friedberg: AI is starting to identify and solve problems on its own “I'll give you a science corner example: there's this Evo 2 model that they publish at the Arc Institute, which Patrick Collison, you know, is the main funder and chairman.” “So that Evo 2 model, they just ingested all the DNA data they could find in the world.” “Trillions and trillions of base paired data that they ingested and then they looked at patterns in DNA. And that's it.” “They had no context for what the DNA represented, they had no context for the concept of genes, none of the structured understanding of what that DNA does, what it is, and you know what it did?” “They fed in the BRCA gene variant and the thing output a warning saying, ‘I think that this is a pathogenic variant to DNA,’ without having any context.” “This is the breast cancer allele.” “And it didn't have any knowledge and it wasn't trained on that at all.” “It had no knowledge that there are pathogenic variants for cancer, and it identified that this was a genetic variant that can cause some sort of pathogenic outcome in the organism.” “That's a great example where there's a lack of understanding at the human level on what really drives some of the patterns in nature, the patterns in society, the patterns in behavior that are kind of emergent phenomena perhaps, that these AI models are starting to identify.”

The All-In Podcast

79,718 просмотров • 1 год назад

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 просмотров • 3 месяцев назад

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:
0:54

A robotic ballet! 🩰 Coordinating multiple robot arms on a busy factory floor is notoriously complex. Each arm needs to move without colliding with its neighbors or the surrounding equipment, and today that planning is still mostly done by hand, a process that takes specialists hundreds of hours. Researchers at UCL, Google DeepMind, and Intrinsic have introduced RoboBallet, a new AI system that tackles this challenge head-on. Using a combination of graph neural networks and reinforcement learning, RoboBallet learns how to coordinate many robots in real time, generating smooth, collision-free motion plans in seconds instead of days. In tests, the system handled up to 40 tasks with eight arms working together, far beyond the limits of traditional planners. More importantly, it showed strong generalization: RoboBallet could adapt instantly to new factory layouts or recover if one robot failed, something previous methods struggled with. For manufacturers, this could mean faster deployment of automation, less downtime, and the ability to reconfigure production lines on the fly. Applications range from automotive welding to electronics assembly, and even large-scale construction. The current system focuses on reaching tasks, but the team plans to expand it to more complex operations like pick-and-place or painting. Here's the paper:

Lukas Ziegler

26,083 просмотров • 11 месяцев назад

LIVE TO FIGHT ANOTHER DAY The bully - that's the establishment, have... They know how to beat people up. They know how to gas them. And they have the arms and the equipment. And the mistake was made that the kids ended up playing their game of violence, you know, and they know how to be violent. They've been running it on violence for two thousand years or a million or whatever it is. And nobody can tell me that violence is the way. After all that time, you know... There must be another way. But a lot of people fell for it. And it's understandable in a way. 'Cause when the bully is actually right there, it's pretty hard to say turn the other cheek, baby. But I say turn the other cheek... and run. You know, and like the Indians are supposed to do, you know, which they lost, of course. but, er, was... Live to fight another day. #powertothepeople
0:48

LIVE TO FIGHT ANOTHER DAY The bully - that's the establishment, have... They know how to beat people up. They know how to gas them. And they have the arms and the equipment. And the mistake was made that the kids ended up playing their game of violence, you know, and they know how to be violent. They've been running it on violence for two thousand years or a million or whatever it is. And nobody can tell me that violence is the way. After all that time, you know... There must be another way. But a lot of people fell for it. And it's understandable in a way. 'Cause when the bully is actually right there, it's pretty hard to say turn the other cheek, baby. But I say turn the other cheek... and run. You know, and like the Indians are supposed to do, you know, which they lost, of course. but, er, was... Live to fight another day. #powertothepeople

Yoko Ono

19,873 просмотров • 10 месяцев назад

Milestone! We (robotic arms for gadgets assembly) finished the first commercial order, which brought the first revenue. Here are some learnings from this: The customer was a smart toy manufacturer. The task was to add a heatsink to Raspberry Pi. We received parts from them and returned the assembled modules back. Currently, it's done by teleoperation. Later it will be done by a remote employee via the Internet. Then it will be automated action by action, reducing the operator's time on this and making the task profitable. ps. If you have an assembly task that we can do for you asynchronically - leave a comment below. Learning 1. It's possible! This task which is usually done by the human arm with 5 fingers can be done with a two-finger gripper with the addition of a couple of simple tooling. The task was not simplified. We peeled off thin films from stickers, unpacked paper boxes, moved PCB boards full of components, etc. And no unsolvable problems have been encountered yet. Challenges: 1) The paper box shifted during the opening Solved with the plastic walls that you can lean against 2) Heat pad, stuck to the gripper instead of heat sync. Can be solved by gripper with a pump, but this time solved with the patience of the operator 3) The film on the pad is very thin. Turned out that sub-millimeter arm precision is enough to peel it off with just a regular gripper. 4) The working area has not enough space. You'll only know this by doing real tasks in bulk. This could be solved by an extra pair of long arms, but in this case, solved with the patience of the operator. I think that in the end, we will have 5-10 types of universal tooling and 5-10 types of grippers to solve almost all the problems in such assembly tasks. Learning 2. It's slow. It took 5 times more time, than doing it with human hands. But the good news is there's a lot of room for improvement. We now have specific “time for task” metrics, which we will decrease with iterations. The main reasons for slowness: 1) To rotate the gripper to a steep angle you are forced to control one robot arm with two hands instead of using both arms. We can fix this by just making more room for rotations. 2) Grabbing PCB board with two arms is hard. A slight difference in rotation can break the board, and it's hard to control these angles visually. To solve this, the best way is to use force feedback so you can feel the pressure applied to the item. 3) Accuracy and steadiness is still can be improved We will try a metal version and double the motors to do this. 4) It is physically difficult for the human hands to move with such precision To solve this, we will add a pad for the hands like in surgical robots Learning 3. It's a good business model The "Factory in the cloud" is a good business model for this stage. You send us parts and we send back assembled modules. Currently, it's more convenient than sending a robot to your place, as we can iterate/fix the robot quickly and utilize it 100% of the time. When we polish the set-up over time - we can send robots to your place. So if we can assemble something for you in the USA with Chinese prices by using modern automation - leave a comment below.
0:48

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

Igor Kulakov

37,266 просмотров • 1 год назад

This guy spent several days teaching a tabletop robot arm to roll a burrito and when one could not do it he did not rewrite the controller he printed 2 more and launched a 3-arm setup for about $1,000. He does not rewrite the software, does not wait for a smarter model, does not update the imitation weights, he just prints another arm and connects it to the existing leader-follower through LeRobot, nonstop. And it got more interesting: the hardware of one arm is a DIY kit SO-101 for about $300 to $400 on STS3215 bus servos, coordination between the arms goes through leader-follower teleoperation in Hugging Face LeRobot, and replication goes through a Bambu Lab A1 for $399 that prints a copy in a day. It knows the position of the tortilla by the leader-arm coordinates at the start, knows the handoff moment between the arms by the fold phase, and knows the final rolling from the sequence of demonstrations from teleop sessions. And it even distributes the work between the arms, one does the initial folds, the second holds the tortilla still, the third performs the final rolling, depending on which phase the burrito is currently in. In one 57-second demonstration 3 arms rolled a burrito for the first time without human involvement, and the total stack cost less than $1,000 in hardware versus a UR5 at $25,000 or an industrial burrito machine Solbern BR-1500 that costs about $50,000. The viewer is not watching 3 robots rolling a burrito. The viewer is watching permission to believe in a future where a kitchen task on $1,000 of hardware is done by the same loop as an industrial one at $50,000. Here is what happens when the bottleneck stops being the intelligence of the controller and the quality of the imitation model, and becomes the number of arms in the setup, and for a maker with a 3D printer the number of arms is limited only by print time. 3 arms do not get tired between sessions, do not require retraining when a new one is added, do not degrade from repetition, and every next burrito goes through the same teleop pipeline at the same quality as the first. Imagine that multi-arm DIY setups are no longer built for one kitchen task, but printed for each one, burritos, sushi, tacos, pizza dough, pour-over coffee. We just watched hobby robotics shift from "retrain the controller" to "print 2 more": when one robot can not handle it, you do not make it smarter, you print 2 more. The viewer thinks they are watching a DIY experiment. They are watching a multi-agent robotics stack that in one 57-second demonstration rolled a burrito for the first time without human hands, and whose filling partially falls out on the final rolling. What will improve the final rolling, a softer silicone gripper, a 4th follower arm in the setup, or a different filling composition and a more moist tortilla?
0:57

This guy spent several days teaching a tabletop robot arm to roll a burrito and when one could not do it he did not rewrite the controller he printed 2 more and launched a 3-arm setup for about $1,000. He does not rewrite the software, does not wait for a smarter model, does not update the imitation weights, he just prints another arm and connects it to the existing leader-follower through LeRobot, nonstop. And it got more interesting: the hardware of one arm is a DIY kit SO-101 for about $300 to $400 on STS3215 bus servos, coordination between the arms goes through leader-follower teleoperation in Hugging Face LeRobot, and replication goes through a Bambu Lab A1 for $399 that prints a copy in a day. It knows the position of the tortilla by the leader-arm coordinates at the start, knows the handoff moment between the arms by the fold phase, and knows the final rolling from the sequence of demonstrations from teleop sessions. And it even distributes the work between the arms, one does the initial folds, the second holds the tortilla still, the third performs the final rolling, depending on which phase the burrito is currently in. In one 57-second demonstration 3 arms rolled a burrito for the first time without human involvement, and the total stack cost less than $1,000 in hardware versus a UR5 at $25,000 or an industrial burrito machine Solbern BR-1500 that costs about $50,000. The viewer is not watching 3 robots rolling a burrito. The viewer is watching permission to believe in a future where a kitchen task on $1,000 of hardware is done by the same loop as an industrial one at $50,000. Here is what happens when the bottleneck stops being the intelligence of the controller and the quality of the imitation model, and becomes the number of arms in the setup, and for a maker with a 3D printer the number of arms is limited only by print time. 3 arms do not get tired between sessions, do not require retraining when a new one is added, do not degrade from repetition, and every next burrito goes through the same teleop pipeline at the same quality as the first. Imagine that multi-arm DIY setups are no longer built for one kitchen task, but printed for each one, burritos, sushi, tacos, pizza dough, pour-over coffee. We just watched hobby robotics shift from "retrain the controller" to "print 2 more": when one robot can not handle it, you do not make it smarter, you print 2 more. The viewer thinks they are watching a DIY experiment. They are watching a multi-agent robotics stack that in one 57-second demonstration rolled a burrito for the first time without human hands, and whose filling partially falls out on the final rolling. What will improve the final rolling, a softer silicone gripper, a 4th follower arm in the setup, or a different filling composition and a more moist tortilla?

Blaze

61,030 просмотров • 3 месяцев назад

Playing #VR games "without" a headset... ... Absolute genius or total abomination? This is called PortalVR and it lets you play any SteamVR game or app on your Windows PC without wearing a headset. Just plug in your #MetaQuest via USB, put it on your desk facing you, and use your controllers while facing your screen. The setup and connection is quite seamless and the team came up with a clever locomotion system to simulate turning the user's head by just tapping the top of the thumbstick. Now the biggest question is: does this make any sense? In my opinion, to play "standard" VR #games, it doesn't. You lose the beauty of being immersed and reaching out to grab objects or looking around can be tricky. On the other hand, I think there might be experiences specifically designed with this interaction in mind like interactive installations or educational experiences that could greatly benefit from a more tactile interface than a mouse and a keyboard
1:10

Playing #VR games "without" a headset... ... Absolute genius or total abomination? This is called PortalVR and it lets you play any SteamVR game or app on your Windows PC without wearing a headset. Just plug in your #MetaQuest via USB, put it on your desk facing you, and use your controllers while facing your screen. The setup and connection is quite seamless and the team came up with a clever locomotion system to simulate turning the user's head by just tapping the top of the thumbstick. Now the biggest question is: does this make any sense? In my opinion, to play "standard" VR #games, it doesn't. You lose the beauty of being immersed and reaching out to grab objects or looking around can be tricky. On the other hand, I think there might be experiences specifically designed with this interaction in mind like interactive installations or educational experiences that could greatly benefit from a more tactile interface than a mouse and a keyboard

Gabriele Romagnoli

66,345 просмотров • 8 месяцев назад

"There is a trickle of excitement from the Jets fan base and I am so f*cking sick of people who try to pour cold water on that the second it happens. If you're not a Jets fan and you are bothered that some Jets fans are excited they scored a TD with their starters on the opening drive of their second preseason game -- and you need to pour cold water on that with this fanbase that has stuck through this without the playoffs for almost two full decades -- what is wrong with you?!" Connor Rogers goes OFF on the #Jets haters!
1:02

"There is a trickle of excitement from the Jets fan base and I am so f*cking sick of people who try to pour cold water on that the second it happens. If you're not a Jets fan and you are bothered that some Jets fans are excited they scored a TD with their starters on the opening drive of their second preseason game -- and you need to pour cold water on that with this fanbase that has stuck through this without the playoffs for almost two full decades -- what is wrong with you?!" Connor Rogers goes OFF on the #Jets haters!

Badlands

81,144 просмотров • 2 дней назад

OK SO FINALLY I have a means to show you what I have been saying for a while.......Watch this clip, share it, so people understand what we are saying here!!! In this Clip, JLR© is having a discussion with Watching the World , and we get to see that front door lock!! Here is what I have been saying.....The Front door is a material called EXPANDED METAL, YOU CAN SEE THROUGH IT. The Door IS a Security Door, that you would have to cut through with two locks. A Lock that can be unlocked via a key on the handle, and a DEADbolt lock that would be locked on the inside of the door. This PREVENTS someone from being able to attack you when you OPEN the HOUSE door that is inside it, and behind this screen door. That door only opens when that deadbolt is unlocked from inside! ALso notice we see NO damage to the door or frame in this video!!
2:59

OK SO FINALLY I have a means to show you what I have been saying for a while.......Watch this clip, share it, so people understand what we are saying here!!! In this Clip, JLR© is having a discussion with Watching the World , and we get to see that front door lock!! Here is what I have been saying.....The Front door is a material called EXPANDED METAL, YOU CAN SEE THROUGH IT. The Door IS a Security Door, that you would have to cut through with two locks. A Lock that can be unlocked via a key on the handle, and a DEADbolt lock that would be locked on the inside of the door. This PREVENTS someone from being able to attack you when you OPEN the HOUSE door that is inside it, and behind this screen door. That door only opens when that deadbolt is unlocked from inside! ALso notice we see NO damage to the door or frame in this video!!

Capt KEVman!!!

46,092 просмотров • 6 месяцев назад

Qullamaggie on Only The Home Runs Will Make you Rich “Look at this APPS. If you bought this 5-star setup. Okay, this is what I'm talking about. Big move, okay. Pullback sideways. It surfed 10 there, then 20 there for a while. But then it kind of went sideways a little bit further. But then it started building higher lows. And a tight range and break out of this range, right? This is a five-star setup. And now, you should have sold a third or half on day 3 or 4 or 5, and you would still be in half or two thirds of your shares and trail this thing with the 10-day moving average. This is why you always trail some, because now and then you're gonna catch these stocks that double or triple in a short amount of time. Those are the ones that are gonna make you rich, okay? Those are the trades that are gonna make you rich. Everything else is just a grind. You gotta find those home runs. The home runs are gonna make you rich, okay? Most of the time, trading is just a grind. Like 70, 80% of the time. But now and then, the market gods bless you with a home run. All you have to do is identify the setups and follow the rules. That's it.”
1:28

Qullamaggie on Only The Home Runs Will Make you Rich “Look at this APPS. If you bought this 5-star setup. Okay, this is what I'm talking about. Big move, okay. Pullback sideways. It surfed 10 there, then 20 there for a while. But then it kind of went sideways a little bit further. But then it started building higher lows. And a tight range and break out of this range, right? This is a five-star setup. And now, you should have sold a third or half on day 3 or 4 or 5, and you would still be in half or two thirds of your shares and trail this thing with the 10-day moving average. This is why you always trail some, because now and then you're gonna catch these stocks that double or triple in a short amount of time. Those are the ones that are gonna make you rich, okay? Those are the trades that are gonna make you rich. Everything else is just a grind. You gotta find those home runs. The home runs are gonna make you rich, okay? Most of the time, trading is just a grind. Like 70, 80% of the time. But now and then, the market gods bless you with a home run. All you have to do is identify the setups and follow the rules. That's it.”

Lone

21,644 просмотров • 1 год назад

Elon Musk just told you the job is dying. Most people heard a prediction. A few heard a prison door opening. Musk: “In less than 20 years, working at all will be optional.” That is not a policy suggestion. That is a countdown. For three hundred years, the human blueprint has been identical. You are born. You move to the city. You rent a box near the office. You trade your body and your hours for the right to exist. You do this until you are old. Then you stop. Then you die. The entire model runs on one assumption. That human labor is the only engine. AI and robotics delete that assumption. When the machine handles production at a scale no human crew can match, the forced migration to the city evaporates. The commute evaporates. The cubicle evaporates. The alarm clock that owns your nervous system for forty years evaporates. Musk: “I think it won’t be the case that you have to be in a city for a job.” The city was never a choice. It was a requirement disguised as ambition. You moved to the noise and the concrete and the $4,000 rent because the paycheck lived there. Remove the paycheck from the equation and the geography changes overnight. You can live in the mountains. On the coast. In the silence of a town most people have never heard of. You can wake up to nothing but trees and cold air and the complete absence of anyone else’s schedule. That is not a fantasy. That is the math resolving. But here is where most people break. They hear “work is optional” and they see emptiness. A species with nothing to do. Billions of people staring at screens until their minds dissolve. That fear tells you everything about what the system has already done to us. We confused labor with purpose. The grind with meaning. The paycheck with proof that we matter. Musk: “In the same way that you could grow your own vegetables in your garden.” The analogy is precise. You do not grow tomatoes because the economy demands it. You grow them because something in you wants to build a thing with your hands and watch it come alive. That instinct does not disappear when the job does. It gets unleashed. The artist who spent twenty years doing accounting finally paints. The engineer who always wanted to build something of her own finally builds it. The kid in a small town who could never afford to take the risk finally takes it. Work does not vanish. Forced work vanishes. What replaces it is creation without a gun to your head. This is the part that keeps me up at night. We are standing at the edge of the largest liberation in human history. And the loudest voices in the room are begging to stay in the cell. They want the commute. They want the boss. They want the structure that tells them when to eat and when to sleep and when they are allowed to think about their own life. Because freedom without a template is terrifying. The next twenty years will not test our technology. The technology is already ahead of schedule. They will test whether the species can handle what it has been asking for since the beginning of civilization. Time. Space. Silence. And the unbearable weight of choosing what your life actually means when no one is forcing the answer. That is not a prediction. That is the final exam. And nobody is ready.
1:46

Elon Musk just told you the job is dying. Most people heard a prediction. A few heard a prison door opening. Musk: “In less than 20 years, working at all will be optional.” That is not a policy suggestion. That is a countdown. For three hundred years, the human blueprint has been identical. You are born. You move to the city. You rent a box near the office. You trade your body and your hours for the right to exist. You do this until you are old. Then you stop. Then you die. The entire model runs on one assumption. That human labor is the only engine. AI and robotics delete that assumption. When the machine handles production at a scale no human crew can match, the forced migration to the city evaporates. The commute evaporates. The cubicle evaporates. The alarm clock that owns your nervous system for forty years evaporates. Musk: “I think it won’t be the case that you have to be in a city for a job.” The city was never a choice. It was a requirement disguised as ambition. You moved to the noise and the concrete and the $4,000 rent because the paycheck lived there. Remove the paycheck from the equation and the geography changes overnight. You can live in the mountains. On the coast. In the silence of a town most people have never heard of. You can wake up to nothing but trees and cold air and the complete absence of anyone else’s schedule. That is not a fantasy. That is the math resolving. But here is where most people break. They hear “work is optional” and they see emptiness. A species with nothing to do. Billions of people staring at screens until their minds dissolve. That fear tells you everything about what the system has already done to us. We confused labor with purpose. The grind with meaning. The paycheck with proof that we matter. Musk: “In the same way that you could grow your own vegetables in your garden.” The analogy is precise. You do not grow tomatoes because the economy demands it. You grow them because something in you wants to build a thing with your hands and watch it come alive. That instinct does not disappear when the job does. It gets unleashed. The artist who spent twenty years doing accounting finally paints. The engineer who always wanted to build something of her own finally builds it. The kid in a small town who could never afford to take the risk finally takes it. Work does not vanish. Forced work vanishes. What replaces it is creation without a gun to your head. This is the part that keeps me up at night. We are standing at the edge of the largest liberation in human history. And the loudest voices in the room are begging to stay in the cell. They want the commute. They want the boss. They want the structure that tells them when to eat and when to sleep and when they are allowed to think about their own life. Because freedom without a template is terrifying. The next twenty years will not test our technology. The technology is already ahead of schedule. They will test whether the species can handle what it has been asking for since the beginning of civilization. Time. Space. Silence. And the unbearable weight of choosing what your life actually means when no one is forcing the answer. That is not a prediction. That is the final exam. And nobody is ready.

Dustin

111,803 просмотров • 4 месяцев назад

A new humanoid robot Spirit AI from China releases a new generation of wheeled humanoid robot Moz1 It is the first embodied intelligent robot with high-precision force control of the whole body, with 26 degrees of freedom, built based on the world's highest power density integrated force control joint. It takes speed, precision and safety to a higher level. It is worth mentioning that Moz1 is driven by SpiritAI's latest OneTwoVLA. It only needs one model to think and operate at the same time, and correct errors autonomously, just like humans, without separating the two systems of "thinking planning" and "coordinated execution". One system is seamlessly connected, which is conducive to dealing with complex, personalized, and immediate tasks, especially in home and care applications.
1:00

A new humanoid robot Spirit AI from China releases a new generation of wheeled humanoid robot Moz1 It is the first embodied intelligent robot with high-precision force control of the whole body, with 26 degrees of freedom, built based on the world's highest power density integrated force control joint. It takes speed, precision and safety to a higher level. It is worth mentioning that Moz1 is driven by SpiritAI's latest OneTwoVLA. It only needs one model to think and operate at the same time, and correct errors autonomously, just like humans, without separating the two systems of "thinking planning" and "coordinated execution". One system is seamlessly connected, which is conducive to dealing with complex, personalized, and immediate tasks, especially in home and care applications.

CyberRobo

26,755 просмотров • 1 год назад

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”
1:43

Chamath: Anthropic's Mythos Warning Is Theater @jason: “Chamath, is it the Boy who Cried Wolf, or is this the real deal now?” Chamath Palihapitiya: “I think it's mostly theater. In February of 2019 when Dario was still at OpenAI, they did the same thing with GPT-2. That was a 1.5 billion parameter model, which sounds like a total fart in the wind in 2026. But at that time, this model was supposed to be the end of days. And at the end of it, it was a huge nothingburger. If you actually think that Mythos is capable of doing what it says it can do, two things are true. One is, a very sophisticated hacker can probably do those things right now with Opus. And two, if these exploits are this easy to find, whether you use Opus or whether you use Mythos, the reality is you'd have to shut down the internet for about five years to patch them all. So when you see a large multi-trillion dollar GSIB bank, it's a bit of theater. Why? What do you think they can actually accomplish in two months? Do you actually think that if there's these vulnerabilities, it's all going to get fixed? Let's give them six months, let's give them nine months. So I do think that Sacks is right, that they have figured out a very clever go-to-market muscle here that activates hyper attention and hyper usage, and so I give them tremendous credit. But we've seen it before, we saw it when these folks were the principal architects at OpenAI, and we're now seeing the same playbook here. The reality is that capitalism moves forward, the funding needs moves forward, and the need for these guys to build adoption moves forward. And that's going to supersede what this is.”

The All-In Podcast

220,049 просмотров • 4 месяцев назад