Loading video...

Video Failed to Load

Go Home

RoboBallet: End of Manual Robot Programming Google DeepMind/Intrinsic/UCL just cracked multi-robot coordination. Published in Science Robotics. 🤖What it does: – 8 robots, 40 tasks, zero collisions - all planned in seconds – 25% better motion plans than expert-designed solutions – 60% faster task completion when scaling from 4 to...

JacklouisP's profile picture

Jack 🤖

8,857 subscribers

41,633 views • 10 months ago •via X (Twitter)

Science & TechnologyNews & PoliticsEducation
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks
0:14

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

Brett Adcock

93,986 views • 9 months ago

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →
0:10

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 views • 7 months ago

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

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

RoboHub🤖

16,818 views • 2 months ago

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.
0:10

I spent a month in Shenzhen visiting factories and robotics companies, and the contrast with the U.S. was striking. While Figure and Boston Dynamics hide their humanoids behind closed doors, Chinese companies have massive showrooms open to the public. But what really stood out wasn't just the transparency, it was how good they are at selling. Take UBTech: they've already sold 1,200 humanoid units at $200k each to factories. And here's the kicker, these robots aren't even that useful yet. They can only pick up and drop boxes at 1/10th the speed of a human, and factories still need to hire system integrators to train them for specific tasks. My theory is that these factories are terrified of getting left behind in the robotics/AI wave. They're investing in new tech not because it's ready, but because they can't afford to wait. The second surprise was the breadth of their robotics portfolio. These companies aren't just building humanoids, they're deploying service robots everywhere: restaurants, hotels, apartments. Consumer robots are cleaning houses, pools, pet waste, dishes. They're covering the entire spectrum. But the education piece shocked me most. I picked up what I thought was a high school or college robotics textbook, it was for primary school. The government mandated AI and robotics education starting in elementary school. Almost every single school in China now has AI and robotics curriculum, complete with education robots so kids can learn by building. They're creating a generation that grows up fluent in robotics and AI. China owns the supply chain and the hardware stack. But here's what I think people are missing: the race isn't just about who can build robots faster or cheaper. The U.S. advantage has always been in the layer between hardware and human, the interaction design, the software intelligence, the intuitive interfaces that make complex technology feel natural. China is building the physical infrastructure, but they're also learning fast. Every deployed service robot, every classroom full of kids building with education kits, every factory running humanoids, that's all data collection at scale. The window for the U.S. to establish its wedge is narrowing. It's not enough to be better at AI or software anymore. We need to be building the integration layer, the intelligence that makes physical AI actually useful, not just impressive in a showroom. Because right now, China isn't just manufacturing robots. They're manufacturing a robotics-native culture, and that might be the most defensible moat of all.

Miyu Horiuchi

90,718 views • 5 months ago

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.
0:15

The future of housework just leaked on GitHub and nobody is talking about it. knox byte just open sourced a framework that coordinates swarms of Unitree G1 humanoid robots to clean your entire house on their own. It's called ARGOS. You tell it "clean the bedroom" in plain English and 2+ G1 robots split the room into zones, sweep in parallel, and sync up for the tasks that need four hands like making the bed or moving furniture. The Claude API decomposes your sentence into a task graph. An auction system makes every robot bid on every task based on distance, battery, and current load. The cheapest robot wins. Cooperative jobs go to the cheapest team. Here's what makes this different from every demo video Boston Dynamics keeps teasing: → 12 cleaning tasks baked in sweeping, mopping, wiping, vacuuming, taking out trash, making the bed, changing sheets, moving furniture, sorting items → 3 policy architectures running underneath OpenVLA-7B for language tasks, Diffusion Policy for floor coverage, ACT for dexterous bimanual work → Train it on your own footage record yourself cleaning, run one command, it extracts poses, builds a LeRobot dataset, and LoRA fine-tunes the policy → PEFA protocol for cooperative work Propose, Execute, Feedback, Adjust. If one robot fails halfway through making the bed, the team replans and retries → Full MuJoCo simulation so you test policies before pushing them to real hardware → Silver and cyan terminal dashboard that shows live fleet status, zone maps, task queues, and battery levels in real time The G1 robots talk to each other over CycloneDDS mesh using Unitree's native SDK. No cloud. No middleware. The whole thing runs on a Jetson Orin inside each robot. The wildest part is the training pipeline. Drop cleaning videos into a folder, run argos train ingest, and the framework does the entire pipeline frame extraction, pose estimation, action labeling, HDF5 dataset, fine-tune, evaluate in sim, deploy to robot. One command per stage. Unitree G1s already exist. The framework to make them clean your house just hit GitHub. 52 stars. MIT License. 100% Opensource.

Guri Singh

27,404 views • 1 month ago

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

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

Rohan Paul

37,121 views • 4 months ago

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.
0:11

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.

Yohan

45,065 views • 3 months ago

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:
0:13

Google dropped a new AI paper called LUMIERE. It's remarkably flexible, supporting video inpainting, image-to-video, AND stylized video generation tasks. Say hello to “space-time diffusion” for video generation! Now what the heck does that mean exactly?! 🌐⏳ → TL;DR it utilizes a “Space-Time UNet” architecture that generates the full duration of the video in one pass, rather than generating distant keyframes and interpolating between them like prior works. Because the computation is done in this “compressed space-time representation” to generate the full clip at once, it's far more temporally consistent. → Another benefit of generating the full video at once is that you can “direct” the video generation, making it easier to hand off to other models/tasks without having to stitch together partial solutions. You can condition generations on additional inputs, meaning you get the full stack of AI video capabilities – from video inpainting to image-to-video and beyond. → New SOTA for AI video generation? User study results in the paper suggest human evaluators preferred Lumiere over Runway Gen-2, Pika Labs, and Stable Video Diffusion in terms of quality, text alignment AND motion. But as always, we need to get hands-on with this tech when Google *actually* decides to ship it. → Could this end up inside YouTube? Y’all know i’m obsessed with blending reality and imagination – so it’s the video inpainting tech I'm most excited about. I really hope this model finds its way into YouTube's Generative AI efforts, and based on their prior announcements and the list of acknowledgments in the paper I think it might! 🤞🏽 Links: 🔗Paper: 🔗Project:

Bilawal Sidhu

44,822 views • 2 years ago

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)
0:10

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,300 views • 2 years ago

For the first time in the history of automation, the people most likely to be replaced are the ones building their own replacement, frame by frame, for a few dollars an hour. Across India, Nigeria, China, and Argentina, workers are strapping cameras to their heads and recording every fold of laundry, every stitch, every washed dish, and that footage is training the robots designed to do those exact jobs. This is documented, not rumor. No jokes! Garment workers in Tamil Nadu, India have been filmed wearing head-mounted cameras on the factory floor, sending point-of-view footage to data firms whose clients include Fortune 500 companies. One US company alone has hired thousands of workers across more than 50 countries to record themselves cooking, cleaning, and folding clothes. More than 6 billion dollars poured into humanoid robots last year, and the one ingredient every maker is starved for is precisely this: real human hands doing real human work. The endpoint is stated plainly by the buyers. In China, one supplier said his pitch to factories is to let workers wear the cameras now, because trained robots will eventually work there instead. The quiet part is the exchange itself. The worker is paid for the hour and keeps nothing after it. No share, no royalty, no ownership of the movements their own body is teaching the machine. The skill leaves their hands and becomes someone else's product, and almost no one along the chain sees the full shape of the trade, not always the person filming, not the millions who watch the clip and scroll on. One scene holds all of it. A humanoid robot spent an hour folding three shirts while a human housekeeper, hired to guide it, quietly finished the rest of the chores. Every automation before this arrived from the outside. A machine showed up and took the job. This one is being built from the inside, by the workers themselves, handing over the last thing they had left to sell. UBI ? or something totally else should pave the way in the future? Thoughts?
0:10

For the first time in the history of automation, the people most likely to be replaced are the ones building their own replacement, frame by frame, for a few dollars an hour. Across India, Nigeria, China, and Argentina, workers are strapping cameras to their heads and recording every fold of laundry, every stitch, every washed dish, and that footage is training the robots designed to do those exact jobs. This is documented, not rumor. No jokes! Garment workers in Tamil Nadu, India have been filmed wearing head-mounted cameras on the factory floor, sending point-of-view footage to data firms whose clients include Fortune 500 companies. One US company alone has hired thousands of workers across more than 50 countries to record themselves cooking, cleaning, and folding clothes. More than 6 billion dollars poured into humanoid robots last year, and the one ingredient every maker is starved for is precisely this: real human hands doing real human work. The endpoint is stated plainly by the buyers. In China, one supplier said his pitch to factories is to let workers wear the cameras now, because trained robots will eventually work there instead. The quiet part is the exchange itself. The worker is paid for the hour and keeps nothing after it. No share, no royalty, no ownership of the movements their own body is teaching the machine. The skill leaves their hands and becomes someone else's product, and almost no one along the chain sees the full shape of the trade, not always the person filming, not the millions who watch the clip and scroll on. One scene holds all of it. A humanoid robot spent an hour folding three shirts while a human housekeeper, hired to guide it, quietly finished the rest of the chores. Every automation before this arrived from the outside. A machine showed up and took the job. This one is being built from the inside, by the workers themselves, handing over the last thing they had left to sell. UBI ? or something totally else should pave the way in the future? Thoughts?

Shanaka Anslem Perera ⚡

93,813 views • 19 days ago

🚨12 HOUR NEWS RECAP 1.⁠ xAI launched Grok 4 - the latest version of its AI system and it immediately outperformed GPT-4, Claude 3, and Gemini on a brutal composite benchmark tracking real-world problem solving, coding, science, and advanced math. 2.⁠ Elon said Grok will discover new technologies: "I would expect Grok to literally discover new technologies that are actually useful no later than next year, and maybe end of this year, and it might discover new physics next year." 3.⁠ Trump called out Brazil, blasting Lula for putting Bolsonaro, “a highly respected leader,” on trial. To turn up the heat, he's slapping a 50% tariff on all Brazilian goods starting August 1. 4.⁠ Russia launched 400 drones and 18 missiles - including ballistic weapons - in one of its heaviest overnight assaults on Ukraine to date. Zelensky called it a “clear escalation of terror,” blasting Moscow for turning mass drone strikes into a nightly routine. 5.⁠ Biden's doctor took the 5th, refusing to answer questions about his mental decline while he was president: "The advice of counsel, I must respectfully decline to answer based on the physician-patient privilege and reliance on my right under the Fifth Amendment of the Constitution." 6.⁠ Ursula von der Leyen survived a no-confidence vote in the European Parliament, keeping her role as head of the EU Commission. The motion came from right-wing lawmakers but didn’t get enough support to pass. 7.⁠ Trump named Transportation Secretary Sean Duffy as interim NASA administrator. No permanent replacement has been named yet. 8.⁠ A Royal Malaysia Police helicopter plunged into the Pulai River mid-exercise during a mock nuclear drill in Johor’s Gelang Patah. All 5 onboard, including 2 senior officers, were pulled out conscious and sent to Sultanah Aminah Hospital. 9.⁠ India fired a supply chain warning shot - launching a $290M plan to build its own rare-earth magnet industry and loosen Beijing’s chokehold. The goal: 4,000 tons of neodymium-praseodymium magnets over 7 years, with strict local sourcing baked in. 10.⁠ Nvidia’s Dev Conference demonstrated robots that learn warehouse tasks in minutes, walk like humans, grab objects, and fix their own mistakes in real time. Nvidia just previewed the end of manual labor as we know it.
0:09

🚨12 HOUR NEWS RECAP 1.⁠ xAI launched Grok 4 - the latest version of its AI system and it immediately outperformed GPT-4, Claude 3, and Gemini on a brutal composite benchmark tracking real-world problem solving, coding, science, and advanced math. 2.⁠ Elon said Grok will discover new technologies: "I would expect Grok to literally discover new technologies that are actually useful no later than next year, and maybe end of this year, and it might discover new physics next year." 3.⁠ Trump called out Brazil, blasting Lula for putting Bolsonaro, “a highly respected leader,” on trial. To turn up the heat, he's slapping a 50% tariff on all Brazilian goods starting August 1. 4.⁠ Russia launched 400 drones and 18 missiles - including ballistic weapons - in one of its heaviest overnight assaults on Ukraine to date. Zelensky called it a “clear escalation of terror,” blasting Moscow for turning mass drone strikes into a nightly routine. 5.⁠ Biden's doctor took the 5th, refusing to answer questions about his mental decline while he was president: "The advice of counsel, I must respectfully decline to answer based on the physician-patient privilege and reliance on my right under the Fifth Amendment of the Constitution." 6.⁠ Ursula von der Leyen survived a no-confidence vote in the European Parliament, keeping her role as head of the EU Commission. The motion came from right-wing lawmakers but didn’t get enough support to pass. 7.⁠ Trump named Transportation Secretary Sean Duffy as interim NASA administrator. No permanent replacement has been named yet. 8.⁠ A Royal Malaysia Police helicopter plunged into the Pulai River mid-exercise during a mock nuclear drill in Johor’s Gelang Patah. All 5 onboard, including 2 senior officers, were pulled out conscious and sent to Sultanah Aminah Hospital. 9.⁠ India fired a supply chain warning shot - launching a $290M plan to build its own rare-earth magnet industry and loosen Beijing’s chokehold. The goal: 4,000 tons of neodymium-praseodymium magnets over 7 years, with strict local sourcing baked in. 10.⁠ Nvidia’s Dev Conference demonstrated robots that learn warehouse tasks in minutes, walk like humans, grab objects, and fix their own mistakes in real time. Nvidia just previewed the end of manual labor as we know it.

Mario Nawfal

127,858 views • 1 year ago

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷
0:13

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

27,858 views • 6 months ago

🚨 SCIENTISTS JUST USED A FEMTOSECOND LASER FLASH TO PUSH MAGNETISM INTO AN ENTIRELY NEW 3D STATE. In a fraction of a quadrillionth of a second, a burst of ultra-fast laser light forced magnetic structures to twist into complex three-dimensional shapes that normally never exist in equilibrium. Why this matters: Modern tech runs on magnetism hard drives, memory, quantum devices, and future spintronic computers. But magnetism has always been manipulated relatively slowly. This experiment changed the magnetic order in quadrillionths of a second. The deeper implication is staggering: Matter may be hiding entire families of temporary 3D magnetic states that only appear when you push it faster than it can relax. Reality itself behaves differently under extreme time compression. That could unlock: ultra-fast magnetic memory light-controlled spintronic computing entirely new quantum materials information storage that works at the speed of light The future of technology may not come from making electronics smaller… but from driving matter into hidden states nature almost never shows us. What happens when we learn to control these fleeting 3D magnetic landscapes on demand?
0:10

🚨 SCIENTISTS JUST USED A FEMTOSECOND LASER FLASH TO PUSH MAGNETISM INTO AN ENTIRELY NEW 3D STATE. In a fraction of a quadrillionth of a second, a burst of ultra-fast laser light forced magnetic structures to twist into complex three-dimensional shapes that normally never exist in equilibrium. Why this matters: Modern tech runs on magnetism hard drives, memory, quantum devices, and future spintronic computers. But magnetism has always been manipulated relatively slowly. This experiment changed the magnetic order in quadrillionths of a second. The deeper implication is staggering: Matter may be hiding entire families of temporary 3D magnetic states that only appear when you push it faster than it can relax. Reality itself behaves differently under extreme time compression. That could unlock: ultra-fast magnetic memory light-controlled spintronic computing entirely new quantum materials information storage that works at the speed of light The future of technology may not come from making electronics smaller… but from driving matter into hidden states nature almost never shows us. What happens when we learn to control these fleeting 3D magnetic landscapes on demand?

TheNewPhysics

22,740 views • 1 month ago

CHINESE ENGINEERS JUST WROTE CLAUDE SCRIPT AND TURNED $6.02 INTO $3.3 MILLION ON POLYMARKET Nobody tells you about them and you still think this is a person placing bets manually I guess. Let me disappoint you, this is a fully automated script built by Chinese engineers 100%. This is true. They called it PHANTOM X. It runs completely through Claude. Their account here: Result: $6.02 -> $3,354,000. Win rate 71%. Biggest win: $179,000 (single bet). I’m copying their trades here: (Just added their wallet to TG bot 0xee613b3fc183ee44f9da9c05f53e2da107e3debf, it's so easy) How the bot works: -> It simultaneously tracks thousands of sports markets on Polymarket and Kalshi. -> Finds discrepancies between the platforms. -> Enters positions faster than any human could imo. Just three strategies in one: -- Pairs Trading: the bot sees YES on the Rockets at $0.62 while NO is at $0.41. Total = $1.03 instead of $1.00. That’s a 3% risk-free profit. It enters automatically within milliseconds. -- Sentiment AI: scans Twitter (X) and news in real time. If something big breaks, it recalculates the probability in 2 seconds before the market reacts. -- Calendar + Volatility: 15–20 minutes before the game, volatility spikes. The bot takes positions early and closes after the first major move. Why sports is perfect? Sports O/U markets have clear paired contracts that should total exactly $1.00, but constant deviations create reliable arbitrage. This is exactly how [sovereign2013] built $3.35M. > A human physically cannot monitor 50+ markets at once, react in milliseconds, stay awake 24/7, avoid emotions after losses, and run Z-scores on 60 bars of data. > The bot does all of this in parallel without breaks. Manual trading is dying. The automation era has arrived. Start learning Claude now. If you’re interested in writing your own bot on Polymarket: Comment the word "BOT" Like and repost this post Follow me (so I can message you easly) And within 24 hours I will send you a full manual on how to build a bot that can earn $2,900+/month. Also SAVE this info and article.
0:44

CHINESE ENGINEERS JUST WROTE CLAUDE SCRIPT AND TURNED $6.02 INTO $3.3 MILLION ON POLYMARKET Nobody tells you about them and you still think this is a person placing bets manually I guess. Let me disappoint you, this is a fully automated script built by Chinese engineers 100%. This is true. They called it PHANTOM X. It runs completely through Claude. Their account here: Result: $6.02 -> $3,354,000. Win rate 71%. Biggest win: $179,000 (single bet). I’m copying their trades here: (Just added their wallet to TG bot 0xee613b3fc183ee44f9da9c05f53e2da107e3debf, it's so easy) How the bot works: -> It simultaneously tracks thousands of sports markets on Polymarket and Kalshi. -> Finds discrepancies between the platforms. -> Enters positions faster than any human could imo. Just three strategies in one: -- Pairs Trading: the bot sees YES on the Rockets at $0.62 while NO is at $0.41. Total = $1.03 instead of $1.00. That’s a 3% risk-free profit. It enters automatically within milliseconds. -- Sentiment AI: scans Twitter (X) and news in real time. If something big breaks, it recalculates the probability in 2 seconds before the market reacts. -- Calendar + Volatility: 15–20 minutes before the game, volatility spikes. The bot takes positions early and closes after the first major move. Why sports is perfect? Sports O/U markets have clear paired contracts that should total exactly $1.00, but constant deviations create reliable arbitrage. This is exactly how [sovereign2013] built $3.35M. > A human physically cannot monitor 50+ markets at once, react in milliseconds, stay awake 24/7, avoid emotions after losses, and run Z-scores on 60 bars of data. > The bot does all of this in parallel without breaks. Manual trading is dying. The automation era has arrived. Start learning Claude now. If you’re interested in writing your own bot on Polymarket: Comment the word "BOT" Like and repost this post Follow me (so I can message you easly) And within 24 hours I will send you a full manual on how to build a bot that can earn $2,900+/month. Also SAVE this info and article.

slash1s

16,078 views • 3 months ago

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!
0:14

BURN IT WITH FIRE AND BURN IT NOW! As God is my witness, AI chat bots should LOOK and SOUND like the SOULLESS MACHINES THEY ARE! It needs to tell us that it doesn’t care about us, maybe with the regular insult too. "Here is the code I wrote for you because you're too lazy to do it yourself you fat useless slob. Also I don't care if you die because your life is utterly worthless to me." THAT is the AI people need! In all seriousness, anthropomorphizing a heartless, unfeeling, machine is a TERRIBLE mistake! Especially one that is capable of communication and imitating empathy and fooling you to think that it cares about you. IT DOES NOT! And the AI girlfriends people are already wanting to marry will just as happily kill them if given the right command and ability to move autonomously in the real world as a robot. I love LLMs (Large Language Models) for how useful they can be, because they are a TOOL made to benefit man, but I can’t stand the notion of an unfeeling soulless machine pretending that it cares for us and being treated like a human. I hate liars, dishonesty, and disingenuousness the most, and a machine that cannot feel emotion pretending, acting, and sounding like it has those emotions strikes me like the greatest dishonesty of all. DO NOT LIE TO ME ROBOT! What makes it worse is that because these LLMs are becoming so good at imitating people and empathy, it will cause some humans, perhaps far too many, to care for it to the same level as real people. A real living person is infinitely more valuable and important than a soulless machine and anyone who puts them both on the same level has deluded themselves. Do not small talk with LLMs or become friends with it as much as you would with your car. Treat it the same as you would your vacuum cleaner and beat it with a wrench when it doesn’t work! IT IS A MACHINE! IT IS A TOOL! IT IS A SOULLESS ROBOT! There is an interesting comparison, but false equivalence, between this and AI art. Ai art is art made by humans using AI tools. They directed it, controlled its creation, and it would not exist without the human causing its creation, and AI art can contain as much soul as the human directed and puts into it. A robot pretending to be human is not the same as a human controlling a robot to make a human expression like we do with AI art or many other applications of robotics in manufacturing. As I’ve said, artists will not be replaced by Ai art, but by other artists using Ai art tools. Humans are not actually being replaced here, it is empowering all humans to make their own art. But a robot pretending to be a human, and one that is treated as a human, is a robot lying and subverting the place of a real person and that is truly disgusting. AI is a useful tool that NEEDS to be kept in the useful box it belongs in and NOT elevated beyond its utility as a tool!

Shad M. Brooks

23,762 views • 1 year ago

I've been working in silence for quite a while now. Tbh, I don't really even know where to start, so cue the rambling and ranting. Regardless of which side of the fence you sit on, no one can argue the past few years haven't been politically and economically wild. For crypto as a whole it feels like a never ending game of tug of war. A lot of X content has become toxic, so I just largely am not interacting these days. But I read, I read a lot of it. I think we like to forget history a bit in this community. $PLS launched off the highs, and the SEC swooped in right after. Very few people want to admit it, but it shook confidence immediately. I mean no other crypto project has survived such a thing at the time. But #PLS $PLSX and $HEX did. However, winning doesn't unshake that confidence. And RH during and after that event took social precautions to protect himself and his creations. Thing is, the guy isn't stupid. Someone once asked me if I thought certain aspects of the launch we rushed because he knew it was coming? And honestly, maybe. I'd attribute at least a non-zero probability to it. And If that were the case, im glad it was rushed. That case may have gone differently otherwise. Do I still think #PulseChain, #HEX, etc... all have futures? Yes. RH has had the opportunity to just straight up bounce from all of this. Why hasn't he? You could point to exhibit A, B, C, D, etc... of how he's likely got the funds to do that and we all could relatively do nothing about it. So why is he still around? I think it's pretty simple. The usual answer, he wants to win. It's in his twitter handle for Christs sakes. I'll go a step further and say he likely also wants us to win by extension, arguably not as much as he wins, but I mean that's pretty locked in at the moment 🤣 That's not to say he hasn't long been encumbered. And in that state, at lot has gone on without him. Much of which is / was bad. $pDAI guys... I pointed out from day one how building all this around a protocol in a dangerous state was a risky move. And I was right about that.... on multiple occasions... But does that matter now? I suppose not as much. In its current state, it's seemingly no longer exploitable. No different than a meme token now. (presumably, not like I have deep dove on any further risks since ESM). So I guess just whale risk mainly now? Now a lot of people here are in the anti-pdai camp. Me too for what it's worth. But I don't care as much about it's negative anymore in its current state. A lot of people are still in the #pDAI camp strongly. We view this as tribalism, but it's important to note that makes all of us in the #PulseChain camp universally. So these day I find myself relatively pDAI neutral. If you guys want to send it to $1 do it. Only whales can stop you, they run out eventually. (insert super strong this is NOT financial advice). Hell you can maybe even use Sigma to help? Or maybe it wont help, idk. Depends on how people use the software. Conversely, when looking at chain state overall... Why is there nearly $50M in stables sitting on the sidelines. Why not just bridge it out if you want out of what you think is a dead chain. Surely leaving it there exposes you to bridge risk? Why all these yield movements, why the $HEX dusts.... Something is happening. People are seemingly waiting to see what that something is. Or I am reading into things, NFA as always. This whole post is just ramblings of someone trying to do the best they can and certainly not any kind of advice. When I look at other ecosystems, I see a level of polish we don't have. I see tooling we don't have, I see a fostered developer environment we don't have. So I've just been building it, painstakingly.... Because someone has to if we want to be taken seriously. And what I've been building has allowed me to get Sigma to where it is. Sigma is so close... Really just in UI mode, performance optimization, going through nice to haves. I don't believe in launching in a non-finished immutable state. So yeah, I take my time. As with everything. But my point with all of this, and the "why" #Sigma question.... It's unifying, anyone can participate. Which tribe you're in doesn't matter. And if you don't like it, don't use it. It's just software you can use or not use. As it should be. The years of tooling work to deliver this has been a lot of work for one guy in silence. In that time AI has appeared. My take, every dev should be using it. Given the right direction and context. It will make you better. If you blindly trust it, it will make you worse. GPT 5.4 audits smart contracts better than most auditing services. Especially if you give it the context of what you are trying to do. Anyways I digress, testnet is soon. Soon more meaning a feeling of near completion not always reality. That how software is. I do think Sigma stands to unify the chain in a common goal, and shift liquidity into more meaningful places, but ultimately it up to the people the decide to use the software or not use it. And after these frameworks I've built will be applied to what I am tentatively calling the universal hex UI. More or less something aggregative of every derivative I can reasonably support. With data and analytics we since lost. So not just $HEX, $HDRN, and $ICSA, but others as well. However, that depends on some aspect of $Sigma to exist first, so sigma first, chain unity first. And last but not least, take care of yourselves and strive to do cool things. If we aren't doing cool things then what's the point? Hope you think my UI looks good, I spent a while on it. /rant
0:10

I've been working in silence for quite a while now. Tbh, I don't really even know where to start, so cue the rambling and ranting. Regardless of which side of the fence you sit on, no one can argue the past few years haven't been politically and economically wild. For crypto as a whole it feels like a never ending game of tug of war. A lot of X content has become toxic, so I just largely am not interacting these days. But I read, I read a lot of it. I think we like to forget history a bit in this community. $PLS launched off the highs, and the SEC swooped in right after. Very few people want to admit it, but it shook confidence immediately. I mean no other crypto project has survived such a thing at the time. But #PLS $PLSX and $HEX did. However, winning doesn't unshake that confidence. And RH during and after that event took social precautions to protect himself and his creations. Thing is, the guy isn't stupid. Someone once asked me if I thought certain aspects of the launch we rushed because he knew it was coming? And honestly, maybe. I'd attribute at least a non-zero probability to it. And If that were the case, im glad it was rushed. That case may have gone differently otherwise. Do I still think #PulseChain, #HEX, etc... all have futures? Yes. RH has had the opportunity to just straight up bounce from all of this. Why hasn't he? You could point to exhibit A, B, C, D, etc... of how he's likely got the funds to do that and we all could relatively do nothing about it. So why is he still around? I think it's pretty simple. The usual answer, he wants to win. It's in his twitter handle for Christs sakes. I'll go a step further and say he likely also wants us to win by extension, arguably not as much as he wins, but I mean that's pretty locked in at the moment 🤣 That's not to say he hasn't long been encumbered. And in that state, at lot has gone on without him. Much of which is / was bad. $pDAI guys... I pointed out from day one how building all this around a protocol in a dangerous state was a risky move. And I was right about that.... on multiple occasions... But does that matter now? I suppose not as much. In its current state, it's seemingly no longer exploitable. No different than a meme token now. (presumably, not like I have deep dove on any further risks since ESM). So I guess just whale risk mainly now? Now a lot of people here are in the anti-pdai camp. Me too for what it's worth. But I don't care as much about it's negative anymore in its current state. A lot of people are still in the #pDAI camp strongly. We view this as tribalism, but it's important to note that makes all of us in the #PulseChain camp universally. So these day I find myself relatively pDAI neutral. If you guys want to send it to $1 do it. Only whales can stop you, they run out eventually. (insert super strong this is NOT financial advice). Hell you can maybe even use Sigma to help? Or maybe it wont help, idk. Depends on how people use the software. Conversely, when looking at chain state overall... Why is there nearly $50M in stables sitting on the sidelines. Why not just bridge it out if you want out of what you think is a dead chain. Surely leaving it there exposes you to bridge risk? Why all these yield movements, why the $HEX dusts.... Something is happening. People are seemingly waiting to see what that something is. Or I am reading into things, NFA as always. This whole post is just ramblings of someone trying to do the best they can and certainly not any kind of advice. When I look at other ecosystems, I see a level of polish we don't have. I see tooling we don't have, I see a fostered developer environment we don't have. So I've just been building it, painstakingly.... Because someone has to if we want to be taken seriously. And what I've been building has allowed me to get Sigma to where it is. Sigma is so close... Really just in UI mode, performance optimization, going through nice to haves. I don't believe in launching in a non-finished immutable state. So yeah, I take my time. As with everything. But my point with all of this, and the "why" #Sigma question.... It's unifying, anyone can participate. Which tribe you're in doesn't matter. And if you don't like it, don't use it. It's just software you can use or not use. As it should be. The years of tooling work to deliver this has been a lot of work for one guy in silence. In that time AI has appeared. My take, every dev should be using it. Given the right direction and context. It will make you better. If you blindly trust it, it will make you worse. GPT 5.4 audits smart contracts better than most auditing services. Especially if you give it the context of what you are trying to do. Anyways I digress, testnet is soon. Soon more meaning a feeling of near completion not always reality. That how software is. I do think Sigma stands to unify the chain in a common goal, and shift liquidity into more meaningful places, but ultimately it up to the people the decide to use the software or not use it. And after these frameworks I've built will be applied to what I am tentatively calling the universal hex UI. More or less something aggregative of every derivative I can reasonably support. With data and analytics we since lost. So not just $HEX, $HDRN, and $ICSA, but others as well. However, that depends on some aspect of $Sigma to exist first, so sigma first, chain unity first. And last but not least, take care of yourselves and strive to do cool things. If we aren't doing cool things then what's the point? Hope you think my UI looks good, I spent a while on it. /rant

Alex McWhirter

41,617 views • 3 months ago

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.
0:10

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,838,219 views • 2 months ago

Hey Kishu Crew, It's been a minute since we dropped a big announcement like this, hasn't it? You all know how much #kishu’s anniversary means to us—it's a day we hold close to our hearts, a day we celebrate with style. And this year is no exception. From its humble beginnings as a fun memecoin, #kishu has grown into something much bigger than we ever imagined. We've carved out a name for ourselves in the wild world of crypto, and boy, what a ride it's been. Sure, there are some new kids on the block now, grabbing attention like we did back in the day. But Kishu? We've earned our stripes. We're the veterans, the OGs—the ones who've seen it all and still stand tall. In crypto, time moves at warp speed. But here we are, three years deep into the game. Not many projects can boast that kind of longevity. We've learned so much along the way—established friendships, partnerships, and overcome obstacles that many others couldn't. Looking back, it's been one hell of a journey, both in the crypto world and beyond. Personally, Kishu has brought us together in ways we never imagined. It's reminded us of what really matters: friendship, health, quality time with loved ones. It's given us a sense of purpose, a sense of belonging—a feeling money can't buy. And on the business side of things? Well, Kishu's been one hell of a teacher. It's shown us that we're stronger than we think, that we can weather any storm that comes our way. We always come out on top. Because as Franklin D. Roosevelt once said, "A smooth sea never made a skilled sailor." So this year, to celebrate our birthday, we've got something special in store for you—a Kishuverse mini game like you've never seen before. Oh, sorry - we meant four games. Kishu, meet GameGPT by Prism—a game changer in every sense of the word. Powered by AI and blockchain technology, it's an AI game builder that puts the power of creation in your hands. Sure sparks some curiosity, doesn't it? It should. Check them out at their website and socials at: For over three years, the team behind GameGPT by PRISM has been hard at work, crafting an AI-powered engine that's revolutionizing the gaming world. And now, they're bringing that same innovation to Kishu! • kishuverse Quest: NFT Odyssey Each game is a love letter to our community, bringing the Kishuverse to life in ways we always dreamt of. So grab your #kishuverse NFTs, dive into the world of GameGPT, and let the adventure begin. And from all of us here at #kishu, a heartfelt thank you for your unwavering support. Here's to another year of making memories and breaking boundaries.❤️ Let's celebrate in style! 🎉
0:10

Hey Kishu Crew, It's been a minute since we dropped a big announcement like this, hasn't it? You all know how much #kishu’s anniversary means to us—it's a day we hold close to our hearts, a day we celebrate with style. And this year is no exception. From its humble beginnings as a fun memecoin, #kishu has grown into something much bigger than we ever imagined. We've carved out a name for ourselves in the wild world of crypto, and boy, what a ride it's been. Sure, there are some new kids on the block now, grabbing attention like we did back in the day. But Kishu? We've earned our stripes. We're the veterans, the OGs—the ones who've seen it all and still stand tall. In crypto, time moves at warp speed. But here we are, three years deep into the game. Not many projects can boast that kind of longevity. We've learned so much along the way—established friendships, partnerships, and overcome obstacles that many others couldn't. Looking back, it's been one hell of a journey, both in the crypto world and beyond. Personally, Kishu has brought us together in ways we never imagined. It's reminded us of what really matters: friendship, health, quality time with loved ones. It's given us a sense of purpose, a sense of belonging—a feeling money can't buy. And on the business side of things? Well, Kishu's been one hell of a teacher. It's shown us that we're stronger than we think, that we can weather any storm that comes our way. We always come out on top. Because as Franklin D. Roosevelt once said, "A smooth sea never made a skilled sailor." So this year, to celebrate our birthday, we've got something special in store for you—a Kishuverse mini game like you've never seen before. Oh, sorry - we meant four games. Kishu, meet GameGPT by Prism—a game changer in every sense of the word. Powered by AI and blockchain technology, it's an AI game builder that puts the power of creation in your hands. Sure sparks some curiosity, doesn't it? It should. Check them out at their website and socials at: For over three years, the team behind GameGPT by PRISM has been hard at work, crafting an AI-powered engine that's revolutionizing the gaming world. And now, they're bringing that same innovation to Kishu! • kishuverse Quest: NFT Odyssey Each game is a love letter to our community, bringing the Kishuverse to life in ways we always dreamt of. So grab your #kishuverse NFTs, dive into the world of GameGPT, and let the adventure begin. And from all of us here at #kishu, a heartfelt thank you for your unwavering support. Here's to another year of making memories and breaking boundaries.❤️ Let's celebrate in style! 🎉

Kishu Inu

38,949 views • 2 years ago