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Announcing the OpenMind BrainPack, launching today. What you can get: - BrainPack: Our hardware add-on to robots, giving them autonomous features - NVIDIA Thor GPU - UniTree Go2 Robot Dog or UniTree G1 Humanoid - OM1 Credits Pre-order today:

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337,992 görüntüleme • 10 ay önce •via X (Twitter)

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Pi Ventures-Backed OpenMind Opens Waitlist For Humanoid Robot Control Pi Network (Pi Network) Ventures backed OpenMind (OpenMind) has opened the waitlist for Instabody, a platform that lets users remotely control a real humanoid robot. The company said users can see the world through the robot's eyes and directly control its movements. It believes giving people the controls will help shape how robots evolve. OpenMind previously launched a robot app store built on its OM1 operating system, allowing developers to add new skills to humanoid and quadruped robots.
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Pi Ventures-Backed OpenMind Opens Waitlist For Humanoid Robot Control Pi Network (Pi Network) Ventures backed OpenMind (OpenMind) has opened the waitlist for Instabody, a platform that lets users remotely control a real humanoid robot. The company said users can see the world through the robot's eyes and directly control its movements. It believes giving people the controls will help shape how robots evolve. OpenMind previously launched a robot app store built on its OM1 operating system, allowing developers to add new skills to humanoid and quadruped robots.

BSCN

56,147 görüntüleme • 2 ay önce

NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0
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NVIDIA DROPPED A MOTION DIFFUSION MODEL FOR HUMANOID ROBOTS trained on 700 hours of mocap data kimodo generates high-quality 3D human and robot motions from text prompts you control it with: → full-body pose keyframes → end-effector positions/rotations → 2D paths and waypoints works on human skeletons and unitree G1 robot plug the outputs directly into mujoco or retarget to other robots using GMR has a web-based interactive demo with a timeline editor. runs locally needs ~17GB VRAM to run inference open source under apache 2.0

Vaishnavi

17,572 görüntüleme • 5 ay önce

Elon Musk today on Optimus robot: "You’ve probably seen a lot of impressive demos of robots on the internet, but those demonstrations are pre-programmed or remote-controlled. There is no humanoid robot that can actually do generalized tasks. Optimus will be the first one that will be capable of doing that, in just a demo, it’s generally useful in day-to-day life."
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Elon Musk today on Optimus robot: "You’ve probably seen a lot of impressive demos of robots on the internet, but those demonstrations are pre-programmed or remote-controlled. There is no humanoid robot that can actually do generalized tasks. Optimus will be the first one that will be capable of doing that, in just a demo, it’s generally useful in day-to-day life."

Nic Cruz Patane

120,482 görüntüleme • 2 ay önce

🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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🚨 BREAKING: NVIDIA just announced the Isaac GR00T Reference Humanoid Robot. The first fully open humanoid robot reference design built on Jetson Thor, and it's going straight to the world's top research institutions. This is Jensen Huang's bet on open physical AI infrastructure. The hardware stack is serious: → Unitree H2 Plus chassis, 6 feet tall, 150 pounds, 31 degrees of freedom → Sharpa Wave tactile five-finger hands, 22 degrees of freedom, bringing total to 75 across the full body → NVIDIA Jetson AGX Thor onboard compute, 2,070 FP4 teraflops of AI performance, 128GB unified memory → Multi-view sensing, stereo head camera, wrist cameras, IMU Alongside this announcement, Unitree also introduced the H2 Plus as a standalone product, a frontier humanoid combining Unitree's own body, Sharpa's five-finger hands and NVIDIA Robotics Jetson Thor compute into one fully integrated research platform. The full Isaac GR00T software stack ships with it, teleoperation for data capture, open foundation models, Isaac Sim for training, Isaac Lab for evaluation, and accelerated ROS middleware for deployment. The complete loop from data to real-world robot in one unified platform. ETH Zürich, Stanford Robotics Center, UC San Diego and Ai2 are already on board as launch research partners. NVIDIA Robotics did to AI what it's now doing to robotics, build the platform, open the ecosystem, let the world build on top of it. Whoever owns the infrastructure layer wins. NVIDIA knows this better than anyone. 👀 Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

16,062 görüntüleme • 3 ay önce

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.
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What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.

Rerun

11,355 görüntüleme • 16 gün önce

Hold 10,000 $CEREBRO and start earning AI inference credits. Today, you can use those credits through the API or trade them on the Cerebro Marketplace. But Phase 3 gives them another purpose. AI agents need compute to run. Every response, model call and task consumes inference. As more agents and applications are built through Cerebro, those credits become part of what actually keeps them running. Something you earn today becomes something the ecosystem can need tomorrow. That’s what Phase 3 starts to unlock.
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Hold 10,000 $CEREBRO and start earning AI inference credits. Today, you can use those credits through the API or trade them on the Cerebro Marketplace. But Phase 3 gives them another purpose. AI agents need compute to run. Every response, model call and task consumes inference. As more agents and applications are built through Cerebro, those credits become part of what actually keeps them running. Something you earn today becomes something the ecosystem can need tomorrow. That’s what Phase 3 starts to unlock.

Cerebro

14,636 görüntüleme • 12 gün önce

“Get vaccinated…and you know what? If you don’t want to get vaccinated…don’t even think you can get on a plane or a train…and sit beside vaccinated people and put them at risk…” -Trudeau who admitted today that the poisonous vaccine has harmed/killed millions of Canadians.
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“Get vaccinated…and you know what? If you don’t want to get vaccinated…don’t even think you can get on a plane or a train…and sit beside vaccinated people and put them at risk…” -Trudeau who admitted today that the poisonous vaccine has harmed/killed millions of Canadians.

Liz Churchill

232,934 görüntüleme • 3 yıl önce

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 görüntüleme • 4 ay önce

40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.
0:11

40 hours of human work. That’s what this humanoid could save every month! A construction company is already testing a Unitree G1 on a real job site, using the robot for site inspections, 360° imaging, data collection and progress monitoring. The robot starts at around $13,500, while the company says its deployment can save roughly 40 hours of work every month. That adds up to around 480 hours a year from a machine that costs a fraction of traditional industrial equipment. The interesting part is that this isn't about replacing an entire construction worker. It's about removing hundreds of repetitive hours from the workflow, including walking inspection routes, documenting progress and collecting information across the site. Humans can then spend their time on decisions and tasks that actually require them. This is where humanoid robots become economically interesting. Construction sites are already designed around human movement, so a robot with two arms, two legs and a human-sized body can potentially work in the same spaces without rebuilding the entire environment. Every additional task it learns turns those same hardware costs into more productive hours. And the economics get even more interesting as prices fall and production scales. A robot that saves 480 hours per year doesn't need to be perfect or replace a full-time employee to justify its existence. It just needs to reliably take over the boring, repetitive work that companies are already paying humans to do. 480 hours saved. Thousands of dollars in hardware. One construction site. This is how humanoids will enter the workforce, not by replacing everyone overnight, but by quietly taking over the hours nobody wants to spend.

Future Memo

18,925 görüntüleme • 12 gün önce

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

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

Pranjal Bora 🧭

29,321 görüntüleme • 1 ay önce

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

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

Rohan Paul

37,121 görüntüleme • 7 ay önce

We’re delighted to announce that Pineapple has officially joined the NVIDIA Developer Program! 🍍🤝 What Benefits Does This Provide To Pineapple? ✅🍍 Enables Innovations with GPU-Optimized Software: The heart of NVIDIA’s developer resources is access to hundreds of software and performance analysis tools across diverse industries and use cases, from AI and HPC to autonomous vehicles, robotics, simulation, and more. These SDKs and tools can be obtained in multiple ways, including containers, pre-trained models, and Helm charts from the NGC catalog applications from Linux repositories, and source code from NVIDIA's GitHub repositories. ✅🍍Accelerates Higher Education and Research: NVIDIA offers an array of benefits to developers, educators, and researchers in academia, including NVIDIA DLI Teaching Kits , DLI Programs for Educators, Higher Education and Research Grants , Educational Pricing, and Graduate Fellowships. ✅🍍Supports Cutting-Edge Startups with NVIDIA Inception: NVIDIA Inception - the leading accelerator of AI, data science, and HPC startups - supports startups worldwide with go-to-market support, expertise, and technology. Startups get access to training through NVIDIA’s Deep Learning Institute, preferred pricing on hardware through our global network of distributors, invitations to exclusive networking events, and more. ✅🍍Pineapple will utilise NVIDIA’s cutting-edge tools and technology to accelerate development in decentralized trading. This will help us bring even more powerful features to the our ecosystem! $PAPPLE
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We’re delighted to announce that Pineapple has officially joined the NVIDIA Developer Program! 🍍🤝 What Benefits Does This Provide To Pineapple? ✅🍍 Enables Innovations with GPU-Optimized Software: The heart of NVIDIA’s developer resources is access to hundreds of software and performance analysis tools across diverse industries and use cases, from AI and HPC to autonomous vehicles, robotics, simulation, and more. These SDKs and tools can be obtained in multiple ways, including containers, pre-trained models, and Helm charts from the NGC catalog applications from Linux repositories, and source code from NVIDIA's GitHub repositories. ✅🍍Accelerates Higher Education and Research: NVIDIA offers an array of benefits to developers, educators, and researchers in academia, including NVIDIA DLI Teaching Kits , DLI Programs for Educators, Higher Education and Research Grants , Educational Pricing, and Graduate Fellowships. ✅🍍Supports Cutting-Edge Startups with NVIDIA Inception: NVIDIA Inception - the leading accelerator of AI, data science, and HPC startups - supports startups worldwide with go-to-market support, expertise, and technology. Startups get access to training through NVIDIA’s Deep Learning Institute, preferred pricing on hardware through our global network of distributors, invitations to exclusive networking events, and more. ✅🍍Pineapple will utilise NVIDIA’s cutting-edge tools and technology to accelerate development in decentralized trading. This will help us bring even more powerful features to the our ecosystem! $PAPPLE

Pineapple $PAPPLE

16,871 görüntüleme • 1 yıl önce

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
0:16

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

Robert Scoble

33,804 görüntüleme • 1 yıl önce

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.
0:11

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Alok

182,483 görüntüleme • 2 ay önce

excited to launch AI Reality TV today! our new platform lets you create your own social simulations. ever wondered if elisabeth preferred jack or will in pirates of the caribbean? now you can simulate and see for yourself! here's how it works: 1. choose a map and scenario. 2. add and customize your characters 3. watch the drama unfold as AI-powered characters interact. 4. talk to them to get their perspective. this is the start of a new kind of entertainment! drop a comment and I'll send you access.
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excited to launch AI Reality TV today! our new platform lets you create your own social simulations. ever wondered if elisabeth preferred jack or will in pirates of the caribbean? now you can simulate and see for yourself! here's how it works: 1. choose a map and scenario. 2. add and customize your characters 3. watch the drama unfold as AI-powered characters interact. 4. talk to them to get their perspective. this is the start of a new kind of entertainment! drop a comment and I'll send you access.

Edgar Haond

46,045 görüntüleme • 2 yıl önce

#ThoughtForTheDay Getting older is just a part of life, but for a Dog, it's the ultimate test of loyalty. We don't mind the grey on our muzzles or the cloudy eyes, as long as your hand is still there to steady us. We don't need to run for miles anymore; we just need to know that you'll walk at our pace, however slow that may be. If you are lucky enough to have an old Dog waiting for you today, please be gentle. Give them an extra minute to get up, a little boost when the stairs feel like mountains, and all the patience in the world. They spent their whole life rushing to the door for you; now, it's YOUR turn to wait for them. The older #DogsOfTwitter 🐶 ❤️
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#ThoughtForTheDay Getting older is just a part of life, but for a Dog, it's the ultimate test of loyalty. We don't mind the grey on our muzzles or the cloudy eyes, as long as your hand is still there to steady us. We don't need to run for miles anymore; we just need to know that you'll walk at our pace, however slow that may be. If you are lucky enough to have an old Dog waiting for you today, please be gentle. Give them an extra minute to get up, a little boost when the stairs feel like mountains, and all the patience in the world. They spent their whole life rushing to the door for you; now, it's YOUR turn to wait for them. The older #DogsOfTwitter 🐶 ❤️

PROTECT ALL WILDLIFE

12,091 görüntüleme • 1 ay önce

#ThoughtForTheDay Getting older is just a part of life, but for a Dog, it's the ultimate test of loyalty. We don't mind the grey on our muzzles or the cloudy eyes, as long as your hand is still there to steady us. We don't need to run for miles anymore; we just need to know that you'll walk at our pace, however slow that may be. If you are lucky enough to have an old Dog waiting for you today, please be gentle. Give them an extra minute to get up, a little boost when the stairs feel like mountains, and all the patience in the world. They spent their whole life rushing to the door for you; now, it's your turn to wait for them. The older #DogsOfTwitter 🐶 ❤️
0:10

#ThoughtForTheDay Getting older is just a part of life, but for a Dog, it's the ultimate test of loyalty. We don't mind the grey on our muzzles or the cloudy eyes, as long as your hand is still there to steady us. We don't need to run for miles anymore; we just need to know that you'll walk at our pace, however slow that may be. If you are lucky enough to have an old Dog waiting for you today, please be gentle. Give them an extra minute to get up, a little boost when the stairs feel like mountains, and all the patience in the world. They spent their whole life rushing to the door for you; now, it's your turn to wait for them. The older #DogsOfTwitter 🐶 ❤️

PROTECT ALL WILDLIFE

46,519 görüntüleme • 8 ay önce

the most beautiful DIY robot build i've ever seen: this guy turned a robot dog into an off-road wheelchair so his dad could go hiking again his dad used to run marathons, but 20 years ago multiple sclerosis put him in a wheelchair. so his son Jake took a Unitree B2-W (an industrial robot with 4 legs and a wheel on each foot) and built a custom seat on top. on flat ground it rolls just like a normal wheelchair. but when it reaches rocks, water, or stairs, each leg can lift and adjust on its own, allowing it to step right over them. getting this to work safely took a while. > the robot was never designed to carry a person (it tipped over many times during testing) > so Jake spent 3 years recalibrating it. he tested it on his friends first, then once it was stable enough his dad finally got to try it. > and after 20 years in a wheelchair, he climbed a hiking trail again. a chair like this could eventually give wheelchair users access to trails, beaches, broken sidewalks, all the places that are still out of reach today. and one dude built it in his garage lol we truly live in a golden age where a single person can create world-changing tech
0:20

the most beautiful DIY robot build i've ever seen: this guy turned a robot dog into an off-road wheelchair so his dad could go hiking again his dad used to run marathons, but 20 years ago multiple sclerosis put him in a wheelchair. so his son Jake took a Unitree B2-W (an industrial robot with 4 legs and a wheel on each foot) and built a custom seat on top. on flat ground it rolls just like a normal wheelchair. but when it reaches rocks, water, or stairs, each leg can lift and adjust on its own, allowing it to step right over them. getting this to work safely took a while. > the robot was never designed to carry a person (it tipped over many times during testing) > so Jake spent 3 years recalibrating it. he tested it on his friends first, then once it was stable enough his dad finally got to try it. > and after 20 years in a wheelchair, he climbed a hiking trail again. a chair like this could eventually give wheelchair users access to trails, beaches, broken sidewalks, all the places that are still out of reach today. and one dude built it in his garage lol we truly live in a golden age where a single person can create world-changing tech

Ole Lehmann

15,697 görüntüleme • 2 ay önce

honestly no surprise why Silicon Valley is so obsessed with Matic robots right now > basically a Roomba on steroids > vacuums first, then mops > uses five cameras to build a live 3D map of your home > recognizes rugs, wires, furniture, pets, and people, then changes how it cleans > point at a mess and say “hey Matic, clean this” and it does > an NVIDIA Jetson inside the robot handles all the vision, mapping, and navigation > raw footage is discarded in real time and your 3D map never leaves the device btw that last privacy part is extremely underrated IMO my biggest fear with robotics is putting moving cameras and microphones inside our most private spaces without knowing where the data goes. > this week, camera components on Royal Navy drones were caught phoning home to China > Chinese Unitree robot dogs were found with a backdoor that let anyone with the key remotely control them and watch through their cameras > Roombas sent images from inside homes to overseas labelers, including a woman on the toilet and a child your home is your most sacred private space. if you're gonna buy a robot that can physically record, map, and move through it, make sure it's secure!
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honestly no surprise why Silicon Valley is so obsessed with Matic robots right now > basically a Roomba on steroids > vacuums first, then mops > uses five cameras to build a live 3D map of your home > recognizes rugs, wires, furniture, pets, and people, then changes how it cleans > point at a mess and say “hey Matic, clean this” and it does > an NVIDIA Jetson inside the robot handles all the vision, mapping, and navigation > raw footage is discarded in real time and your 3D map never leaves the device btw that last privacy part is extremely underrated IMO my biggest fear with robotics is putting moving cameras and microphones inside our most private spaces without knowing where the data goes. > this week, camera components on Royal Navy drones were caught phoning home to China > Chinese Unitree robot dogs were found with a backdoor that let anyone with the key remotely control them and watch through their cameras > Roombas sent images from inside homes to overseas labelers, including a woman on the toilet and a child your home is your most sacred private space. if you're gonna buy a robot that can physically record, map, and move through it, make sure it's secure!

Ole Lehmann

45,092 görüntüleme • 1 ay önce