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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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336,702 次观看 • 8 个月前 •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.

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

55,459 次观看 • 11 天前

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

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 次观看 • 3 个月前

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."

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

119,803 次观看 • 11 天前

🚨 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 →

🚨 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 次观看 • 2 个月前

“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.

“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

232,914 次观看 • 3 年前

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.

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 次观看 • 2 个月前

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.

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 次观看 • 5 个月前

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

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 次观看 • 1 年前

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?

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 次观看 • 1 年前

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.

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

178,744 次观看 • 29 天前

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.

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 次观看 • 2 年前

#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 🐶 ❤️

#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

11,882 次观看 • 5 天前

#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 🐶 ❤️

#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 次观看 • 7 个月前

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

Hey #NeuraxonMini is literally out! , we manage to "transplant" a Neuraxon 2 bioinspired #AI brain to a physical robot the #SpheroMini moving from our last Scientific Paper (link bellow) by David Vivancos - e/acc & Jose Sánchez for Qubic #OpenScience hybridized with #Aigarth to the real World. First you need a Sphero Education Mini robot about 50$ Then you can try the first cool demos at Hugging Face: 1.- Neuraxon2MiniControl to drive the sphero robot 2.- Neuraxon2MiniWrite to write letters or words with physical moves of the sphero robot using Neuraxon Video Tutorials on youtube later today. Why this matters? Remember we are not building "dead" LLMs we are building #AliveAIs and for that we need to explore how it behaves in reality, from how it learns to how it fails, and what better way that in the emerging field of #robotics , time will tell if your next #HumanoidRobot have a #Neuraxon brain... Read the Paper: Explore the Neuraxon code here: Are you ready for #TrueAI ?

David Vivancos - e/acc

29,293 次观看 • 5 个月前

Today we are launching Kaito Studio beta. Starting today, we’re moving to a new model where brands and creators can more intentionally match based on mutual fit, selection, and expectations - launching with 16 partners, with more in the pipeline as details are finalized. Since launching our waitlist in February, a rapidly growing network of creators has joined Kaito Studio, representing 80 million collective followers and $14 billion in follower net worth. These creators span 118 countries, with English-focused creators making up the largest segment, and China and Korea representing the largest country cohorts. Around 30% have also joined with TikTok, Instagram, or YouTube accounts, positioning them to bring content cross-platform. From here, Kaito Studio will focus on solving three main problems, one step at a time: - Ambassador and creator matching - helping brands find the right ambassadors or creators based on data, audience, and subject alignment - Performance attribution - measuring real impact across influence, mindshare, and conversion - End-to-end orchestration - powering a repeatable workflow from creator matching and campaign execution to measurement, evaluation, and optimization We will ramp up opportunities over the coming weeks as more brands finalize their profiles and program details. As we continue building more features for the GenAI and agentic ecosystem, stay tuned for more opportunities ahead.

Today we are launching Kaito Studio beta. Starting today, we’re moving to a new model where brands and creators can more intentionally match based on mutual fit, selection, and expectations - launching with 16 partners, with more in the pipeline as details are finalized. Since launching our waitlist in February, a rapidly growing network of creators has joined Kaito Studio, representing 80 million collective followers and $14 billion in follower net worth. These creators span 118 countries, with English-focused creators making up the largest segment, and China and Korea representing the largest country cohorts. Around 30% have also joined with TikTok, Instagram, or YouTube accounts, positioning them to bring content cross-platform. From here, Kaito Studio will focus on solving three main problems, one step at a time: - Ambassador and creator matching - helping brands find the right ambassadors or creators based on data, audience, and subject alignment - Performance attribution - measuring real impact across influence, mindshare, and conversion - End-to-end orchestration - powering a repeatable workflow from creator matching and campaign execution to measurement, evaluation, and optimization We will ramp up opportunities over the coming weeks as more brands finalize their profiles and program details. As we continue building more features for the GenAI and agentic ecosystem, stay tuned for more opportunities ahead.

Kaito AI 🌊

590,203 次观看 • 4 个月前

Okay my lovelies, #WatchingYou is out on August 28, and I wouldn't lie to you, it's actually a pretty darned good book. But because I don't have Richard Osman's marketing budget, Louise Minchin's TV platform, Richard Coles' publicist or any other celebrity writer's stranglehold on the book world, it's hard to reach readers. So if you wonderful book social media folks could help me out, I'd be eternally grateful. You can pre-order this anywhere online (but signed/dedicated paperback copies are available through Steyning Bookshop). Or just RT this for me/get it from your local library/tell your friends/leave a review when you've read it...every small action helps keep this writer and this book afloat. Thanks all. Raising a teacup to you today!

Okay my lovelies, #WatchingYou is out on August 28, and I wouldn't lie to you, it's actually a pretty darned good book. But because I don't have Richard Osman's marketing budget, Louise Minchin's TV platform, Richard Coles' publicist or any other celebrity writer's stranglehold on the book world, it's hard to reach readers. So if you wonderful book social media folks could help me out, I'd be eternally grateful. You can pre-order this anywhere online (but signed/dedicated paperback copies are available through Steyning Bookshop). Or just RT this for me/get it from your local library/tell your friends/leave a review when you've read it...every small action helps keep this writer and this book afloat. Thanks all. Raising a teacup to you today!

Helen S Fields

46,189 次观看 • 1 年前

💰 FINAL PHASE LAUNCH : MONEY BAG NFT 💰 Capybaras we’ve hit our final milestone before BIG EVENT ! Get ready to mint your Money Bag NFTs on Sui Blockchain😘 💰What's New: ⁃ Convert your in-game coins into Money Bag NFTs ⁃ Each Money Bag costs 5 million in-game coins to mint and can hold up to 1️⃣5️⃣ MILLION coins! ⁃ The more bags you Open, the higher your multiplier for the Big Event 💰Benefits: ⁃ You can sell your Money Bag NFTs on for a quick reward or open them to earn more coins before the BIG EVENT snapshot ⁃ Get the BIG EVENT total balance multiplier by opening as many bags as you can 🧑‍💻 HOW TO GET YOUR MONEY BAG: ⁃ Ensure you have earned at least 5,000,000 coins to qualify ⁃ Click on a Money Bag icon near your total balance to Mint your Money Bag NFT Get Ready for the Big Event 🔥 - This is the final phase before our grand finale. Minting Money Bags now will prepare you for what's coming! Mint your Money Bag today and secure your spot at the top: 😎 Note: Some accounts may be restricted from Money Bag minting due to suspicious activity. Thank you for your support as we maintain fairness and reward active participation.

💰 FINAL PHASE LAUNCH : MONEY BAG NFT 💰 Capybaras we’ve hit our final milestone before BIG EVENT ! Get ready to mint your Money Bag NFTs on Sui Blockchain😘 💰What's New: ⁃ Convert your in-game coins into Money Bag NFTs ⁃ Each Money Bag costs 5 million in-game coins to mint and can hold up to 1️⃣5️⃣ MILLION coins! ⁃ The more bags you Open, the higher your multiplier for the Big Event 💰Benefits: ⁃ You can sell your Money Bag NFTs on for a quick reward or open them to earn more coins before the BIG EVENT snapshot ⁃ Get the BIG EVENT total balance multiplier by opening as many bags as you can 🧑‍💻 HOW TO GET YOUR MONEY BAG: ⁃ Ensure you have earned at least 5,000,000 coins to qualify ⁃ Click on a Money Bag icon near your total balance to Mint your Money Bag NFT Get Ready for the Big Event 🔥 - This is the final phase before our grand finale. Minting Money Bags now will prepare you for what's coming! Mint your Money Bag today and secure your spot at the top: 😎 Note: Some accounts may be restricted from Money Bag minting due to suspicious activity. Thank you for your support as we maintain fairness and reward active participation.

Capybara on Sui

10,175 次观看 • 1 年前

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,251 次观看 • 1 个月前

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap *is* taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you *see* what better looks like in the first place? 2. Even if you can see, can you *articulate* what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Taste is invisible until you try to write it down. This is probably my biggest lesson with AI building as of late. At Sundial, I get to work with really friggin' amazing analysts who know the art, and I see how much of our collective time now is now spent turning that art into playbooks or skills for an LLM. Encoding things like: "How would a great analyst actually look at this metric move?" or "What is ACTUALLY the interesting signal in this story versus noise?" or "How can we know if a product change actually moved the needle?" It's really humbling work! You write an instruction set. The LLM misses. You add more context. It still misses. You add even more. Now it's confused. You strip it back. Now it's too vague. You try a different framing. Better, but inconsistent. Works on Monday, fails on Tuesday. You go again. I've come to realize the gap between 70% quality and 95% quality is not 3 or 4 big things. It's more like 100s of small things. Which is exactly why you can't write an article about it, or copy it, or shortcut it! This gap is taste, quantified. The accumulated weight of a thousand small judgments you don't notice you're making, until you sit down to externalize them and realize you can't. Being good at something is not the same as being able to articulate why you're good at it. I now see two bottlenecks to making something better than today's generic AI: 1. Can you see what better looks like in the first place? 2. Even if you can see, can you articulate what that is in a way that the LLM can understand and systemize? #2 is now a new craft, the art of distilling the art. The people who can do it well are the ones building standout products.

Julie Zhuo

17,521 次观看 • 2 个月前