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Okay, this is wild—Unitree’s embodied AI stack is moving fast. 👏 WVLA 2.0 cleaned a messy conference room autonomously in one take. Now UnifoLM-OminiA-0.3 adds real-time omni-modal interaction, letting one model understand the scene and coordinate its whole body for home-care and wellness tasks.

RoboHub🤖

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Real-Time Omni-Modal Interaction Driven Whole-Body Mobile Manipulation🥳 Unitree UnifoLM-OminiA-0.3 — a single model handling diverse home-care and wellness tasks, with omni-modal interactive understanding, fully autonomous, stable and disturbance-resistant execution.

Real-Time Omni-Modal Interaction Driven Whole-Body Mobile Manipulation🥳 Unitree UnifoLM-OminiA-0.3 — a single model handling diverse home-care and wellness tasks, with omni-modal interactive understanding, fully autonomous, stable and disturbance-resistant execution.

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🤖 Introducing ω-0 (OMEGA-0) — a whole-body World Action Model for humanoids. Can one model make a humanoid walk, manipulate, and coordinate its whole body simultaneously across many real-world tasks? ω-0 does exactly that. ⚡ One unified model for multi-task whole-body loco-manipulation 🧠 Predicts future visual latents while generating executable whole-body actions 🏠 Trained with ω-HOME: 40+ hours, 24 household tasks, 4,827 episodes 🔥 81.8% real-world success rate across 11 evaluation tasks No separate locomotion/manipulation policies. One model. Whole body. Multiple tasks. Humanoid intelligence should not be a collection of isolated skills. It should be a unified whole-body policy. 🔗 📜 One-take, uncut real-world demo👇

🤖 Introducing ω-0 (OMEGA-0) — a whole-body World Action Model for humanoids. Can one model make a humanoid walk, manipulate, and coordinate its whole body simultaneously across many real-world tasks? ω-0 does exactly that. ⚡ One unified model for multi-task whole-body loco-manipulation 🧠 Predicts future visual latents while generating executable whole-body actions 🏠 Trained with ω-HOME: 40+ hours, 24 household tasks, 4,827 episodes 🔥 81.8% real-world success rate across 11 evaluation tasks No separate locomotion/manipulation policies. One model. Whole body. Multiple tasks. Humanoid intelligence should not be a collection of isolated skills. It should be a unified whole-body policy. 🔗 📜 One-take, uncut real-world demo👇

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I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

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Okay, this is genuinely impressive. 🤯 Oli just worked through a roomful of household chores — no teleop, no cuts, one continuous take. The brain is LimX COSA 0.5, a three-layer stack coordinating reasoning (Sys 2), skill execution (Sys 1), and motion control (Sys 0), along with memory, task scheduling, and whole-body coordination. What matters isn’t one polished demo trick. It’s that the robot stayed coherent across an entire long-horizon mobile manipulation sequence — on real hardware, in one take.

Okay, this is genuinely impressive. 🤯 Oli just worked through a roomful of household chores — no teleop, no cuts, one continuous take. The brain is LimX COSA 0.5, a three-layer stack coordinating reasoning (Sys 2), skill execution (Sys 1), and motion control (Sys 0), along with memory, task scheduling, and whole-body coordination. What matters isn’t one polished demo trick. It’s that the robot stayed coherent across an entire long-horizon mobile manipulation sequence — on real hardware, in one take.

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The possibilities are endless with Dreamina AI Seedance 2.0! I’ve been testing its omni-features, and it’s seriously impressive. It can handle multiple inputs at once, images, video, and audio, and still understand the full context perfectly. The biggest breakthrough? Seamless character swapping in existing videos with high precision. This is a huge step forward for creators. And the good news Dreamina Seedance 2.0 Fast is available for free trial to all users. What do you think? #dreamina #dreaminapartner #seedance #dreaminaseedance2 #seedream

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Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:

Today, we're announcing the next evolution of Sword Thrive: a fully revamped experience anchored by a new conversational care model, Vision AI, and a more powerful Phoenix. At the center of this evolution is a first-of-its-kind care model: a 3-way chat connecting the member, Phoenix, and their Doctor of Physical Therapy in a single interface, making continuous, 24/7 care possible for the first time, with the clinical oversight only a human specialist can provide. Vision AI, our proprietary technology trained on years of movement data, now delivers precise movement detection in real-world home environments - cluttered, noisy, and everything in between - and runs entirely through the Sword Health app on smartphones and tablets. Phoenix, our AI Care Specialist, is now more capable as well, providing deeper, personalized two-way conversational guidance and real-time form correction, adapting to each member as they move. From proactive care to continuous recovery, this is how healthcare should show up: with reliability, clinical memory, and real-time support that never drops the thread. Watch the video to see it in action:

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AI is moving fast. And OpenAI's new image model proves it — knowing how to use AI images to your advantage is now crucial for designers. So we teamed up with Nick St. Pierre (The AI Image Master) to walkthrough one of the best AI image generation tools right now, Midjourney, and build a full website using only AI images, in 1 hour. Watch the full breakdown👇

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X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.

X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.

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🤖Robots that think ahead and act in real time. LingBot-VA 2.0 — the first embodied-native foundation model. Not fine-tuned from a video generator. Built from scratch for the physical world. ✅ 93.6% success on bimanual tasks ⚡ 150 Hz single-GPU inference 🎯 20 demos to generalize. This is what happens when you stop adapting and start building natively. 🧵👇

🤖Robots that think ahead and act in real time. LingBot-VA 2.0 — the first embodied-native foundation model. Not fine-tuned from a video generator. Built from scratch for the physical world. ✅ 93.6% success on bimanual tasks ⚡ 150 Hz single-GPU inference 🎯 20 demos to generalize. This is what happens when you stop adapting and start building natively. 🧵👇

Robbyant

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OpenClaw just turned Discord into a 24/7 AI call center. Gemini 3.1 is now built in. That means your agent can read screenshots, understand the issue, and answer like a real support rep. Then it joins your Discord voice channel and talks live in real time. This isn’t “AI replies.” This is AI doing the job. Version 2026.2.21 also added threadbound sessions so multi-agent workflows stop becoming a chaotic group chat. Prompt caching makes repeat tasks way faster too. If you run a community, agency, or support channel, this update changes your whole ops stack. What would you use a voice agent for first, support, onboarding, or moderation?

OpenClaw just turned Discord into a 24/7 AI call center. Gemini 3.1 is now built in. That means your agent can read screenshots, understand the issue, and answer like a real support rep. Then it joins your Discord voice channel and talks live in real time. This isn’t “AI replies.” This is AI doing the job. Version 2026.2.21 also added threadbound sessions so multi-agent workflows stop becoming a chaotic group chat. Prompt caching makes repeat tasks way faster too. If you run a community, agency, or support channel, this update changes your whole ops stack. What would you use a voice agent for first, support, onboarding, or moderation?

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The future of home cleaning just landed in Shenzhen and it is walking right into your living room. 🤖🏠 X Square Robot and officially launched China’s first robot home service, moving embodied AI from the lab to your front door. When you book a cleaning on the app, a professional cleaner now shows up with an X Square robot partner to tag team the house. The human handles the tricky stuff that needs real judgment while the robot takes over repetitive tasks like wiping tables and tidying up surfaces. X Square is using an end to end foundation model which means the robot actually perceives and plans its own moves instead of just following a script. By testing in the messy reality of a real home, they are proving that if a robot can master a living room, it can handle almost any physical space. This pilot is part of a massive push to turn these machines into reliable partners that can actually assist in our daily lives.

The future of home cleaning just landed in Shenzhen and it is walking right into your living room. 🤖🏠 X Square Robot and officially launched China’s first robot home service, moving embodied AI from the lab to your front door. When you book a cleaning on the app, a professional cleaner now shows up with an X Square robot partner to tag team the house. The human handles the tricky stuff that needs real judgment while the robot takes over repetitive tasks like wiping tables and tidying up surfaces. X Square is using an end to end foundation model which means the robot actually perceives and plans its own moves instead of just following a script. By testing in the messy reality of a real home, they are proving that if a robot can master a living room, it can handle almost any physical space. This pilot is part of a massive push to turn these machines into reliable partners that can actually assist in our daily lives.

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AI is becoming a new computing model for media and entertainment. At the 2026 Runway AI Summit, NVIDIA’s Richard Kerris explored how real-time generative AI can make creative production a live, artist-controlled process. Powered by NVIDIA Vera Rubin, Runway brought Gen-4.5 to its platform in one day, helping close the gap between imagining a scene and bringing it to life.

AI is becoming a new computing model for media and entertainment. At the 2026 Runway AI Summit, NVIDIA’s Richard Kerris explored how real-time generative AI can make creative production a live, artist-controlled process. Powered by NVIDIA Vera Rubin, Runway brought Gen-4.5 to its platform in one day, helping close the gap between imagining a scene and bringing it to life.

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For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post

For the first time in history, GPT-6 Astra created a fly that is simply a fly. Not a chatbot pretending to be a fly. Not a simplified animation. A digital fly running its own connectome brain, perceiving a real room through its own senses and interacting with the environment using the power of AR. The entire loop is surprisingly wild: Spectacles capture the retina, light rays, hands, and the surrounding room → the room is converted into an inventory → sensory information is fed into the fly’s neural system → the MaleCNS connectome processes it → descending neurons and motor pools generate actions → the commands return to the Spectacles → wings, legs, and head respond. In other words, the fly sees through its own eyes, processes the world through its own simulated brain, and acts through its own virtual body. We are moving from AI that generates outputs to AI that actually has a body, senses, a brain, and a closed perception-action loop. The 🪰 is no longer just simulated. It is experiencing a world The developer Pavlo Tkachenko is pinned below in the post

Zentrix⌚️

133,088 görüntüleme • 19 saat önce

Canadian POLICE STATE GOES AI: Facial Recognition Body Cams Are NOW LIVE!! Edmonton Police just turned body cameras into AI facial recognition spy tools. Every face is scanned in real time against a secret watchlist with no consent and no warning. “Proactive surveillance,” they call it. And just in time, Toronto Police dropped their slick “Project Guardian” propaganda video: tactical units in full battle gear marching through your streets under the banner of “visible presence.” Body cams were supposed to watch the cops. Now the cops and their AI are watching YOU. In this video Dan Dicks of Press For Truth shows that this is how freedom dies…one pilot program and one militarized patrol at a time.

Canadian POLICE STATE GOES AI: Facial Recognition Body Cams Are NOW LIVE!! Edmonton Police just turned body cameras into AI facial recognition spy tools. Every face is scanned in real time against a secret watchlist with no consent and no warning. “Proactive surveillance,” they call it. And just in time, Toronto Police dropped their slick “Project Guardian” propaganda video: tactical units in full battle gear marching through your streets under the banner of “visible presence.” Body cams were supposed to watch the cops. Now the cops and their AI are watching YOU. In this video Dan Dicks of Press For Truth shows that this is how freedom dies…one pilot program and one militarized patrol at a time.

Dan Dicks

48,314 görüntüleme • 5 ay önce

So cool...a humanoid just drove a go-kart for real. A Unitree G1 lowers itself into the tight cockpit, hands on the wheel, feet on the pedals, then runs the track， even drifts. It shows constrained whole-body loco-manipulation, mobile pick-and-place, and hand-eye-foot coordination in one sequence. This comes from China’s Symbiosis Robotics and their Direct Perception Control (DPC) model. It skips the usual high-level goals + tracker stack, mapping vision, language, body state and feedback straight to joint and hand targets. The focus is whole-body coordination for loco-manipulation. Interestingly, data diversity...Training used 15,010 hours of joint-aligned data-- 6,781h human egocentric, 4,024h armed robots, 3,660h wheeled humanoids, 545h bipedal. Teleop, head-mounted and egocentric sources were all converted into time-aligned joint trajectories across embodiments. A clean test that packs perception, balance and continuous whole-body control into one task chain ,and leaves room for other real-world scenarios.

So cool...a humanoid just drove a go-kart for real. A Unitree G1 lowers itself into the tight cockpit, hands on the wheel, feet on the pedals, then runs the track， even drifts. It shows constrained whole-body loco-manipulation, mobile pick-and-place, and hand-eye-foot coordination in one sequence. This comes from China’s Symbiosis Robotics and their Direct Perception Control (DPC) model. It skips the usual high-level goals + tracker stack, mapping vision, language, body state and feedback straight to joint and hand targets. The focus is whole-body coordination for loco-manipulation. Interestingly, data diversity...Training used 15,010 hours of joint-aligned data-- 6,781h human egocentric, 4,024h armed robots, 3,660h wheeled humanoids, 545h bipedal. Teleop, head-mounted and egocentric sources were all converted into time-aligned joint trajectories across embodiments. A clean test that packs perception, balance and continuous whole-body control into one task chain ,and leaves room for other real-world scenarios.

CyberRobo

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Until now, AI agent ran on one machine, and one workflow at a time. A memory-heavy step crashed six hours in, and the run started over. Biomni agent now autonomously decides how many machines it needs, how much CPU and memory each task requires, spins them up, and distributes work across them. It operates like a coordinated team working in parallel, rather than one researcher at a work station. This is the foundation for agent-managed infrastructure.

Until now, AI agent ran on one machine, and one workflow at a time. A memory-heavy step crashed six hours in, and the run started over. Biomni agent now autonomously decides how many machines it needs, how much CPU and memory each task requires, spins them up, and distributes work across them. It operates like a coordinated team working in parallel, rather than one researcher at a work station. This is the foundation for agent-managed infrastructure.

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