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QACE Dynamics | Qognix Rule Engine Qognix is built on a library of modular AI blocks that can be reused across many applications. Each block has NLP built in, so it understands natural instructions and converts them into intents, rules, and actions. The blocks are organized as tiles for... specific domains such as surveillance, navigation, manipulation, or voice, which makes it simple to assemble intelligence for any robot. Every block is simulation ready, so engineers can validate workflows inside the dApp or in Gazebo before deployment. Once tested, the same blocks run seamlessly on hardware through QARC or directly on the robot. This approach turns robotics AI into a system of reusable and composable parts. From perception to decisions, Qognix makes intent driven autonomy practical and scalableshow more

Qace Dynamics

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36,922 views • 10 months ago •via X (Twitter)

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Qace Dynamics | End to End Autonomy Today we show the full workflow of how QACE brings autonomy to robots. Inside the dApp you can define inputs, outputs, and rules, then add modular AI blocks that map directly into reusable ROS2 nodes and DDS communication interfaces. The system interprets natural instructions through the NLP core, compiles them into abstract syntax trees, builds the directed execution graph, and generates the scripts automatically. The workflow is validated back in Gazebo where detections, temporal operators, and event triggers are applied in real time against the simulation runtime. From there the same pipeline can be containerized and deployed on QARC or directly on the robot with no additional coding or manual integration. This is how we replace fragmented pipelines and redundant middleware with one unified framework where high level intent is compiled into deterministic autonomy graphs.

Qace Dynamics | End to End Autonomy Today we show the full workflow of how QACE brings autonomy to robots. Inside the dApp you can define inputs, outputs, and rules, then add modular AI blocks that map directly into reusable ROS2 nodes and DDS communication interfaces. The system interprets natural instructions through the NLP core, compiles them into abstract syntax trees, builds the directed execution graph, and generates the scripts automatically. The workflow is validated back in Gazebo where detections, temporal operators, and event triggers are applied in real time against the simulation runtime. From there the same pipeline can be containerized and deployed on QARC or directly on the robot with no additional coding or manual integration. This is how we replace fragmented pipelines and redundant middleware with one unified framework where high level intent is compiled into deterministic autonomy graphs.

Qace Dynamics

53,180 views • 10 months ago

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.

QACE Dynamics | VIBR Tech Preview 1 Introducing VIBR, the vision intelligence block that brings real perception to robots. In this first preview, we take a simple vacuum bot, connect it through the QACE dApp, and instantly give it object detection and smarter behavior without touching code. QACE makes it easy by automating the usual workflow: what once took hours of coding in Python and assembling AI models is now reduced to a few clicks. Complex AI vision blocks are created, configured, and ready-to-deploy scripts are generated automatically. Robots can now see, decide, and act in real time. Up next: we will show how these rules and actions evolve into full autonomy modules, with the Qognix Tech Preview dropping this week.

Qace Dynamics

29,448 views • 10 months ago

QACE Dynamics | Inside the Making of the Robotics Lab A first look at the core setup behind QACE. Three Jetson Orin NX based QARC units, an Intel mini system, and a firewall are now active as part of the infrastructure powering the decentralised robotics lab. The QACE dApp is already connected to these systems, running live simulations and processing autonomy workloads directly on them. These NVIDIA-powered edge units serve as the backbone for real-time AI inference, multi-sensor processing, and distributed control inside the QACE ecosystem. Very soon, AGVs, robotic arms, and vision systems will be connected to this network as we move toward a live, fully integrated setup. Stay tuned for the next video where we show the robots running on this infrastructure. #QACE #RoboticsAI #EdgeAI #QARC #RoboticsLab #Qognix

QACE Dynamics | Inside the Making of the Robotics Lab A first look at the core setup behind QACE. Three Jetson Orin NX based QARC units, an Intel mini system, and a firewall are now active as part of the infrastructure powering the decentralised robotics lab. The QACE dApp is already connected to these systems, running live simulations and processing autonomy workloads directly on them. These NVIDIA-powered edge units serve as the backbone for real-time AI inference, multi-sensor processing, and distributed control inside the QACE ecosystem. Very soon, AGVs, robotic arms, and vision systems will be connected to this network as we move toward a live, fully integrated setup. Stay tuned for the next video where we show the robots running on this infrastructure. #QACE #RoboticsAI #EdgeAI #QARC #RoboticsLab #Qognix

Qace Dynamics

10,191 views • 8 months ago

QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.

QACE Dynamics | Autonomous Navigation Preview Today we are showing autonomous navigation running live inside QACE, the robot plans its route on the map, then adjusts in real time as lidar and vision pick up new obstacles. All of this runs through a single navigation block inside QACE, ready to plug into different robots without touching low-level control. This navigation block is one of the core building pieces of QACE autonomy, and it’s just the first of many we will be rolling out.

Qace Dynamics

12,654 views • 8 months ago

🤯incredible,its playability and usability are outstanding. Tron2 is a LEGO robot. It can be assembled into various forms depending on the scenario, including bipedal, wheeled, and humanoid torso configurations, and its end effectors are also interchangeable. Each form of the robot is driven by a VLA model. It can perform complex movements and can also be integrated into existing workflows to perform tasks. It's worth mentioning that this solves the problem of which type of humanoid robot to use. Each user can assemble the robot in a configuration that suits their specific scenario, making it truly flexible and versatile – it doesn't necessarily have to be bipedal or wheeled... I look forward to seeing more real-world use cases.

🤯incredible,its playability and usability are outstanding. Tron2 is a LEGO robot. It can be assembled into various forms depending on the scenario, including bipedal, wheeled, and humanoid torso configurations, and its end effectors are also interchangeable. Each form of the robot is driven by a VLA model. It can perform complex movements and can also be integrated into existing workflows to perform tasks. It's worth mentioning that this solves the problem of which type of humanoid robot to use. Each user can assemble the robot in a configuration that suits their specific scenario, making it truly flexible and versatile – it doesn't necessarily have to be bipedal or wheeled... I look forward to seeing more real-world use cases.

CyberRobo

147,344 views • 7 months ago

Seattle Seahawks HC Mike Macdonald - Pressure vs Duo Scheme - "I want to take blocks they're going to have and I want to move them. So the guys that are getting single blocks, I want to wipe those blocks. A lot of times I'm moving into the Duo, so I can wipe the Center's block and Edge pressure away from where the TE is. Or I want to create space in the C gap and I can try to penetrate it there." - "But any time we're looking at early down run pressures, it's all about the blocks you're going to get & how you can move/manipulate those blocks to try & get someone running free or attack those run schemes."

Seattle Seahawks HC Mike Macdonald - Pressure vs Duo Scheme - "I want to take blocks they're going to have and I want to move them. So the guys that are getting single blocks, I want to wipe those blocks. A lot of times I'm moving into the Duo, so I can wipe the Center's block and Edge pressure away from where the TE is. Or I want to create space in the C gap and I can try to penetrate it there." - "But any time we're looking at early down run pressures, it's all about the blocks you're going to get & how you can move/manipulate those blocks to try & get someone running free or attack those run schemes."

James Light

32,464 views • 3 months ago

Seattle Seahawks HC Mike Macdonald - Pressure vs Duo Scheme - "I want to take blocks they're going to have and I want to move them. So the guys that are getting single blocks, I want to wipe those blocks. A lot of times I'm moving into the Duo, so I can wipe the Center's block and Edge pressure away from where the TE is. Or I want to create space in the C gap and I can try to penetrate it there." "But any time we're looking at early down run pressures, it's all about the blocks you're going to get & how you can move/manipulate those blocks to try & get someone running free or attack those run schemes."

Seattle Seahawks HC Mike Macdonald - Pressure vs Duo Scheme - "I want to take blocks they're going to have and I want to move them. So the guys that are getting single blocks, I want to wipe those blocks. A lot of times I'm moving into the Duo, so I can wipe the Center's block and Edge pressure away from where the TE is. Or I want to create space in the C gap and I can try to penetrate it there." "But any time we're looking at early down run pressures, it's all about the blocks you're going to get & how you can move/manipulate those blocks to try & get someone running free or attack those run schemes."

James Light

213,211 views • 6 months ago

This looks like another step toward removing the data bottleneck in robotics. The bottleneck exists because recording demonstrations is easy, but labeling every grasp, hold, move, and release is slow and expensive. Perceptron Egocentric dropped a new robotics video annotation system. You give it raw robot-camera or first-person/egocentric video. It gives back structured labels that look more like training data: where each small manipulation starts and ends, what the subtask is, what each hand is doing, where the hands are frame by frame, and which hand is left or right. So, Input: raw video of a person or robot doing a task. Output: a machine-readable breakdown of the physical actions inside that video. Basically, it is a machine that converts robot video into policy-training supervision. That means timestamps, subtask boundaries, per-hand actions, and left-right hand grounding. So this will turn large piles of robot or egocentric video into supervision for training robot policies.

This looks like another step toward removing the data bottleneck in robotics. The bottleneck exists because recording demonstrations is easy, but labeling every grasp, hold, move, and release is slow and expensive. Perceptron Egocentric dropped a new robotics video annotation system. You give it raw robot-camera or first-person/egocentric video. It gives back structured labels that look more like training data: where each small manipulation starts and ends, what the subtask is, what each hand is doing, where the hands are frame by frame, and which hand is left or right. So, Input: raw video of a person or robot doing a task. Output: a machine-readable breakdown of the physical actions inside that video. Basically, it is a machine that converts robot video into policy-training supervision. That means timestamps, subtask boundaries, per-hand actions, and left-right hand grounding. So this will turn large piles of robot or egocentric video into supervision for training robot policies.

Rohan Paul

18,391 views • 26 days ago

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

383,061 views • 1 year ago

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The World Model as NEO's Cognitive Core 1X has revealed a major AI development where the NEO humanoid can translate any natural language prompt into robotic action. It demonstrates this capability even for novel tasks, objects, and environments not found in its robot dataset. - the 1X World Model is trained on internet-scale human interaction videos and fine-tuned with robot data to ground its understanding in physics and in NEO's embodiment - from a simple voice or text prompt, the world model generates a visualization of future actions - a built-in inverse dynamics model then translates these into precise motor movements for NEO

The Humanoid Hub

68,453 views • 6 months ago

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

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

Ilir Aliu

42,864 views • 6 months ago

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot *and* a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.

This robot assistant from the NVIDIA CES Keynote on Monday is going viral. Nader Khalil🍊 explains all the hottest emerging AI trends in one demo: AI applications in 2026 will be multi-model, multi-modal, hybrid cloud/local, use open source models as well as proprietary models, control robots and embedded devices in the physical world, and have voice interfaces. (And the demo had a cute robot and a cute dog. Gold.) The demo was built with Pipecat AI. NVIDIA posted a really nice technical walk-through and complete code. The Reachy Mini robot from Hugging Face is open source hardware. (You can order it now, I have one!). You can run the assistant locally on your own hardware, in the cloud, or both.

kwindla

49,020 views • 7 months ago

💡 What is a Gaia Domain?! Gaia Domains were created to make AI infrastructure truly sovereign. Each domain is a programmable environment you own and control on-chain, where your logic, models, and data stay in your hands at all times. With Gaia, you set the rules: choose which AI agents to deploy, configure your workflows, and decide how others can interact with your domain. Domains integrate seamlessly with smart contracts, other domains, and a wide range of decentralized tools is simple and secure. Every part of your domain operates on Gaia’s decentralized node network, protecting your work from centralized servers or closed AI systems. Thankfully, getting started is quick. Domains are ready out of the box with open-source language models and SDKs, so you can launch and iterate fast. Their modular design makes them easy to adapt, and EVM compatibility means they fit right into the broader DeAI ecosystem. Earning is built in at every step: you can run services, sell access, or enable delegated tasks with all transactions managed on-chain for total transparency. Gaia Domains open new ground for developers, DAOs, founders, researchers, and enterprises seeking to decentralize their AI stack. Questions? Reach out to our DevRels Tobiloba 🦀 Harish Kotra 🥑 meowy🦀! Get started here: In this video, Tobiloba 🦀 demonstrates how to integrate Gaia's public domains for AI agents, applications, chatbots, and frameworks, or even interact directly via the Gaia chat UI 🔽

💡 What is a Gaia Domain?! Gaia Domains were created to make AI infrastructure truly sovereign. Each domain is a programmable environment you own and control on-chain, where your logic, models, and data stay in your hands at all times. With Gaia, you set the rules: choose which AI agents to deploy, configure your workflows, and decide how others can interact with your domain. Domains integrate seamlessly with smart contracts, other domains, and a wide range of decentralized tools is simple and secure. Every part of your domain operates on Gaia’s decentralized node network, protecting your work from centralized servers or closed AI systems. Thankfully, getting started is quick. Domains are ready out of the box with open-source language models and SDKs, so you can launch and iterate fast. Their modular design makes them easy to adapt, and EVM compatibility means they fit right into the broader DeAI ecosystem. Earning is built in at every step: you can run services, sell access, or enable delegated tasks with all transactions managed on-chain for total transparency. Gaia Domains open new ground for developers, DAOs, founders, researchers, and enterprises seeking to decentralize their AI stack. Questions? Reach out to our DevRels Tobiloba 🦀 Harish Kotra 🥑 meowy🦀! Get started here: In this video, Tobiloba 🦀 demonstrates how to integrate Gaia's public domains for AI agents, applications, chatbots, and frameworks, or even interact directly via the Gaia chat UI 🔽

Gaia 🌱

15,829 views • 1 year ago

ELON: THINK OF TESLA AS THE WORLD’S LARGEST SEMI-SENTIENT ROBOT COMPANY “With Tesla Autopilot or full self-driving, we're effectively creating the most advanced practical AI for navigating the real world. You can always think of Tesla as the world's biggest robot company or semi-sentient robot company. The car is kind of a robot on four wheels. So then, well, you know, we could probably take that same technology and put it in a humanoid robot and have that robot be useful.” Source: Interview with the Wall Street Journal, December 2021

ELON: THINK OF TESLA AS THE WORLD’S LARGEST SEMI-SENTIENT ROBOT COMPANY “With Tesla Autopilot or full self-driving, we're effectively creating the most advanced practical AI for navigating the real world. You can always think of Tesla as the world's biggest robot company or semi-sentient robot company. The car is kind of a robot on four wheels. So then, well, you know, we could probably take that same technology and put it in a humanoid robot and have that robot be useful.” Source: Interview with the Wall Street Journal, December 2021

Mario Nawfal

57,126 views • 1 year ago

Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.

Presenting: $PALM AI Applications System Hardware 📹 Watch the Reveal Teaser from below ✅ The first Web3 related microcontroller-based device that combines offline voice recognition with in-built AI profiles, performing on electronic circuits straight from the breadboard it was incepted from. 1️⃣ The next steps are to document the use cases of our system, which can be integrated into existing devices and physical services in real life. We will be sure to document and publish processes as the device goes into factory production all the way to shipping them out and showcasing the real-world adoption of our products. 2️⃣ Our hardware is built for flexibility and to innovate on, and we will be working with the top experts in the field to make the best out of it and is built to allow the device structure to be embedded as the smart component of any and all devices. ⌛️Stay tuned for the first use-case reveal of our AI Applications System inside an AI-enhanced, by default offline hardware wallet, fully built from scratch by our talented team. 🙏 Finally, thank you for the continued support. We will keep working as hard as ever. Enjoy the reveal.

PaLM AI - $PALM

166,276 views • 2 years ago

The QVAC SDK is the "LEGO block" of the next era of computing. It’s a modular, local-first framework designed to turn anything—from a simple robot to an industrial server—into a sovereign, autonomous mind. Why build with QVAC? Atomic Intelligence: AI as a raw material embedded directly into your hardware. No Cloud Dependency: 0 latency and total privacy. If the internet breaks, your world keeps thinking. Infinite Scale: A single API for local AI that runs on any device, anywhere. From a child’s toy to the fabric of the universe, if you can dream it, you can build it. Start building the future: 🚀

The QVAC SDK is the "LEGO block" of the next era of computing. It’s a modular, local-first framework designed to turn anything—from a simple robot to an industrial server—into a sovereign, autonomous mind. Why build with QVAC? Atomic Intelligence: AI as a raw material embedded directly into your hardware. No Cloud Dependency: 0 latency and total privacy. If the internet breaks, your world keeps thinking. Infinite Scale: A single API for local AI that runs on any device, anywhere. From a child’s toy to the fabric of the universe, if you can dream it, you can build it. Start building the future: 🚀

QVAC

4,707,146 views • 3 months ago

I made a little cartoon animation to visualise Minswap V2 swaps using the library I have been building. Each delivery truck represents a block containing Minswap buy or sell swaps, with the size of the truck reflecting the amount of swap activity inside that block. The data comes from OgmiosDotnet.BlockchainEvents, a rule based transaction filtering and event emission engine I have been working on for Cardano. It processes live Cardano blocks through Ogmios, applies pluggable transaction rules, then emits a standardised event schema that downstream systems can consume. The broader idea is to make Cardano blockchain events much easier to integrate into existing enterprise architecture. Applications can define the transactions they care about, turn them into structured events, and feed those events directly into infrastructure across GCP, Azure, AWS and other systems. Minswap swaps are just one example of what can be extracted and visualised. There is still plenty I want to build, but I thought this was a cool basic way of showing what is happening inside each block in real time, whilst thinking outside of the box. Hope you like it :)

I made a little cartoon animation to visualise Minswap V2 swaps using the library I have been building. Each delivery truck represents a block containing Minswap buy or sell swaps, with the size of the truck reflecting the amount of swap activity inside that block. The data comes from OgmiosDotnet.BlockchainEvents, a rule based transaction filtering and event emission engine I have been working on for Cardano. It processes live Cardano blocks through Ogmios, applies pluggable transaction rules, then emits a standardised event schema that downstream systems can consume. The broader idea is to make Cardano blockchain events much easier to integrate into existing enterprise architecture. Applications can define the transactions they care about, turn them into structured events, and feed those events directly into infrastructure across GCP, Azure, AWS and other systems. Minswap swaps are just one example of what can be extracted and visualised. There is still plenty I want to build, but I thought this was a cool basic way of showing what is happening inside each block in real time, whilst thinking outside of the box. Hope you like it :)

Dave

32,716 views • 9 days ago

Introducing Jessie OS — the first AI Operating System engineered for real-world autonomy. 💚 Built on Solana, Jessie OS is not a simple assistant or interface. It’s a self-evolving layer of cognition that perceives your environment, understands your behavior, and executes with intelligent precision — without waiting for commands or relying on prompts.🧠⚡️ Context-Aware Intelligence: Your habits, energy, and intent are continuously processed by autonomous agents — translating daily patterns into structured, real-time action. Jessie OS doesn’t need your input — it learns from your output. Agentic Infrastructure: Jessie OS powers a modular ecosystem where anyone can deploy, use, and monetize autonomous AI agents across real-world and digital applications: 🏪 AI-Agent Marketplace 📲 Deployment Tools 🧠 Composable Architecture All fees for deploying AI Agents and launching ecosystem tokens through the AI-Agent Marketplace will be paid in $JESSIE. 🔥 These fees will be automatically burned via smart contracts — ensuring a deflationary supply. 🪙 Marketplace usage, upgrades, and premium agent modules will also require $JESSIE. ⛓️All activity is fully on-chain — transparent, verifiable, and trustless. 🧑‍🚀 Built for Visionaries: For creators, architects, engineers, and futurists — those who see autonomy not as control, but as trust, context, and adaptive intelligence.

Introducing Jessie OS — the first AI Operating System engineered for real-world autonomy. 💚 Built on Solana, Jessie OS is not a simple assistant or interface. It’s a self-evolving layer of cognition that perceives your environment, understands your behavior, and executes with intelligent precision — without waiting for commands or relying on prompts.🧠⚡️ Context-Aware Intelligence: Your habits, energy, and intent are continuously processed by autonomous agents — translating daily patterns into structured, real-time action. Jessie OS doesn’t need your input — it learns from your output. Agentic Infrastructure: Jessie OS powers a modular ecosystem where anyone can deploy, use, and monetize autonomous AI agents across real-world and digital applications: 🏪 AI-Agent Marketplace 📲 Deployment Tools 🧠 Composable Architecture All fees for deploying AI Agents and launching ecosystem tokens through the AI-Agent Marketplace will be paid in $JESSIE. 🔥 These fees will be automatically burned via smart contracts — ensuring a deflationary supply. 🪙 Marketplace usage, upgrades, and premium agent modules will also require $JESSIE. ⛓️All activity is fully on-chain — transparent, verifiable, and trustless. 🧑‍🚀 Built for Visionaries: For creators, architects, engineers, and futurists — those who see autonomy not as control, but as trust, context, and adaptive intelligence.

Jessie

293,818 views • 1 year ago

SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

SkildAI, which is developing a foundational AI model for robots, has released a new video of their humanoid robot walking over objects and exploring environments it had never seen before. The robot is using vision & adapts in real time. SkildAI: "From raw images and joint feedback, the model directly outputs low-level motor commands. This single neural network enables humanoid robots to seamlessly walk across flat ground, climb stairs, and step over obstacles without any planning, mapping, or manual switching between behaviors. For all of our testing in this video, we deployed the robot in each new environment without any prior planning or mapping. We took off the humanoid’s shoes so you can hear it.

Sawyer Merritt

69,381 views • 1 year ago