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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...

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Qace Dynamics

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29,448 görüntüleme • 10 ay önce •via X (Twitter)

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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.
0:58

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 görüntüleme • 8 ay önce

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
0:32

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 görüntüleme • 8 ay önce

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.
2:07

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 görüntüleme • 10 ay önce

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 scalable
1:01

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 scalable

Qace Dynamics

36,922 görüntüleme • 10 ay önce

Tom Hanks: "Eventually the World will have to many people in it" This is what the 'Global Elites' all believe - now they no, we are moving into an age, where Humans are expendable and can simply be replaced by robots and AI.
0:58

Tom Hanks: "Eventually the World will have to many people in it" This is what the 'Global Elites' all believe - now they no, we are moving into an age, where Humans are expendable and can simply be replaced by robots and AI.

Concerned Citizen

12,548 görüntüleme • 5 ay önce

The First AI-Generated Video That Looks Super Real Microsoft Research announced VASA-1. It takes a single portrait photo and speech audio and produces a hyper-realistic talking face video with precise lip-audio sync, lifelike facial behavior, and naturalistic head movements generated in real-time. This is amazing, given that the AI-generated video looks very real! Of course, these examples will likely be cherry-picked, but this is still amazing. My favorite use-case for this tech is to revive old actors like Cary Grant in new movies with this tech :)
0:57

The First AI-Generated Video That Looks Super Real Microsoft Research announced VASA-1. It takes a single portrait photo and speech audio and produces a hyper-realistic talking face video with precise lip-audio sync, lifelike facial behavior, and naturalistic head movements generated in real-time. This is amazing, given that the AI-generated video looks very real! Of course, these examples will likely be cherry-picked, but this is still amazing. My favorite use-case for this tech is to revive old actors like Cary Grant in new movies with this tech :)

Bindu Reddy

2,875,146 görüntüleme • 2 yıl önce

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.
0:45

Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is the important part: You can now build an interface for your Python code without learning HTML, JavaScript, or CSS. Go to this link to try this: Remember to work smarter, not harder! Thanks to the team behind Taipy for partnering with me on this post.

Santiago

155,179 görüntüleme • 2 yıl önce

Australia needs to take advantage of AI before it takes advantage of us. This is quite the show. The progress in AI technology as demonstrated by these robots is both impressive and scary. Impressive as to how far AI technology has come, and scary in the sense of the potential job losses AI will bring. Australia really needs to get up to speed when it comes to manufacturing and technology skills. The sooner we up skill our workforce to ensure we are not beholden to foreign controlled AI the better.
1:16

Australia needs to take advantage of AI before it takes advantage of us. This is quite the show. The progress in AI technology as demonstrated by these robots is both impressive and scary. Impressive as to how far AI technology has come, and scary in the sense of the potential job losses AI will bring. Australia really needs to get up to speed when it comes to manufacturing and technology skills. The sooner we up skill our workforce to ensure we are not beholden to foreign controlled AI the better.

Gerard Rennick

12,663 görüntüleme • 5 ay önce

🚨 AI just crossed into reality. A single human eye reflects an entire AI-generated world — futuristic cities, robots, and digital landscapes forming in real time. We are no longer imagining the future. We are actively creating it. Made with AI video generation (Kling model) on Yapper “This is not the future. It’s now.”
0:53

🚨 AI just crossed into reality. A single human eye reflects an entire AI-generated world — futuristic cities, robots, and digital landscapes forming in real time. We are no longer imagining the future. We are actively creating it. Made with AI video generation (Kling model) on Yapper “This is not the future. It’s now.”

Mimu | AI Tools & News

72,897 görüntüleme • 3 ay önce

Still believe AI isn’t dangerous? It’s just a machine that can perform some parlor tricks? This seems pretty dang real to me and yet right now we know that it is AI. But very soon, none of us will. The tech billionaires know this and are leaning into it. It’s about control.
0:54

Still believe AI isn’t dangerous? It’s just a machine that can perform some parlor tricks? This seems pretty dang real to me and yet right now we know that it is AI. But very soon, none of us will. The tech billionaires know this and are leaning into it. It’s about control.

MAGA Cult Slayer

1,941,486 görüntüleme • 6 ay önce

Watching Generalist AI demonstrate real-time intuition is incredible! 🤖✨ Check out this "Physical Commonsense" in action: the robot flips a washer, picks it up and then double-nudges it perfectly into a tight foam slot. This isn't a scripted motion; it’s an emergent behavior from a model trained on 270,000+ hours of real-world interaction. We are seeing the era where robots are starting to intuit and react to physics.
0:32

Watching Generalist AI demonstrate real-time intuition is incredible! 🤖✨ Check out this "Physical Commonsense" in action: the robot flips a washer, picks it up and then double-nudges it perfectly into a tight foam slot. This isn't a scripted motion; it’s an emergent behavior from a model trained on 270,000+ hours of real-world interaction. We are seeing the era where robots are starting to intuit and react to physics.

Humanoids daily

14,762 görüntüleme • 6 ay önce

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.
5:26

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 görüntüleme • 6 ay önce

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.
0:38

The teams shipping AI agents right now are bleeding money on the dumbest possible expense: teaching a 400B-parameter model to read a file name. Every time an AI agent needs to "see" something today, it routes an image through a frontier model. OCR, object detection, checking if a button exists on screen. You're paying GPT-4o or Claude pricing for tasks that require perception, not reasoning. One agent workflow processing a few thousand screenshots per day can burn through more on vision calls than on the actual thinking. Perceptron's Isaac is 2B parameters. Built by the team that created Meta's Chameleon multimodal models. On perceptive benchmarks, it matches or beats models 50x its size. The VQA, OCR, and object detection scores are competitive with models running on infrastructure that costs orders of magnitude more. The MCP wrapper is the distribution play. One install command and every Claude Code agent can offload vision tasks to a model that runs on a single consumer GPU. The agent keeps its reasoning in the frontier model and routes perception to a specialist. That split is how you get vision-heavy agent workflows from "technically possible but expensive" to "cheap enough to run on everything." This is the same pattern that won in every other compute-intensive stack. General-purpose handles orchestration. Specialists handle the heavy lifting. Graphics went through it. Audio went through it. Video encoding went through it. Vision in AI agents is next. The teams building agents that see 10,000 images a day will care about this before anyone else does.

Aakash Gupta

55,978 görüntüleme • 4 ay önce

✨ To inspire more people to go build something now that we have AI to help us (especially non-tech people, cause I still know so many who are scared of building something): I added a [ BUILD IT ] button to It's like a mini-Lovable/Replit/v0: Any idea you see you like, you can click [ BUILD IT ], and it will use Opus 4.6 to build a landing page for it And then you can download the code it generated It's not a full startup of course, but a nice preview of what it can be, to give you an idea and inspire you to build it out further The code is live streamed also so you can see it being built 😊 Ironically this itself took me 1 hour to build with AI too Completely free and I pay for the tokens (please don't abuse it :D)
0:55

✨ To inspire more people to go build something now that we have AI to help us (especially non-tech people, cause I still know so many who are scared of building something): I added a [ BUILD IT ] button to It's like a mini-Lovable/Replit/v0: Any idea you see you like, you can click [ BUILD IT ], and it will use Opus 4.6 to build a landing page for it And then you can download the code it generated It's not a full startup of course, but a nice preview of what it can be, to give you an idea and inspire you to build it out further The code is live streamed also so you can see it being built 😊 Ironically this itself took me 1 hour to build with AI too Completely free and I pay for the tokens (please don't abuse it :D)

@levelsio

276,957 görüntüleme • 4 ay önce

Introducing $KLED: the first data marketplace. Thousands of users are already selling their content. Today, we’re extremely excited to launch our Enterprise Portal: AI labs, data companies, and Fortune 500s can now search, preview, and license datasets instantly. Making Kled the only two sided data marketplace on the planet. Our vision is now a reality. Full demo and signup link will be live on our completely revamped site in the next 48-72 hours→
0:50

Introducing $KLED: the first data marketplace. Thousands of users are already selling their content. Today, we’re extremely excited to launch our Enterprise Portal: AI labs, data companies, and Fortune 500s can now search, preview, and license datasets instantly. Making Kled the only two sided data marketplace on the planet. Our vision is now a reality. Full demo and signup link will be live on our completely revamped site in the next 48-72 hours→

Kled AI

1,007,400 görüntüleme • 1 yıl önce

Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.
14:24

Can United States manufacture robots? Matic Robots says "yes." It makes the best floor cleaning robot, that has won many perfect scores from Wired to many others. We love ours. But my trip there to get a tour from AI pioneer Navneet Dalal Navneet Dalal provided some real insights into how hard it is for a hardware company to make hardware in the United States. And how deeply AI is changing consumer electronics products that are going to be in many more homes soon. In this first part (Part II coming tomorrow) we get a look at how long it took for this company to go through prototypes to a shipping product. In the second part, you'll see the scaling hell that it takes to even ship a few thousand robots and the kinds of problems that scaling up a factory brings. Matic is one of my favorite small Silicon Valley companies. It has found what we call "product market fit." I just came back from CES where I saw many of its competitors, and the Matic wins because of not just the product thinking of Mehul and Navneet Dalal but because of their AI leadership. In a way their robot took many lessons from Tesla, from where to put the batteries to its bet on computer vision, which Navneet has been a pioneer in for years, working quietly behind the scenes. It is about to move into a new location that will allow it to grow to meet the demand that now is showing up (the boxes in its lobby show that it's outgrowing its current facilities). In terms of AI, it has aspirations of making a humanoid too, but it is taking a far more measured approach to getting there. By starting on the floor it can not just build world models based on real world data (customers are given a choice whether to allow its data to be used that way. Most customers choose to keep their data on the robot only, for privacy reasons, but if you opt in you can help them improve their models). They are using that data to understand homes. Navneet told me they hit very unusual situations in people's homes already that they couldn't really predict in simulators, like full-wall mirrors that confuse computer vision systems, or pools and water features in people's homes. Having real customers brings a ton of customer feedback about how to further improve the robot, and, as Navneet demonstrates in the second video, forces them to build a manufacturing muscle memory. Getting teams to work together, figuring out how to solve supply chain problems, from Trump's tarriffs, to a new one that showed up over the past couple of weeks. A supplier for its bags (one of the cheaper parts that goes into the robot) changed the glue it used, which caused robots to fail quality tests and the manufacturing line to stop. Reminds me a lot of the hell Elon Musk faced in its Fremont factory when Tesla was first starting to manufacture its Model 3, which almost bankrupted the company. Off the record Mehul and Navneet 🇮🇳 showed me some of the prototypes and plans for its next products that will show up over the next few years. Certainly not as sexy as Tesla, Figure, 1x_tech, and all the Chinese manufacturers are showing off already, but far better thought out for the typical Western home and AI plays a huge role in its future. It is the product that speaks for itself. It's amazing, and is about to get better this year due to AI. It's the first real vision-only robot to be in my home and I bet it won't be the last from this company. Real honor that they invited me over with my Insta360 camera (another company launched in my home, just like Matic was last year). In Part II we go into the factory.

Robert Scoble

69,229 görüntüleme • 6 ay önce

Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.
0:50

Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.

Action Model

22,174 görüntüleme • 2 ay önce