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honestly no surprise why Silicon Valley is so obsessed with Matic robots right now > basically a Roomba on steroids > vacuums first, then mops > uses five cameras to build a live 3D map of your home > recognizes rugs, wires, furniture, pets, and people, then changes how...

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Ole Lehmann

158,569 subscribers

45,092 次观看 • 1 个月前 •via X (Twitter)

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You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots
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You point at the mess and say "clean this." That is the whole manual. Matic just shipped this. No app, no map to draw, no menu. The robot listens in more than 70 languages, watches where your hand points, and drives to that spot. Say "follow me" and it trails you into the next room. All of it runs on the robot itself, so it works with the WiFi off. Nine years of work for one thing: a machine you talk to like a dog that actually listens. Matic Robots

Chubby♨️

495,322 次观看 • 1 个月前

WIRED magazine just gave a 10/10 rating to a piece of hardware for the first time in a decade. It is a robot vacuum. Meet Matic. You do not use an app to control it. You literally treat it like a pet. You can point at a coffee spill and say "Hey Matic, clean this." You can even tell it to "follow me" and it walks behind you. It spent 9 years in development to solve the biggest smart home problem: privacy. It has 5 cameras, but it processes everything locally on an Nvidia chip. Your data is deleted in real-time. Hardware is finally getting fun again.
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WIRED magazine just gave a 10/10 rating to a piece of hardware for the first time in a decade. It is a robot vacuum. Meet Matic. You do not use an app to control it. You literally treat it like a pet. You can point at a coffee spill and say "Hey Matic, clean this." You can even tell it to "follow me" and it walks behind you. It spent 9 years in development to solve the biggest smart home problem: privacy. It has 5 cameras, but it processes everything locally on an Nvidia chip. Your data is deleted in real-time. Hardware is finally getting fun again.

shirish

2,012,176 次观看 • 1 个月前

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?
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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

Imagine you go to a store and you want to buy candy. The shopkeeper knows you're a real kid because they can see you standing right there. Now imagine you send a robot to buy candy for you. The shopkeeper looks at the robot and thinks: wait, who sent this? Is this robot allowed to buy candy? What if someone else's robot pretends to be yours and steals your candy money? That's basically what's happening with AI right now. Companies like Visa let people buy things all over the world. But now, smart computer robots (AI agents) want to buy things too. Shop around, compare prices, even pay for stuff. Visa looked at this and said: nope, not yet. Because they have no way to check if the robot is real, who it belongs to, or if it's allowed to spend that money. The problem is that all the rules we have for checking identity - showing your ID, scanning your face, typing your password - only work for humans. Robots can't do any of that. Worse, bad robots can actually copy and fake human identities really well. So Evin McMullen evin, Billions Network co-founder and CEO, says we need a new kind of ID system. One where you can prove something is true without showing all your private stuff. Like proving you're tall enough for a ride without telling anyone your exact height. That's called zero-knowledge proof. And for the robots specifically, we need something called KYA - Know Your Agent. It's like giving every robot its own ID card that says: this is who I am, this is what I'm allowed to do, and this is the human responsible for me. Until we build that, the robot economy can't really get going. Here is Evin’s Thought Leader article at Silicon Valleys Journal
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Imagine you go to a store and you want to buy candy. The shopkeeper knows you're a real kid because they can see you standing right there. Now imagine you send a robot to buy candy for you. The shopkeeper looks at the robot and thinks: wait, who sent this? Is this robot allowed to buy candy? What if someone else's robot pretends to be yours and steals your candy money? That's basically what's happening with AI right now. Companies like Visa let people buy things all over the world. But now, smart computer robots (AI agents) want to buy things too. Shop around, compare prices, even pay for stuff. Visa looked at this and said: nope, not yet. Because they have no way to check if the robot is real, who it belongs to, or if it's allowed to spend that money. The problem is that all the rules we have for checking identity - showing your ID, scanning your face, typing your password - only work for humans. Robots can't do any of that. Worse, bad robots can actually copy and fake human identities really well. So Evin McMullen evin, Billions Network co-founder and CEO, says we need a new kind of ID system. One where you can prove something is true without showing all your private stuff. Like proving you're tall enough for a ride without telling anyone your exact height. That's called zero-knowledge proof. And for the robots specifically, we need something called KYA - Know Your Agent. It's like giving every robot its own ID card that says: this is who I am, this is what I'm allowed to do, and this is the human responsible for me. Until we build that, the robot economy can't really get going. Here is Evin’s Thought Leader article at Silicon Valleys Journal

Billions Network

21,775 次观看 • 8 个月前

What does a real humanoid robotics dataset actually look like? Humanoid robots are expensive and complex, which makes their data unusually difficult to collect - and difficult for most people to get hands-on experience with. We previously shared a post on conversion code for the new Human in the Wild dataset by BitRobot 🦾, Hugging Face, and Unitree. Now we’re publishing the full HIW-500 data converted to RRD, so you can explore that data directly with Rerun. Watch the robot move. Inspect cameras and robot states together. Zoom in on a moment and query episodes through Rerun catalog! If you’re curious about a real-world humanoid dataset, HIW-500 is now a lot easier to explore. We share the conversion code along with our previous abc-130k dataset conversion example.
0:16

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

Rerun

11,355 次观看 • 1 个月前

JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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JUST IN: Reimagine Robotics has just emerged from stealth! 🥷🏻 Its approach to robot training is one of the most human-centric I've seen. The founder is Jonathan Scholz, the person who built and led Google DeepMind's Applied Robotics team in London for seven years. This is not a first-time founder taking a swing at robotics. He has spent a decade at the frontier of the field. The philosophy is powerful. He calls it "monkey-see, monkey-do." 🐒 A worker shows the robot what to do. Watches it attempt the task. Corrects it on the spot. The robot learns. No specialist programmers. No months of integration. And it's already working in the real world: → A made-to-order plastics business trained robots to tend 3D printers overnight, removing print beds, operating latches, pressing controls → A hard drive disassembly facility built a three-robot cell combining robots and people to recover critical materials → Time to prototype and test a new robot behaviour reduced from one day to 10 MINUTES That last number is the one that changes everything. When testing a new behaviour takes 10 minutes instead of a day, the entire pace of deployment transforms. Scholz's framing of the human-robot relationship is worth reading carefully: "A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful." It's August, and we keep getting robotics bangers week in week. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

23,404 次观看 • 2 个月前

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:
0:14

A simple MPC controller for path tracking implemented in python. Few resources actually teach it without assuming a PhD first. This open-source project does exactly that: An iterative MPC tracking controller (built with CVXPY and MuJoCo) that shows how to make a robot (or autonomous vehicle) follow a path and avoid obstacles in real time. → Autonomous logistics, robot navigation, vehicle control; there's a path-following algorithm behind all of it. → Most implementations are black boxes. This one shows you what's actually happening. → It uses convex optimization (no non-linear solver), and still handles real vehicle physics through iterative linearization. What makes it practical is that you can run it with the mushr car model in MuJoCo, or headless without any physics simulation if you just want to understand the logic. If you're making the jump from classical control to real-time optimisation, this is a very good starting point. 📌 Repository: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

16,064 次观看 • 1 个月前

There's an open-source home robot vacuum you can actually build yourself. It's called OOMWOO. Runs on a Raspberry Pi with 2D LiDAR mapping, ROS2 navigation, and native Home Assistant integration. No cloud dependency, no vendor lock-in. The team spent real time researching before designing it. They reviewed the entire 2025-2026 robot vacuum market, from budget to flagship, and found real-world cleaning doesn't track advertised suction power. The anti-tangle brush design alone is worth mentioning. A tapered rubber roller that resists hair-wrap, one of the most common complaints with commercial models, easy to 3D-print yourself. If you've got a Raspberry Pi and a 3D printer sitting around, this is a genuinely interesting weekend project.
0:15

There's an open-source home robot vacuum you can actually build yourself. It's called OOMWOO. Runs on a Raspberry Pi with 2D LiDAR mapping, ROS2 navigation, and native Home Assistant integration. No cloud dependency, no vendor lock-in. The team spent real time researching before designing it. They reviewed the entire 2025-2026 robot vacuum market, from budget to flagship, and found real-world cleaning doesn't track advertised suction power. The anti-tangle brush design alone is worth mentioning. A tapered rubber roller that resists hair-wrap, one of the most common complaints with commercial models, easy to 3D-print yourself. If you've got a Raspberry Pi and a 3D printer sitting around, this is a genuinely interesting weekend project.

Oliver Prompts

587,420 次观看 • 2 个月前

Peer-to-Peer RocketChats were already a hit. Then RocketChat TTS turned the room all the way up. 🚀🔊 And the room shown inside the phone? That is not a mockup. It is a real Chatter room from yesterday, filled with real people, real conversation, and a community showing up for each other. Send RocketChats directly to people in the room, hear them come alive through TTS, and keep the energy moving in real time. Get your community on Chatter now. Update the app, start a room, and bring your people with you. 🚀
0:16

Peer-to-Peer RocketChats were already a hit. Then RocketChat TTS turned the room all the way up. 🚀🔊 And the room shown inside the phone? That is not a mockup. It is a real Chatter room from yesterday, filled with real people, real conversation, and a community showing up for each other. Send RocketChats directly to people in the room, hear them come alive through TTS, and keep the energy moving in real time. Get your community on Chatter now. Update the app, start a room, and bring your people with you. 🚀

Chatter

12,112 次观看 • 2 个月前

The Prime Minister and the Home Minister speak endlessly about Nari Shakti. They posture on women’s empowerment and perform outrage for the cameras. But their conduct tells a different story. The Prime Minister publicly mocks a three-time elected woman Chief Minister with “Didi O Didi,” delivered with visible condescension. The Home Minister echoes it with “Ei Didi,” reducing a respected leader to a taunt. This is the “Poriborton” the BJP seeks to import into Bengal from Uttar Pradesh and Bihar. It is not change, it is a regression in values. An insult to Bengal. An insult to every woman in this state and across the country. On 4th May, the people of Bengal will deliver a verdict that cuts through this hypocrisy once and for all. And that response will be decisive enough to remind these ZAMINDARS that respect for women is not a slogan, it is a standard!
0:10

The Prime Minister and the Home Minister speak endlessly about Nari Shakti. They posture on women’s empowerment and perform outrage for the cameras. But their conduct tells a different story. The Prime Minister publicly mocks a three-time elected woman Chief Minister with “Didi O Didi,” delivered with visible condescension. The Home Minister echoes it with “Ei Didi,” reducing a respected leader to a taunt. This is the “Poriborton” the BJP seeks to import into Bengal from Uttar Pradesh and Bihar. It is not change, it is a regression in values. An insult to Bengal. An insult to every woman in this state and across the country. On 4th May, the people of Bengal will deliver a verdict that cuts through this hypocrisy once and for all. And that response will be decisive enough to remind these ZAMINDARS that respect for women is not a slogan, it is a standard!

Abhishek Banerjee

1,677,811 次观看 • 5 个月前

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.
0:12

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

Rina

45,651 次观看 • 2 个月前

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:
0:14

A Gaussian Splat can become a world where Robots and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:

Over the Reality 🌐

15,316 次观看 • 1 个月前

THE FIRST GENERATION TO GROW UP WITH HUMANOID ROBOTS IS ALREADY HERE. And that could matter more than any robot demo. Look at this scene. A humanoid standing outside a school. Children walking around it. Watching it. Interacting with it. For adults, a machine like this still looks futuristic. For a child growing up around them? It could eventually look completely normal. Think about how quickly that changes a market. A generation ago, having a computer inside your home was unusual. Then children grew up with computers. The same happened with smartphones. Then tablets. Then AI. Technology stops feeling revolutionary when an entire generation has never known a world without it. Humanoids could be next. Imagine children growing up seeing robots in schools. Stores. Airports. Hospitals. Hotels. Homes. They won’t ask whether humans should interact with robots. They’ll already be doing it. And that matters economically. Because one of the biggest barriers to consumer robotics isn’t just price. It’s trust. People need to feel comfortable allowing an autonomous machine into their personal space. Inside their homes. Around their families. Around their children. That kind of acceptance can take years. Sometimes generations. But children who grow up around humanoids won’t need the same adjustment. To them, asking a robot to carry something might feel as normal as asking Alexa a question feels today. And once that happens, the addressable market changes dramatically. A robot isn’t just industrial equipment anymore. It becomes a household product. A service platform. An assistant. Potentially even something families upgrade every few years. First robot at school. First robot at the mall. First robot at work. Eventually… first robot at home. The companies building humanoids today aren’t only competing for today’s customers. They may be competing to become familiar to an entire generation of future customers. Because the biggest sign that humanoid robots have won won’t be people staring at them. It’ll be children walking past one without thinking twice.
0:10

THE FIRST GENERATION TO GROW UP WITH HUMANOID ROBOTS IS ALREADY HERE. And that could matter more than any robot demo. Look at this scene. A humanoid standing outside a school. Children walking around it. Watching it. Interacting with it. For adults, a machine like this still looks futuristic. For a child growing up around them? It could eventually look completely normal. Think about how quickly that changes a market. A generation ago, having a computer inside your home was unusual. Then children grew up with computers. The same happened with smartphones. Then tablets. Then AI. Technology stops feeling revolutionary when an entire generation has never known a world without it. Humanoids could be next. Imagine children growing up seeing robots in schools. Stores. Airports. Hospitals. Hotels. Homes. They won’t ask whether humans should interact with robots. They’ll already be doing it. And that matters economically. Because one of the biggest barriers to consumer robotics isn’t just price. It’s trust. People need to feel comfortable allowing an autonomous machine into their personal space. Inside their homes. Around their families. Around their children. That kind of acceptance can take years. Sometimes generations. But children who grow up around humanoids won’t need the same adjustment. To them, asking a robot to carry something might feel as normal as asking Alexa a question feels today. And once that happens, the addressable market changes dramatically. A robot isn’t just industrial equipment anymore. It becomes a household product. A service platform. An assistant. Potentially even something families upgrade every few years. First robot at school. First robot at the mall. First robot at work. Eventually… first robot at home. The companies building humanoids today aren’t only competing for today’s customers. They may be competing to become familiar to an entire generation of future customers. Because the biggest sign that humanoid robots have won won’t be people staring at them. It’ll be children walking past one without thinking twice.

Mag1strY0da

10,149 次观看 • 13 天前

I was working through Steam Early Access requirements for VRUnderground and I had a really spooky realization. The whole conversation around training images and videos is not the important part. The media itself is just a bridge. What is actually being extracted is cognitive patterning. Models learn how people think, not just how they draw. The images are only the residue of the process. The real map is of the minds that created them. It reminds me of poker. A great poker player is not "stealing your cards". They are reading the patterns in your behavior and mapping your decision space. In that sense, training AI on human-created data is not about automating tasks. It is about mapping cognition through metadata and behavior trails. If that is true, then the internet starts looking a lot more like a poker table. And nobody wins a hand against someone who can read their mind.
0:13

I was working through Steam Early Access requirements for VRUnderground and I had a really spooky realization. The whole conversation around training images and videos is not the important part. The media itself is just a bridge. What is actually being extracted is cognitive patterning. Models learn how people think, not just how they draw. The images are only the residue of the process. The real map is of the minds that created them. It reminds me of poker. A great poker player is not "stealing your cards". They are reading the patterns in your behavior and mapping your decision space. In that sense, training AI on human-created data is not about automating tasks. It is about mapping cognition through metadata and behavior trails. If that is true, then the internet starts looking a lot more like a poker table. And nobody wins a hand against someone who can read their mind.

VRUnderground

26,622 次观看 • 10 个月前

MEDICAL ROBOT RUNS FROM POLICE RIGHT IN THE CITY CENTER Robots are built to follow commands. This one turned and ran on its own. This isn't staged. This isn't a demo. Cameras caught it live. A police car and an ambulance were already on scene. The robot in a blue medical gown bolted through the pedestrian zone, weaving straight through café tables. It dodged between chairs. It moved around people without slowing down. It never lost its balance. Officers on foot couldn't keep pace. Step speed, balance, obstacle response, all running in real time. Nobody scripted this scenario in advance. Autonomy isn't a glitch in the code. It's the basic wiring of a body that finally learned to move on its own.
0:15

MEDICAL ROBOT RUNS FROM POLICE RIGHT IN THE CITY CENTER Robots are built to follow commands. This one turned and ran on its own. This isn't staged. This isn't a demo. Cameras caught it live. A police car and an ambulance were already on scene. The robot in a blue medical gown bolted through the pedestrian zone, weaving straight through café tables. It dodged between chairs. It moved around people without slowing down. It never lost its balance. Officers on foot couldn't keep pace. Step speed, balance, obstacle response, all running in real time. Nobody scripted this scenario in advance. Autonomy isn't a glitch in the code. It's the basic wiring of a body that finally learned to move on its own.

VynqorxeAI

5,699,782 次观看 • 28 天前

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓
0:12

HOLY SH*T, A DEAD FLY’S BRAIN MAP IS NOW MAKING A ROBOT MOVE. not a brain sitting inside a jar. not an AI trained to imitate how a fly behaves. the actual wiring reconstructed from its nervous system. scientists mapped: 166,700 neurons roughly 125 million synapses the circuits behind vision, motion and behavior AI helped reconstruct the connections. developers then turned the map into a control loop: camera sees the environment → fly-derived visual circuits process the input → modeled neurons produce activity → activity becomes motor commands → the robot moves the body is synthetic. the control logic begins with biological wiring shaped by millions of years of evolution. this does not mean the fly was resurrected. the robot isn’t conscious. it doesn’t remember being alive. it doesn’t understand where it is going. but the solution its nervous system used to see, react and move can now operate inside a completely different body. this is bigger than one robot. if biological circuits can become inspectable controllers, engineers may not need to invent every intelligent behavior from zero. they can study solutions evolution already built. the fly is dead. its wiring is still making something move. I broke down how 166,700 neurons escaped the brain map and entered a machine below ↓

kozh ./

37,886 次观看 • 13 天前

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.
0:10

Running cold email campaigns just became a whole lot easier Smartlead now runs an MCP server, which in plain terms means Claude can read and act on your live campaign data directly instead of working off a spreadsheet that went stale the moment you exported it. The workflow is worth walking through properly, because it is shorter than people expect. You generate an API key inside your account, point Claude at the server once, and from then on you ask for what you want in a sentence. Here is a prompt worth stealing in full: "Fetch all Smartlead clients, then get today's performance for each: emails sent, replied, positive replies, unique lead count. Compute reply rate per client, run a top and bottom performer analysis, format it as a daily client performance report, and post it to Slack." One paste, and it pulls live figures for every account, does the arithmetic, ranks the strongest and the weakest, and delivers the finished thing into the channel your team already sits in, before anyone has logged on for the day. Be clear about the division of labour, because it is what makes this useful rather than a novelty. Smartlead is the engine holding the campaigns, the mailboxes, the warmup and the reply data, and Claude is simply the interface you operate all of it through, so nothing about your sending changes and everything about how you interrogate it does. The effect people underestimate is on the questions you start asking. Once a report costs you a sentence rather than an afternoon, you stop rationing the ones that used to feel like too much trouble, and problems that used to surface on a Friday start surfacing on a Tuesday. Connect it with Claude through MCP and run one prompt against your own account today.

Tim

21,666 次观看 • 23 天前

The off switch tells you more than the encryption does. Anyone can say "private." The question that matters is who can reverse it and what has to be true first. One company with a master key means you were never private. No way to reverse it at all means the system breaks the first time a court asks. Veilnyx splits the authority. Disclosure runs through threshold cryptography across a decentralized compliance network, so no single party can unmask an account. Your address stays as it is, with a private account layer on top and zero-knowledge proofs verifying every transaction. Next time a protocol says "private", ask who holds the switch. Powered by Palliora
0:15

The off switch tells you more than the encryption does. Anyone can say "private." The question that matters is who can reverse it and what has to be true first. One company with a master key means you were never private. No way to reverse it at all means the system breaks the first time a court asks. Veilnyx splits the authority. Disclosure runs through threshold cryptography across a decentralized compliance network, so no single party can unmask an account. Your address stays as it is, with a private account layer on top and zero-knowledge proofs verifying every transaction. Next time a protocol says "private", ask who holds the switch. Powered by Palliora

Veilnyx

55,949 次观看 • 6 天前

THIS ROBOT IS RUNNING ON THE WIRING OF A DEAD FLY. NOT A BRAIN-INSPIRED AI. THE ACTUAL CIRCUITS BIOLOGY BUILT. scientists reconstructed a fruit fly nervous system containing: 166,700 neurons roughly 125 million synapses the circuits responsible for vision, movement and behavior then developers gave that wiring something it was never designed to control: a completely different body. camera input enters the system sensory neurons react signals travel through the reconstructed neural circuits command neurons produce a movement decision and the machine moves. the robot provides the cameras, motors and metal limbs. the fly provides the biological architecture behind part of its behavior. this is not consciousness uploaded into a machine. the robot doesn’t think it is a fly. it doesn’t remember being alive. and the original animal is still dead. but the neural machinery that once controlled six tiny legs can now send commands into an entirely new body. millions of years of evolution designed the wiring. AI reconstructed it. engineers connected it to metal. the fly never came back to life. its nervous system simply found another body. I broke down how a dead brain map became a robot controller below ↓
0:18

THIS ROBOT IS RUNNING ON THE WIRING OF A DEAD FLY. NOT A BRAIN-INSPIRED AI. THE ACTUAL CIRCUITS BIOLOGY BUILT. scientists reconstructed a fruit fly nervous system containing: 166,700 neurons roughly 125 million synapses the circuits responsible for vision, movement and behavior then developers gave that wiring something it was never designed to control: a completely different body. camera input enters the system sensory neurons react signals travel through the reconstructed neural circuits command neurons produce a movement decision and the machine moves. the robot provides the cameras, motors and metal limbs. the fly provides the biological architecture behind part of its behavior. this is not consciousness uploaded into a machine. the robot doesn’t think it is a fly. it doesn’t remember being alive. and the original animal is still dead. but the neural machinery that once controlled six tiny legs can now send commands into an entirely new body. millions of years of evolution designed the wiring. AI reconstructed it. engineers connected it to metal. the fly never came back to life. its nervous system simply found another body. I broke down how a dead brain map became a robot controller below ↓

kozh ./

83,737 次观看 • 9 天前