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Matic detects wires using neural networks trained on 300,000+ simulated (#AirSim) images and 25,000 real ones, learning different shapes, colors, and lighting. So instead of rescuing a stuck robot, you can just let Matic do its thing! The map shown here is a semantic map, helping Matic understand your... show more

Matic Robots

16,386 subscribers

53,020 次观看 • 1 年前 •via X (Twitter)

科学技术新闻政治 #AirSim

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Matic Robots 的头像

Matic Robots1 年前

These images show wires in varied shapes, colors, textures, and lighting. Different positions and angles help Matic path-plan smoothly.

TG Promo 的头像

TG Promo1 年前

🌍 A world of devastation awaits you in Machine Mind. 💥 Can you master the art of base-building, automation, and survival in a post-apocalyptic wasteland? 🎮 Wishlist it now on Steam and prepare for an adventure like no other:

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Introducing Cues: Voice & Gesture control for Matic We raised $115M and spent 9 years to make the world’s first intuitive home robot Say you spilled coffee: Point to the spill and say, "Hey Matic, clean this" and it will hear, see, locate in 3D, and go clean on its own. Matic comes with a lot of features: 1. Say "Hey Matic" - it locates your voice, turns, and looks at you 2. Say "Hey Matic, follow me" and start walking. Matic will follow behind 3. Say "Hey Matic, go clean the living room". Since it knows your house map, it navigates and just does it It's so easy, a 5 year old and an 80 year old can use it and it understands 75 different languages. Matic has 8x the airflow, specialised cleaning algorithms for rugs, corners, toekicks, mopping, etc and cleans better than any other robot vacuum. Also keeps improving with software updates. 13,000 families use and love Matic. WIRED magazine gave it a 10/10 (the only hardware to receive this rating in a decade) Buy yours at and if you don't love it after 6 months, we'll give you a full refund. To celebrate our launch, we're cleaning 300 homes with Matic in San Francisco and New York City. Comment "Matic" below, we'll send you the link to sign up and come to your doorstep to clean your home with Matic.

Introducing Cues: Voice & Gesture control for Matic We raised $115M and spent 9 years to make the world’s first intuitive home robot Say you spilled coffee: Point to the spill and say, "Hey Matic, clean this" and it will hear, see, locate in 3D, and go clean on its own. Matic comes with a lot of features: 1. Say "Hey Matic" - it locates your voice, turns, and looks at you 2. Say "Hey Matic, follow me" and start walking. Matic will follow behind 3. Say "Hey Matic, go clean the living room". Since it knows your house map, it navigates and just does it It's so easy, a 5 year old and an 80 year old can use it and it understands 75 different languages. Matic has 8x the airflow, specialised cleaning algorithms for rugs, corners, toekicks, mopping, etc and cleans better than any other robot vacuum. Also keeps improving with software updates. 13,000 families use and love Matic. WIRED magazine gave it a 10/10 (the only hardware to receive this rating in a decade) Buy yours at and if you don't love it after 6 months, we'll give you a full refund. To celebrate our launch, we're cleaning 300 homes with Matic in San Francisco and New York City. Comment "Matic" below, we'll send you the link to sign up and come to your doorstep to clean your home with Matic.

Matic Robots

10,837,323 次观看 • 7 天前

Matic Robots' Mehul live demos the Matic Cues on MTS by saying "Hey Matic, clean this" while pointing at the floor, and the robot triangulates his hand, eyes, and position to find the mess: sophia dew: "Now that we just talked about so much of what Matic Cues actually does, I wish we could just see it." Mehul: "Perfect. We've got it right here. Let's actually do it. Nothing like a live demo." "Hey, Matic... And it sort of looks at you." "Clean this." sof 𓋹: "Aw. It's like WALL-E." Mehul: "When you say clean this, it literally triangulates your hand, eyes, and its position. It's a combination of everything together, and we have four microphone arrays which allows it to turn towards the voice." "The idea was that if you call your pet, your friend, anyone inside home, they'll look at you. So why doesn't the robot as well?" Matic Robots

Matic Robots' Mehul live demos the Matic Cues on MTS by saying "Hey Matic, clean this" while pointing at the floor, and the robot triangulates his hand, eyes, and position to find the mess: sophia dew: "Now that we just talked about so much of what Matic Cues actually does, I wish we could just see it." Mehul: "Perfect. We've got it right here. Let's actually do it. Nothing like a live demo." "Hey, Matic... And it sort of looks at you." "Clean this." sof 𓋹: "Aw. It's like WALL-E." Mehul: "When you say clean this, it literally triangulates your hand, eyes, and its position. It's a combination of everything together, and we have four microphone arrays which allows it to turn towards the voice." "The idea was that if you call your pet, your friend, anyone inside home, they'll look at you. So why doesn't the robot as well?" Matic Robots

MTS

23,306 次观看 • 5 天前

🚨🇺🇸FIGURE ROBOT LEARNS TO WALK IN HOURS Turns out you can teach a robot to walk—if you’ve got a few years to burn in a physics simulator (or a few hours of GPU time). Figure’s team trained a neural network using reinforcement learning to mimic natural human locomotion, and the wild part is it worked on the first try with real hardware. No tuning. No fiddling. Just plug and strut. Thanks to sim-to-real transfer and some sneaky domain randomization, the robot didn’t just wobble out of the lab—it walked like it had somewhere to be. Next up: running. Not terrifying at all. Source: Figure

🚨🇺🇸FIGURE ROBOT LEARNS TO WALK IN HOURS Turns out you can teach a robot to walk—if you’ve got a few years to burn in a physics simulator (or a few hours of GPU time). Figure’s team trained a neural network using reinforcement learning to mimic natural human locomotion, and the wild part is it worked on the first try with real hardware. No tuning. No fiddling. Just plug and strut. Thanks to sim-to-real transfer and some sneaky domain randomization, the robot didn’t just wobble out of the lab—it walked like it had somewhere to be. Next up: running. Not terrifying at all. Source: Figure

Mario Nawfal

117,866 次观看 • 1 年前

So Matic Robots offered to send me their new vision-only robot vacuum/mop. I saw the Shopify CEO post about it on X, so I figured, why not. For a brand-new company that just started shipping a few months ago, it's actually a pretty great product. Here's my short review: Things I like: • It's quiet, really quiet. I've been using a Roomba J7+ robot vacuum for a few years and the Matic is MUCH quieter. You can easily have a convo with it on next to you. • Because the robot is vision-only, when it maps your house, you get color data/almost 3D map. • Its wheels are big, which means it can traverse areas much more easily than smaller robots with tiny wheels. • The app is great. Easy to use and intuitive. Much better than the iRobot app. • It handles hair better than my Roomba. Hair always gets wound up on the edge-sweeping brushes on the Roomba. • You can control the robot from your phone like a little remote control car with precise control. • It does a good job of detecting objects like wires • Doesn't constantly bump into things like my Roomba •The unboxing experience is the best of any robot vacuum. They even displayed my and my wife's names on the screen as it rolled out of the box lol. Things I think can improve: • It can sometimes take a little while longer to vacuum than the Roomba, but that's improving over time • It's a tall robot, and while the extended vacuum arm is cool, it can't go under certain furniture or tables that my Roomba can. I think toe kick cleaning is coming soon via software update tho? • The robot mops up backwards. This means some of what you spilled could get on the wheels, which can get messy. Although, some competitors do this too. • This is a pro or con depending on the person: The robot has a HEPA filter bag, as well as the water tank, integrated directly into the robot itself. This means there isn't a large base station it needs to dock at and empty. But this results in you having to throw away more bags than you'd probably like. The bags only cost $2.50 each (vs $6 for Roomba), which is pretty cheap. Ideal solution would be something more reusable. I'd also love to have a live camera feed so I can see what it sees while cleaning. The Robot vacuum/mop costs $1,095. Overall, it's a cool product. Improves on many things that other vacuums don't do well/are annoying. While this is a first-gen product, it's a great first iteration, and it feels pretty polished. Nice work Mehul. Note: Matic didn't pay me. I don't have a referral/affiliate link. They aren't an account sponsor. They just sent me the robot. Ok, back to EV news lol.

Sawyer Merritt

60,188 次观看 • 1 年前

"clean this" while pointing at a spill is the demo. the interesting part is everything that has to happen for it to work. the robot has to locate your arm, project where it's aimed, and match that ray against objects it already holds in memory. speech is the easy half. grounding is the part that kept voice assistants stuck on context free commands for a decade. Matic runs all of it onboard: > 5 RGB-IR cameras, no lidar > an NVIDIA Jetson doing the inference locally > a 3D map of the home that updates while it cleans > raw footage discarded in real time, nothing leaves the house 75 languages, no app, no account. calling this voice control undersells it. it's a machine that can see what you're pointing at.

"clean this" while pointing at a spill is the demo. the interesting part is everything that has to happen for it to work. the robot has to locate your arm, project where it's aimed, and match that ray against objects it already holds in memory. speech is the easy half. grounding is the part that kept voice assistants stuck on context free commands for a decade. Matic runs all of it onboard: > 5 RGB-IR cameras, no lidar > an NVIDIA Jetson doing the inference locally > a 3D map of the home that updates while it cleans > raw footage discarded in real time, nothing leaves the house 75 languages, no app, no account. calling this voice control undersells it. it's a machine that can see what you're pointing at.

Leo

543,717 次观看 • 7 天前

SOMEONE VIBE CODED TWITCH BUT FOR RUNNING, YOU STREAM YOUR RUN LIVE AND PEOPLE WATCH YOU MOVE ACROSS A 3D MAP IN REAL TIME instead of posting your run after its done, people watch it happen. your dot moves along a live 3d map of the city while your stats update as you go. > a live 3d map showing exactly where you are and the route youve covered > your time, pace, distance and heart rate all updating live on screen > a live graph tracking your heart rate, pace and elevation as you run > viewers can chat with you mid run, so people are literally going to tell you to push up the hill while youre running it > the whole thing was vibe coded with claude code the interface looks like a proper broadcast, not a fitness app, which is exactly why it works as something people would actually watch running has always been the loneliest sport but someone just gave it an audience

SOMEONE VIBE CODED TWITCH BUT FOR RUNNING, YOU STREAM YOUR RUN LIVE AND PEOPLE WATCH YOU MOVE ACROSS A 3D MAP IN REAL TIME instead of posting your run after its done, people watch it happen. your dot moves along a live 3d map of the city while your stats update as you go. > a live 3d map showing exactly where you are and the route youve covered > your time, pace, distance and heart rate all updating live on screen > a live graph tracking your heart rate, pace and elevation as you run > viewers can chat with you mid run, so people are literally going to tell you to push up the hill while youre running it > the whole thing was vibe coded with claude code the interface looks like a proper broadcast, not a fitness app, which is exactly why it works as something people would actually watch running has always been the loneliest sport but someone just gave it an audience

Om Patel

400,161 次观看 • 1 个月前

🇹🇬🚨 URGENT: Togo just declared war on the map. The foreign minister is going to the UN to demand a world map that actually shows Africa's true size. No more Mercator projection. No more colonial cartography that makes Europe look massive and Africa look tiny. Africa is bigger than China, India, the US, and most of Europe combined. But on your classroom map, it looks like an afterthought. That is not an accident. That is propaganda. The West will fight this because the map shapes the mind. Make Africa look small, and people treat it as small. Change the map, and you change the story. Togo just called out the lie. The Global South is rewriting the map. Finally.

🇹🇬🚨 URGENT: Togo just declared war on the map. The foreign minister is going to the UN to demand a world map that actually shows Africa's true size. No more Mercator projection. No more colonial cartography that makes Europe look massive and Africa look tiny. Africa is bigger than China, India, the US, and most of Europe combined. But on your classroom map, it looks like an afterthought. That is not an accident. That is propaganda. The West will fight this because the map shapes the mind. Make Africa look small, and people treat it as small. Change the map, and you change the story. Togo just called out the lie. The Global South is rewriting the map. Finally.

New Direction AFRICA

460,155 次观看 • 4 个月前

Here's a copy/paste prompt recipe and vid showing exactly how to ask an LLM for an interactive map with satellite/map layers + a georeferencer that lets you see how old maps correspond with modern geography. Today the computer can’t make good print maps (that's your hill to climb ) but it can, with five bucks and twenty minutes, make good interactive maps. No software/GIS knowledge necessary, you just need a few nouns and an LLM. Scroll to the bottom for the repo/live map if you want those. I'm using Claude Code as an extension in VS Code but you can use the Claude CLI, Cursor, whatever. 1) Let's grab an old cadastral map and see who owned big tracts of a city; I found this an 1854 map of Niagara Falls, NY I found in the Library of Congress: , grabbed the .jp2, saved as a jpg from photoshop. 2) Let's ask Claude Code for a map. You can see exactly what I did in the video but my prompt, sans simple "hey it's busted" debugging, is written out in the following paragraphs. I explain the map-specific nouns in brackets. You can likely dump this whole thing in your LLM window and it'll work; I'd try plan mode + skip permissions. THE PROMPT Make an interactive map with MapLibre GL JS [maplibre is a javascript mapping library, a FOSS version of Mapbox GL JS. This lets us display tiled map data and arbitrary images on the map] Add basemap toggles with Esri satellite, Carto Positron, and OSM [these map layers require no API keys for light usage; Carto Positron is a nice road map layer and OSM is ugly but comprehensive] Add a globe/mercator projection toggle [I think the globe looks better at low zooms] Add a layer panel on the left with visibility checkboxes and delete buttons. Add a search box on the map that flies to results, with deletable pin markers [Makes this easy to get to your area of interest] Include an interactive local georeferencer: drop a JPG, pick ground control points on a zoomable/pannable image viewer, place them on the map, watch it warp with a progress bar centered on the map. [The georeferencer uses math ("affine transform"??) to match points on the old map to points on the new map; generally you click road intersections on the old map, match them on the new map, repeat a dozen times and everything aligns] The georeferenced map overlay defaults to 25% opacity with a slider above the control point list. [I want it easy to see the underlying modern geography] Add Export/import control point buttons [this saves the control points as a JSON so you can save and reimport your work] Add a button to export the warped image as a GeoTIFF with a .prj [In case you want to add the georeferenced image to a real GIS program like QGIS] Look up all relevant docs before starting [Claude sometimes uses outdated stuff] Split everything into separate HTML/CSS/JS files [Claude tends to pile everything in index.html, which is hard to read] Use Optima font, base color #FEFAF6 [I just like this style] Let me test with a local server [it serves it on a simple server so you can nav your host to localhost:8000 and try it out] Log all errors [so you don't have to play telephone with the LLM describing what's busted] 3) Once your LLM finishes, test it out in your browser; if it doesn't work, ask the LLM to check logs. Repeat 'til functional. 4) After this works on your computer, you can show it to everyone by hosting it on GitHub: prompt with "write a README explaining what everything does, add it to a new GitHub repo, deploy using GitHub pages, gimme the live URL" Here's what Claude made for me, try it yourself: • Upload the JPG in the repo, which is linked below • "Add GCP" • Click somewhere recognizable on the old map, like the tip of an island or a road intersection • Click the matching point on the new map • Repeat til you have least 3x points • Hit "georeference" • You'll see the old map atop the new map; if you want a better fit, delete bad points or add a dozen new ones, hit georeference again, repeat Repo: Is this map robust? Human-maintainable? Elegant? Performant? Secure? No, but *your* personal web map need not be. It just needs to work for *your* narrow use case, because it’s *your* map.

Here's a copy/paste prompt recipe and vid showing exactly how to ask an LLM for an interactive map with satellite/map layers + a georeferencer that lets you see how old maps correspond with modern geography. Today the computer can’t make good print maps (that's your hill to climb ) but it can, with five bucks and twenty minutes, make good interactive maps. No software/GIS knowledge necessary, you just need a few nouns and an LLM. Scroll to the bottom for the repo/live map if you want those. I'm using Claude Code as an extension in VS Code but you can use the Claude CLI, Cursor, whatever. 1) Let's grab an old cadastral map and see who owned big tracts of a city; I found this an 1854 map of Niagara Falls, NY I found in the Library of Congress: , grabbed the .jp2, saved as a jpg from photoshop. 2) Let's ask Claude Code for a map. You can see exactly what I did in the video but my prompt, sans simple "hey it's busted" debugging, is written out in the following paragraphs. I explain the map-specific nouns in brackets. You can likely dump this whole thing in your LLM window and it'll work; I'd try plan mode + skip permissions. THE PROMPT Make an interactive map with MapLibre GL JS [maplibre is a javascript mapping library, a FOSS version of Mapbox GL JS. This lets us display tiled map data and arbitrary images on the map] Add basemap toggles with Esri satellite, Carto Positron, and OSM [these map layers require no API keys for light usage; Carto Positron is a nice road map layer and OSM is ugly but comprehensive] Add a globe/mercator projection toggle [I think the globe looks better at low zooms] Add a layer panel on the left with visibility checkboxes and delete buttons. Add a search box on the map that flies to results, with deletable pin markers [Makes this easy to get to your area of interest] Include an interactive local georeferencer: drop a JPG, pick ground control points on a zoomable/pannable image viewer, place them on the map, watch it warp with a progress bar centered on the map. [The georeferencer uses math ("affine transform"??) to match points on the old map to points on the new map; generally you click road intersections on the old map, match them on the new map, repeat a dozen times and everything aligns] The georeferenced map overlay defaults to 25% opacity with a slider above the control point list. [I want it easy to see the underlying modern geography] Add Export/import control point buttons [this saves the control points as a JSON so you can save and reimport your work] Add a button to export the warped image as a GeoTIFF with a .prj [In case you want to add the georeferenced image to a real GIS program like QGIS] Look up all relevant docs before starting [Claude sometimes uses outdated stuff] Split everything into separate HTML/CSS/JS files [Claude tends to pile everything in index.html, which is hard to read] Use Optima font, base color #FEFAF6 [I just like this style] Let me test with a local server [it serves it on a simple server so you can nav your host to localhost:8000 and try it out] Log all errors [so you don't have to play telephone with the LLM describing what's busted] 3) Once your LLM finishes, test it out in your browser; if it doesn't work, ask the LLM to check logs. Repeat 'til functional. 4) After this works on your computer, you can show it to everyone by hosting it on GitHub: prompt with "write a README explaining what everything does, add it to a new GitHub repo, deploy using GitHub pages, gimme the live URL" Here's what Claude made for me, try it yourself: • Upload the JPG in the repo, which is linked below • "Add GCP" • Click somewhere recognizable on the old map, like the tip of an island or a road intersection • Click the matching point on the new map • Repeat til you have least 3x points • Hit "georeference" • You'll see the old map atop the new map; if you want a better fit, delete bad points or add a dozen new ones, hit georeference again, repeat Repo: Is this map robust? Human-maintainable? Elegant? Performant? Secure? No, but your personal web map need not be. It just needs to work for your narrow use case, because it’s your map.

Evan Applegate

15,772 次观看 • 5 个月前

The new-fangled vacuum salesman. This is the best example I've seen of how AI and computer vision is changing consumer electronics. Here Matic Robots founder Mehul comes to my home to give me an in-depth demo (the good stuff starts about 30 minutes into the video). This also shows how products are greatly improved when you have a team that looks at life from first principles. Some things to note? Because of its roller design it doesn't get clogged by pet hair. Because of its computer vision it doesn't get stuck on cords, or fringes on rugs. Because of its AI it doesn't "chew" an expensive rug and destroy it. Because of its 3D real-time scan of your home it lets it do things other vacuums can't.

The new-fangled vacuum salesman. This is the best example I've seen of how AI and computer vision is changing consumer electronics. Here Matic Robots founder Mehul comes to my home to give me an in-depth demo (the good stuff starts about 30 minutes into the video). This also shows how products are greatly improved when you have a team that looks at life from first principles. Some things to note? Because of its roller design it doesn't get clogged by pet hair. Because of its computer vision it doesn't get stuck on cords, or fringes on rugs. Because of its AI it doesn't "chew" an expensive rug and destroy it. Because of its 3D real-time scan of your home it lets it do things other vacuums can't.

Robert Scoble

275,896 次观看 • 2 年前

The pull-up is the one exercise I see people quit on the fastest. They try a few, can't do them, decide they're just "not a pull-up person," and move on. But the thing is, strength is a skill. A lot of it is just your nervous system learning the exact movement, and your body only gets good at what you actually practise. That's why you can lift heavy on a barbell and still struggle to pull your own bodyweight up. It's a different skill, and it takes time. So no, you're not bad at pull-ups. You just haven't trained them yet. Start with regressions like slow negatives or banded reps, and try grease the groove method (thread below). That's exactly how a skill gets built. One day the bodyweight that felt impossible starts moving. Genuinely one of the best feelings in the gym.

The pull-up is the one exercise I see people quit on the fastest. They try a few, can't do them, decide they're just "not a pull-up person," and move on. But the thing is, strength is a skill. A lot of it is just your nervous system learning the exact movement, and your body only gets good at what you actually practise. That's why you can lift heavy on a barbell and still struggle to pull your own bodyweight up. It's a different skill, and it takes time. So no, you're not bad at pull-ups. You just haven't trained them yet. Start with regressions like slow negatives or banded reps, and try grease the groove method (thread below). That's exactly how a skill gets built. One day the bodyweight that felt impossible starts moving. Genuinely one of the best feelings in the gym.

Dr Neha Chawla || FreeGym

24,223 次观看 • 2 个月前

I’m here in the Southwest — the actual Southwest, not just a pit stop in Wytheville… As Governor, I’ll open an official office in the Southwest so you know we care for you and see you. Abigail Spanberger, I’m happy to mail you a map of the Southwest since it was hard for you to find on your “tour.”

I’m here in the Southwest — the actual Southwest, not just a pit stop in Wytheville… As Governor, I’ll open an official office in the Southwest so you know we care for you and see you. Abigail Spanberger, I’m happy to mail you a map of the Southwest since it was hard for you to find on your “tour.”

Winsome Earle-Sears

31,841 次观看 • 1 年前

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Suryansh Tiwari

132,607 次观看 • 4 个月前

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

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

RoboHub🤖

101,350 次观看 • 5 个月前

Do you wanna learn how Sushil Kedia analysed #Nifty is now headed to 28000? And the expected map of movement? Same analysis he shared with our subscribers you can get free, for learning. Just: a) Follow us, so we can DM you b) Retweet c) Nice Replies help

Do you wanna learn how Sushil Kedia analysed #Nifty is now headed to 28000? And the expected map of movement? Same analysis he shared with our subscribers you can get free, for learning. Just: a) Follow us, so we can DM you b) Retweet c) Nice Replies help

KEDIANOMICS

47,833 次观看 • 5 个月前

I don’t have my action cam rigged up right now because I’m with people, but had to pull this and share it. FSD V14.2 will literally drive you off of a completely unmapped construction site using cameras and neural nets. Understand that this is a big grey block of nothing on the map and the car did not have any routing path on the GPS until we made it out to the road at the end. Do you understand the difference yet? Are you getting it? Tesla already ships robots to consumers.

I don’t have my action cam rigged up right now because I’m with people, but had to pull this and share it. FSD V14.2 will literally drive you off of a completely unmapped construction site using cameras and neural nets. Understand that this is a big grey block of nothing on the map and the car did not have any routing path on the GPS until we made it out to the road at the end. Do you understand the difference yet? Are you getting it? Tesla already ships robots to consumers.

Mike P

20,935 次观看 • 7 个月前

Supreme Court decriminalized adultery. Now, adultery is just a ground for divorce. So, if your husband is not loyal to you or having an affair then just go and take divorce from him instead of doing contempt of Honorable Court. Live and let live. You can not force loyalty on someone. If he is not loyal to you then just leave him and let him go.

Supreme Court decriminalized adultery. Now, adultery is just a ground for divorce. So, if your husband is not loyal to you or having an affair then just go and take divorce from him instead of doing contempt of Honorable Court. Live and let live. You can not force loyalty on someone. If he is not loyal to you then just leave him and let him go.

NCMIndia Council For Men Affairs

112,866 次观看 • 6 个月前

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.

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 次观看 • 6 个月前

"We're intentionally trying to not put out any guesstimate, because that's what it would be, is a guesstimate. We just have to let the thing heal and then after that, we can map out a runway." Brian Cashman talks about Aaron Judge's lack of a set return timeline:

"We're intentionally trying to not put out any guesstimate, because that's what it would be, is a guesstimate. We just have to let the thing heal and then after that, we can map out a runway." Brian Cashman talks about Aaron Judge's lack of a set return timeline:

SNY Yankees

18,365 次观看 • 2 个月前

here's just the looping part on its own, real time, no edits. gemma 4 12b had already built the thing, the work was done, and instead of stopping it just kept going. the same fragments over and over, talking to itself while the token counter climbed and climbed. watching a model do this is its own special kind of pain, you can see it's stuck but it has absolutely no idea that it's stuck. genuine question for anyone running gemma 4 locally through an agent, are you hitting this too, or did i just pull a cursed seed and everyone else is fine. i can't tell yet if it's the model or me, so tell me what you're seeing.

here's just the looping part on its own, real time, no edits. gemma 4 12b had already built the thing, the work was done, and instead of stopping it just kept going. the same fragments over and over, talking to itself while the token counter climbed and climbed. watching a model do this is its own special kind of pain, you can see it's stuck but it has absolutely no idea that it's stuck. genuine question for anyone running gemma 4 locally through an agent, are you hitting this too, or did i just pull a cursed seed and everyone else is fine. i can't tell yet if it's the model or me, so tell me what you're seeing.

Sudo su

16,483 次观看 • 2 个月前