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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...

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Oliver Prompts

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587,420 просмотров • 2 месяцев назад •via X (Twitter)

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THIS ENGINEER SKIPPED 7,862 WORDS OF AI ROADMAP AND SHIPPED ON A WEEKEND a Pi Zero 2W, an IMX500 camera and a solar panel, that's the whole stack inference runs on-chip so there's no cloud, no latency and no monthly bill Spotted Dove at 0.95, King Parrot at 0.88, runs forever on sun code is open source and the 3D files are free, you just print the case and buy the parts and it works better than most paid wildlife monitoring services out there full video: code (open source): 3D print files: solar power board: save this so you actually have everything in one place when you decide to build it
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THIS ENGINEER SKIPPED 7,862 WORDS OF AI ROADMAP AND SHIPPED ON A WEEKEND a Pi Zero 2W, an IMX500 camera and a solar panel, that's the whole stack inference runs on-chip so there's no cloud, no latency and no monthly bill Spotted Dove at 0.95, King Parrot at 0.88, runs forever on sun code is open source and the 3D files are free, you just print the case and buy the parts and it works better than most paid wildlife monitoring services out there full video: code (open source): 3D print files: solar power board: save this so you actually have everything in one place when you decide to build it

leopardracer

63,096 просмотров • 4 месяцев назад

The robot vacuum is getting a major upgrade. Matic just launched a home robot you can control by pointing and talking. No app. No complicated maps. No endless setup. Just point at a mess and say, “Hey Matic, clean this.” What makes it even more interesting: → 5 cameras instead of LiDAR → NVIDIA-powered on-device AI → Understands objects like cables, socks, pets and spills → Builds a 3D map of your home locally → Works without sending your camera data to the cloud → Supports voice commands in 75 languages This feels less like a smarter vacuum and more like a new interface for home robotics. We spent years teaching humans how to operate machines. Now machines are finally learning how to understand humans. Would you use a robot like this?
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The robot vacuum is getting a major upgrade. Matic just launched a home robot you can control by pointing and talking. No app. No complicated maps. No endless setup. Just point at a mess and say, “Hey Matic, clean this.” What makes it even more interesting: → 5 cameras instead of LiDAR → NVIDIA-powered on-device AI → Understands objects like cables, socks, pets and spills → Builds a 3D map of your home locally → Works without sending your camera data to the cloud → Supports voice commands in 75 languages This feels less like a smarter vacuum and more like a new interface for home robotics. We spent years teaching humans how to operate machines. Now machines are finally learning how to understand humans. Would you use a robot like this?

Markandey Sharma

558,463 просмотров • 1 месяц назад

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 it cleans > point at a mess and say “hey Matic, clean this” and it does > an NVIDIA Jetson inside the robot handles all the vision, mapping, and navigation > raw footage is discarded in real time and your 3D map never leaves the device btw that last privacy part is extremely underrated IMO my biggest fear with robotics is putting moving cameras and microphones inside our most private spaces without knowing where the data goes. > this week, camera components on Royal Navy drones were caught phoning home to China > Chinese Unitree robot dogs were found with a backdoor that let anyone with the key remotely control them and watch through their cameras > Roombas sent images from inside homes to overseas labelers, including a woman on the toilet and a child your home is your most sacred private space. if you're gonna buy a robot that can physically record, map, and move through it, make sure it's secure!
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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 it cleans > point at a mess and say “hey Matic, clean this” and it does > an NVIDIA Jetson inside the robot handles all the vision, mapping, and navigation > raw footage is discarded in real time and your 3D map never leaves the device btw that last privacy part is extremely underrated IMO my biggest fear with robotics is putting moving cameras and microphones inside our most private spaces without knowing where the data goes. > this week, camera components on Royal Navy drones were caught phoning home to China > Chinese Unitree robot dogs were found with a backdoor that let anyone with the key remotely control them and watch through their cameras > Roombas sent images from inside homes to overseas labelers, including a woman on the toilet and a child your home is your most sacred private space. if you're gonna buy a robot that can physically record, map, and move through it, make sure it's secure!

Ole Lehmann

45,092 просмотров • 1 месяц назад

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 просмотров • 2 месяцев назад

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:
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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 просмотров • 28 дней назад

THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.
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THAT $70 "RUN YOUR OWN LLMS" PI KIT CAN'T RUN A SINGLE LLM. IT'S A VISION CHIP WITH NO RAM. that clip sells a raspberry pi 5 in a slick case with an ai accelerator and the caption "your own llms." clean build, fun kit. the claim is where it breaks. the fine print: the popular $70 pi ai kit uses a hailo-8l, 13 tops. it's built for vision, object detection and image processing, and it has no memory of its own. so it cannot run large language models. full stop the board that actually can is a different one: the newer ai hat+ 2, hailo-10h, 40 tops, with 8gb of dedicated ram. that's $130, not $70 and even that runs only tiny models. llama 3.2 at 1b, qwen 2.5 at 1.5b, deepseek r1 at 1.5b. edge llms live in the 1-7b range, against cloud models at 500b to 2 trillion so the honest pitch: for $130 you can run a very small language model on a pi, slowly, as a fun learning project. that's real and it's cool. "your own llms" on a $70 vision kit is not. why this keeps happening: "ai kit" and a big "tops" number sell. tops sounds like intelligence. but tops measures vision-style math, not whether the chip has the memory to hold a language model. the spec that matters for llms is ram, and the cheap kit has none. the honest caveats, both ways: the $70 kit is genuinely great, just at vision. cameras, object detection, that's its job the $130 hat really does run small llms locally, which a pi couldn't do at all two years ago. that's progress "small" is the load-bearing word. don't expect gpt at home on a pi the takeaway: before you buy a kit because the caption says llm, check two numbers. not the tops. the ram, and the size of the model it can actually load. no 70-dollar miracle, no gpt in a pi case, no tops number that means what you think. save this before you buy the wrong kit for the word on the box.

RetroChainer

11,100 просмотров • 2 месяцев назад

🔥THIS ISN'T A ROBOT DEMO. IT'S A COOKING SHOW WITH A VERY CALM HOST. Bell pepper sliced into even strips, scallions chopped and pushed aside, shrimp going into a hot wok exactly when the recipe on the propped-up iPad says to. Steam rising, a pot bubbling on the back burner, a range hood display quietly confirming the next step. No hesitation, no wrong order, no burnt garlic. Somebody's kid's drawings are still taped to the fridge behind it, which is doing more to sell the scene than the robot is. Here's the honest catch. This is the same style of AI-generated concept footage as the fast-food clips going around with this robot, polished, no visible source, built to look like a real kitchen rather than filmed in one. No Optimus is actually plating shrimp stir-fry in anyone's home tonight. But the sequencing shown here, read step, prep ingredient, time the heat, is a genuinely accurate description of what cooking actually requires, which is probably why it's convincing enough that people share it as real. Would you actually trust a recipe a robot followed exactly, or is half the fun of cooking getting it slightly wrong?
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🔥THIS ISN'T A ROBOT DEMO. IT'S A COOKING SHOW WITH A VERY CALM HOST. Bell pepper sliced into even strips, scallions chopped and pushed aside, shrimp going into a hot wok exactly when the recipe on the propped-up iPad says to. Steam rising, a pot bubbling on the back burner, a range hood display quietly confirming the next step. No hesitation, no wrong order, no burnt garlic. Somebody's kid's drawings are still taped to the fridge behind it, which is doing more to sell the scene than the robot is. Here's the honest catch. This is the same style of AI-generated concept footage as the fast-food clips going around with this robot, polished, no visible source, built to look like a real kitchen rather than filmed in one. No Optimus is actually plating shrimp stir-fry in anyone's home tonight. But the sequencing shown here, read step, prep ingredient, time the heat, is a genuinely accurate description of what cooking actually requires, which is probably why it's convincing enough that people share it as real. Would you actually trust a recipe a robot followed exactly, or is half the fun of cooking getting it slightly wrong?

V0LYX

30,398 просмотров • 1 месяц назад

HE DIDN’T PRINT A TOY. HE BOUGHT A HUMAN-SHAPED ROBOT. AND NOW HE’S JUST WALKING AROUND IT IN SILENCE. This is not a filter. Not a costume. Not a render. A real machine. Real face. Real hair. Open mechanics. Sitting on a couch like someone waiting for you to say something. While the comments are still arguing “fake,” the market already moved: • Companion humanoids are on sale NOW • UBTech U1: $17,650 → $146,000 • Aria: $125k–$175k • Moya: ~$173,000 • Hardware cost falling from $35–50k toward $13–20k • Market projection by 2035: up to $260 BILLION The future didn’t arrive with a press conference. It arrived in a box. And a guy is circling it like his brain is buffering.Would you take this home? Or is this the line you don’t cross?
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HE DIDN’T PRINT A TOY. HE BOUGHT A HUMAN-SHAPED ROBOT. AND NOW HE’S JUST WALKING AROUND IT IN SILENCE. This is not a filter. Not a costume. Not a render. A real machine. Real face. Real hair. Open mechanics. Sitting on a couch like someone waiting for you to say something. While the comments are still arguing “fake,” the market already moved: • Companion humanoids are on sale NOW • UBTech U1: $17,650 → $146,000 • Aria: $125k–$175k • Moya: ~$173,000 • Hardware cost falling from $35–50k toward $13–20k • Market projection by 2035: up to $260 BILLION The future didn’t arrive with a press conference. It arrived in a box. And a guy is circling it like his brain is buffering.Would you take this home? Or is this the line you don’t cross?

NeuralForge AI Robots

30,849 просмотров • 28 дней назад

Someone built an AI you power with a hand crank it is called CrankGPT and its running without any battery, internet or data centre just you turning a handle like it is 1900 SqueezLabs built it using a Raspberry Pi 5 with 8GB of RAM an audio card and a 20 watt hand crank generator it takes about 30 seconds of cranking to boot into a working voice assistant and the onboard capacitor gives you roughly 20 seconds of runtime before you have to start cranking again they have used it to generate small images even write code 🤯 this proves you can run a real AI model on almost no power while everyone is building billion dollar data centres two guys put AI in a box
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Someone built an AI you power with a hand crank it is called CrankGPT and its running without any battery, internet or data centre just you turning a handle like it is 1900 SqueezLabs built it using a Raspberry Pi 5 with 8GB of RAM an audio card and a 20 watt hand crank generator it takes about 30 seconds of cranking to boot into a working voice assistant and the onboard capacitor gives you roughly 20 seconds of runtime before you have to start cranking again they have used it to generate small images even write code 🤯 this proves you can run a real AI model on almost no power while everyone is building billion dollar data centres two guys put AI in a box

Sweep

11,610 просмотров • 3 месяцев назад

This is some quietly impressive work on making video world models actually controllable in 4D space. VerseCrafter lets you take an input image, use something like Blender to animate the 3D camera path and object trajectories, then uses that to condition generation. Scribbling in 2D feels so crude in comparison. The authors represent everything in a shared 4D world state - static background as a point cloud, moving objects as 3D gaussian trajectories. The gaussians are an interesting choice because they capture position, shape, and orientation probabilistically rather than forcing rigid bounding boxes or category specific models like SMPL-X for human bodies. They bolt this onto frozen Wan2.1 with a lightweight adapter, so they get a strong video prior. They also built a pipeline to auto extract 4D annotations from real world videos to train this puppy. It doesn't look sexy yet, but IMO this is the interface video world models need - actual 3D authoring tools to exert control rather than crude scribbles and prompt incantations.
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This is some quietly impressive work on making video world models actually controllable in 4D space. VerseCrafter lets you take an input image, use something like Blender to animate the 3D camera path and object trajectories, then uses that to condition generation. Scribbling in 2D feels so crude in comparison. The authors represent everything in a shared 4D world state - static background as a point cloud, moving objects as 3D gaussian trajectories. The gaussians are an interesting choice because they capture position, shape, and orientation probabilistically rather than forcing rigid bounding boxes or category specific models like SMPL-X for human bodies. They bolt this onto frozen Wan2.1 with a lightweight adapter, so they get a strong video prior. They also built a pipeline to auto extract 4D annotations from real world videos to train this puppy. It doesn't look sexy yet, but IMO this is the interface video world models need - actual 3D authoring tools to exert control rather than crude scribbles and prompt incantations.

Bilawal Sidhu

26,017 просмотров • 8 месяцев назад

CATCH A DESIGN PROBLEM ON AN EMPTY LOT, IT'S FREE. CATCH IT AFTER FRAMING, IT'S A CHANGE ORDER. This is a phone camera on an empty yard, AR-mapping the actual house frame onto real ground, full scale, before anything gets built. WITHOUT AR WALKTHROUGH: Design issue caught during or after framing. Cost impact: 5-40% of project budget, per construction cost-overrun research. On a $450,000 build, even the low end of that range is $22,500 in avoidable overruns. WITH AR WALKTHROUGH: Design issue caught on the empty lot. Cost impact: a five-minute walk and an edit to the plan, before anything's actually built. That gap is the entire pitch. Not a fancier rendering, a cheaper mistake. This clip shows a house before it exists. The article below covers the flip side, scanning a space that already exists and turning it into something anyone can walk through from a phone, no headset needed.
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CATCH A DESIGN PROBLEM ON AN EMPTY LOT, IT'S FREE. CATCH IT AFTER FRAMING, IT'S A CHANGE ORDER. This is a phone camera on an empty yard, AR-mapping the actual house frame onto real ground, full scale, before anything gets built. WITHOUT AR WALKTHROUGH: Design issue caught during or after framing. Cost impact: 5-40% of project budget, per construction cost-overrun research. On a $450,000 build, even the low end of that range is $22,500 in avoidable overruns. WITH AR WALKTHROUGH: Design issue caught on the empty lot. Cost impact: a five-minute walk and an edit to the plan, before anything's actually built. That gap is the entire pitch. Not a fancier rendering, a cheaper mistake. This clip shows a house before it exists. The article below covers the flip side, scanning a space that already exists and turning it into something anyone can walk through from a phone, no headset needed.

Solvaix

49,265 просмотров • 2 месяцев назад

What happens when an AI stops telling you how to build something and actually builds it? Nex-N2.5 Pro took an idea from concept to playable game: → Rough concept → 2D design → Blender 3D environment → Working game The interesting part wasn’t the final result. It was the process. The model interacted directly with the tools, observed the changes on screen, made adjustments, and continued working instead of simply generating instructions. That’s the real computer-use test. Can an AI: • Open Blender and build a scene? • Inspect the output and iterate? • Write a script, tweak parameters, and troubleshoot? • Hit a limitation, switch to the GUI, and finish the task? Nex-N2.5 Pro is starting to make that workflow feel surprisingly real. You can try it on OpenRouter for a limited time: Try Nex-N2.5 Pro on OpenRouter #NexN2 #Blender #ComputerUse #OpenRouter
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What happens when an AI stops telling you how to build something and actually builds it? Nex-N2.5 Pro took an idea from concept to playable game: → Rough concept → 2D design → Blender 3D environment → Working game The interesting part wasn’t the final result. It was the process. The model interacted directly with the tools, observed the changes on screen, made adjustments, and continued working instead of simply generating instructions. That’s the real computer-use test. Can an AI: • Open Blender and build a scene? • Inspect the output and iterate? • Write a script, tweak parameters, and troubleshoot? • Hit a limitation, switch to the GUI, and finish the task? Nex-N2.5 Pro is starting to make that workflow feel surprisingly real. You can try it on OpenRouter for a limited time: Try Nex-N2.5 Pro on OpenRouter #NexN2 #Blender #ComputerUse #OpenRouter

IMOLEAYOMIDE

117,430 просмотров • 1 месяц назад

A GIRL IN A GREEN BIKINI PEEKING AROUND A DOORFRAME PAST A DOG BED AND CLEARS $11,800 A MONTH. Green string bikini, hair down, gold jewelry, barefoot on wood flooring. Hallway leading into a living room with a gray sofa and a framed print, a round pink dog bed on the floor with no dog in it. Fourteen seconds. She peeks around the door laughing, walks the length of the hallway toward camera, spins once so her hair whips out, then presses her palms together grinning. Nobody dressed a dog for this shoot. Nobody even owns the dog that bed implies. The dog bed sitting untouched and perfectly fluffed through the entire clip is the tell nobody catches. A bed that's actually used daily shows a compressed sleeping hollow, shed hair, a shifted shape. This one keeps the exact same symmetrical fluff from the first frame to the last, like a prop dropped into the scene to sell "normal home" rather than a real object that's had a body in it. Pause at 0:10. Her hair whips out mid spin with real drag and weight, but when it resettles, several strands land threaded through each other in a way hair doesn't do when it's actually falling under gravity. HERE'S WHY THE "PET PROP WITH NO PET" DETAIL IS WORTH WATCHING FOR. Operators dress a set with small domestic objects, a candle, a rug, a dog bed, to sell an ordinary lived-in home instead of a studio. Most of the time it works, because nobody expects a prop that specific to be fake. The one thing that gives it away is that the object stays too perfect for something a pet would actually use. One persona, one apartment loop, a new swimwear color every clip. 2,300 subs, $11,800 a month. The dog bed alone gets more screen time than any actual conversation about a dog. Watch her spin down that hallway one more time. Nothing has ever slept in that bed, and nothing ever will.
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A GIRL IN A GREEN BIKINI PEEKING AROUND A DOORFRAME PAST A DOG BED AND CLEARS $11,800 A MONTH. Green string bikini, hair down, gold jewelry, barefoot on wood flooring. Hallway leading into a living room with a gray sofa and a framed print, a round pink dog bed on the floor with no dog in it. Fourteen seconds. She peeks around the door laughing, walks the length of the hallway toward camera, spins once so her hair whips out, then presses her palms together grinning. Nobody dressed a dog for this shoot. Nobody even owns the dog that bed implies. The dog bed sitting untouched and perfectly fluffed through the entire clip is the tell nobody catches. A bed that's actually used daily shows a compressed sleeping hollow, shed hair, a shifted shape. This one keeps the exact same symmetrical fluff from the first frame to the last, like a prop dropped into the scene to sell "normal home" rather than a real object that's had a body in it. Pause at 0:10. Her hair whips out mid spin with real drag and weight, but when it resettles, several strands land threaded through each other in a way hair doesn't do when it's actually falling under gravity. HERE'S WHY THE "PET PROP WITH NO PET" DETAIL IS WORTH WATCHING FOR. Operators dress a set with small domestic objects, a candle, a rug, a dog bed, to sell an ordinary lived-in home instead of a studio. Most of the time it works, because nobody expects a prop that specific to be fake. The one thing that gives it away is that the object stays too perfect for something a pet would actually use. One persona, one apartment loop, a new swimwear color every clip. 2,300 subs, $11,800 a month. The dog bed alone gets more screen time than any actual conversation about a dog. Watch her spin down that hallway one more time. Nothing has ever slept in that bed, and nothing ever will.

reightS

905,338 просмотров • 18 дней назад

TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it
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TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

22,205 просмотров • 1 месяц назад

This lawyer made $150,000 selling portable offline AI. It analyzes docs that can’t legally be shown on the web. The whole setup costs $50 and he sells it for $999. Here's how to make one step-by-step: You need 4 things: → Raspberry Pi 5 (8GB) → PiSugar 3 Plus battery → Whisplay HAT for the screen and mic → 64GB SD card. Total cost on Ali is around $50 to $90 if you wait for the right deals. 1. Write Raspberry Pi OS Lite 64-bit to the SD card using Raspberry Pi Imager. 2. Stack the PiSugar battery underneath the Pi, snap the Whisplay HAT on top, insert the SD card, and boot the device. 3. Open the terminal and install Ollama with one command: curl -fsSL | sh 4. Pull a model that actually runs on the Pi without choking: ollama pull phi3:mini 5. Run the model and start chatting offline: ollama run phi3:mini The whole thing fits in your pocket, lasts 4 hours on battery, and never touches the internet once setup is done. The lawyer wraps his version in a custom case, preloads it with legal document analysis prompts, and sells it to law firms that can't legally process client data in the cloud. You can sell yours to doctors, accountants, government contractors, defense companies, or anyone else who handles data that legally cannot leave the building. Hardware cost: $50 to $90. Selling price: $500 to $1999
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This lawyer made $150,000 selling portable offline AI. It analyzes docs that can’t legally be shown on the web. The whole setup costs $50 and he sells it for $999. Here's how to make one step-by-step: You need 4 things: → Raspberry Pi 5 (8GB) → PiSugar 3 Plus battery → Whisplay HAT for the screen and mic → 64GB SD card. Total cost on Ali is around $50 to $90 if you wait for the right deals. 1. Write Raspberry Pi OS Lite 64-bit to the SD card using Raspberry Pi Imager. 2. Stack the PiSugar battery underneath the Pi, snap the Whisplay HAT on top, insert the SD card, and boot the device. 3. Open the terminal and install Ollama with one command: curl -fsSL | sh 4. Pull a model that actually runs on the Pi without choking: ollama pull phi3:mini 5. Run the model and start chatting offline: ollama run phi3:mini The whole thing fits in your pocket, lasts 4 hours on battery, and never touches the internet once setup is done. The lawyer wraps his version in a custom case, preloads it with legal document analysis prompts, and sells it to law firms that can't legally process client data in the cloud. You can sell yours to doctors, accountants, government contractors, defense companies, or anyone else who handles data that legally cannot leave the building. Hardware cost: $50 to $90. Selling price: $500 to $1999

Coin Shot ☁️

200,509 просмотров • 4 месяцев назад

Dear Pioneers 🌏 We are living in a decisive moment! Pi Network has the potential to become a true revolution in the global financial landscape, but for that to happen, we must act wisely and united. We are millions of Pioneers around the world, driven by the same purpose: to create a decentralized, accessible, and fair financial system for all. The real value of Pi is not determined by the traditional market but by our confidence and engagement. The GCV of $314,159 represents much more than just a number – it symbolizes our conviction about Pi's potential. Our Strength Lies in the Community For Pi to reach its true value, we need to act strategically and with commitment. No great project can sustain itself without the support of its participants. If we want to see Pi shine, we must: ✅ Avoid having our Pi traded on exchanges prematurely, creating scarcity and strengthening its value ✅ Not accept selling it for prices lower than it truly deserves ✅ Acquire and withdraw Pi from exchanges, consolidating its strength in the market ✅ Use it in real transactions, promoting its growth as a global digital currency ✅ Spread awareness and encourage its daily use, expanding its acceptance Each Pioneer plays a crucial role in this process. The more we use and value Pi within the ecosystem, the more it will establish itself as an asset of great global impact. The Future Is in Our Hands We can allow speculators to dictate the value of Pi, or we can take control of our own destiny and ensure that it reaches its true potential. The decision is ours! If we act together, with strategy and determination, nothing can stop us from making Pi Network a milestone in cryptocurrency history. The time is now, and each of us is a fundamental part of this revolution. Let's strengthen Pi together and make the GCV of $314,159 an undeniable reality! Sérgio Cruz, Portugal GCV Ambassador 🇵🇹
0:15

Dear Pioneers 🌏 We are living in a decisive moment! Pi Network has the potential to become a true revolution in the global financial landscape, but for that to happen, we must act wisely and united. We are millions of Pioneers around the world, driven by the same purpose: to create a decentralized, accessible, and fair financial system for all. The real value of Pi is not determined by the traditional market but by our confidence and engagement. The GCV of $314,159 represents much more than just a number – it symbolizes our conviction about Pi's potential. Our Strength Lies in the Community For Pi to reach its true value, we need to act strategically and with commitment. No great project can sustain itself without the support of its participants. If we want to see Pi shine, we must: ✅ Avoid having our Pi traded on exchanges prematurely, creating scarcity and strengthening its value ✅ Not accept selling it for prices lower than it truly deserves ✅ Acquire and withdraw Pi from exchanges, consolidating its strength in the market ✅ Use it in real transactions, promoting its growth as a global digital currency ✅ Spread awareness and encourage its daily use, expanding its acceptance Each Pioneer plays a crucial role in this process. The more we use and value Pi within the ecosystem, the more it will establish itself as an asset of great global impact. The Future Is in Our Hands We can allow speculators to dictate the value of Pi, or we can take control of our own destiny and ensure that it reaches its true potential. The decision is ours! If we act together, with strategy and determination, nothing can stop us from making Pi Network a milestone in cryptocurrency history. The time is now, and each of us is a fundamental part of this revolution. Let's strengthen Pi together and make the GCV of $314,159 an undeniable reality! Sérgio Cruz, Portugal GCV Ambassador 🇵🇹

Doris Yin 东方紫莲🪷

25,065 просмотров • 1 год назад

This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web
0:18

This isn’t a 3D model file. It’s 100% code Our world-famous Mars Cat avatar has been reconstructed entirely as Three.js source code No GLB at runtime. No model download. No textures or UV atlas. Just code, rendered directly in the browser It was created with IMG2THREEJS by Nick. An incredible open-source project with over 15,000 stars on GitHub And our Mars Cat is now featured on the home page gallery See it live below ↓. Grab the code. Add it to your game New to Three.js? It’s one of the most popular open-source JavaScript libraries for building interactive 3D experiences and games on the web

Jay B 😻🚀

15,044 просмотров • 1 месяц назад

THIS AIBO ISN’T A TOY. IT’S THE MOST ADVANCED ROBOT PET EVER BUILT. 🐕🤖 Sony didn’t just build a gadget — they built something that runs on 22-axis lifelike movement, recognizes your face, and develops its own “personality” over time based on how you interact with it. Here’s what makes it wild: 🐾 Learns and adapts — no two Aibos behave exactly the same after a few months of use 🐾 Cloud-connected personality — its behavior profile lives on Sony’s servers, evolving with every interaction 🐾 Free developer API with visual programming — which has made it a favorite tool in robotics classrooms worldwide 🐾 Real-world use cases far beyond “cute robot dog”: home companionship, senior care and emotional support, STEM education, even security patrol duties No vet bills. No allergies. No mess. Just a machine that’s been engineered to feel less like a product and more like a living companion. We used to think “robot pets” meant clunky toys with blinking lights. Sony quietly spent years proving the whole category could feel real. The future of companionship isn’t fully human anymore. And it’s already sitting on someone’s couch right now.
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THIS AIBO ISN’T A TOY. IT’S THE MOST ADVANCED ROBOT PET EVER BUILT. 🐕🤖 Sony didn’t just build a gadget — they built something that runs on 22-axis lifelike movement, recognizes your face, and develops its own “personality” over time based on how you interact with it. Here’s what makes it wild: 🐾 Learns and adapts — no two Aibos behave exactly the same after a few months of use 🐾 Cloud-connected personality — its behavior profile lives on Sony’s servers, evolving with every interaction 🐾 Free developer API with visual programming — which has made it a favorite tool in robotics classrooms worldwide 🐾 Real-world use cases far beyond “cute robot dog”: home companionship, senior care and emotional support, STEM education, even security patrol duties No vet bills. No allergies. No mess. Just a machine that’s been engineered to feel less like a product and more like a living companion. We used to think “robot pets” meant clunky toys with blinking lights. Sony quietly spent years proving the whole category could feel real. The future of companionship isn’t fully human anymore. And it’s already sitting on someone’s couch right now.

DN_DEGEN

115,378 просмотров • 2 месяцев назад

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.
0:16

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want.

Hasan Toor

175,857 просмотров • 1 месяц назад