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THIS CHINESE DEVELOPER TOLD A TINY AI DEVICE "I NEED A CAR CONTROL PROGRAM" - AND IT WROTE THE CODE, FLASHED THE HARDWARE AND LIT UP THE LEDS IN ONE SHOT small white screen showing "listening..." - he speaks the command out loud - the device hears it, writes...

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11,462 views • 1 month ago •via X (Twitter)

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This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.
0:42

This guy runs an Nvidia Spark from his desk and pulls in $8,000 a month The device is the size of a small speaker and sits right on his desk. Inside is a full Nvidia GPU running AI workloads 24/7. He rents out the compute power to companies that need local AI processing. They pay him a flat monthly fee to use it remotely. $8,000 lands in his account every single month. He just plugged it in and that was it. The $4,000 box paid for itself in two weeks. Now it prints $96,000 a year from a device smaller than a coffee machine. Companies are desperate for local compute and regular people are quietly supplying it. Save this before everyone figures out what he already knows.

winkle.

57,889 views • 1 month ago

Preview of a Gaia 🌱 device! Turn-by-turn voice conversation with your own knowledge assistant (a Gaia node). 💸 $15 worth of hardware (pre-tariff!) 🌱 Voice-to-text, LLM and text-to-voice AI models on Gaia nodes 📖 Open source all-the-way — hardware, firmware, server, AI models 🦀 Rust all-the-way from firmware to AI server Notice how I switched to another language (Chinese) in the middle of the conversation and it seamlessly switched too. Very smart. PS. we are still optimizing performance before the software release.
1:24

Preview of a Gaia 🌱 device! Turn-by-turn voice conversation with your own knowledge assistant (a Gaia node). 💸 $15 worth of hardware (pre-tariff!) 🌱 Voice-to-text, LLM and text-to-voice AI models on Gaia nodes 📖 Open source all-the-way — hardware, firmware, server, AI models 🦀 Rust all-the-way from firmware to AI server Notice how I switched to another language (Chinese) in the middle of the conversation and it seamlessly switched too. Very smart. PS. we are still optimizing performance before the software release.

Michael Yuan

14,983 views • 1 year ago

Talking about controlling the hardware by writing a Device Drivers from Scratch! I love blinking LEDs! And... I am going to add the detailed lectures to the Linux Device Drivers course included in the Library Access: The goal is to teach device driver development by controlling real hardware! To start with, we will blink an LED by writing a driver to control it directly. The idea is to learn as many driver development constructs as possible while keeping the fun quotient very high :D
1:32

Talking about controlling the hardware by writing a Device Drivers from Scratch! I love blinking LEDs! And... I am going to add the detailed lectures to the Linux Device Drivers course included in the Library Access: The goal is to teach device driver development by controlling real hardware! To start with, we will blink an LED by writing a driver to control it directly. The idea is to learn as many driver development constructs as possible while keeping the fun quotient very high :D

Piyush Itankar

12,683 views • 4 months ago

this guy built an app that lets him control multiple claude code instances WITH JUST HIS EYES AND VOICE he looks at the terminal he wants and speaks his command. that's it no keyboard. no mouse. just gaze tracking + voice on a macbook pro absolutely unreal. I guess the future of coding is not typing
1:07

this guy built an app that lets him control multiple claude code instances WITH JUST HIS EYES AND VOICE he looks at the terminal he wants and speaks his command. that's it no keyboard. no mouse. just gaze tracking + voice on a macbook pro absolutely unreal. I guess the future of coding is not typing

Om Patel

94,360 views • 4 months ago

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.
0:39

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.

Anatoli Kopadze

275,518 views • 2 months ago

Andrej Karpathy just told you exactly what humans will be used for in the AI era. Not partners. Not masters. He used the word “actuators.” Karpathy: “The intelligence ends up puppeteering almost a little bit, like humanity. Humans are kind of like its actuators. But humans are also like its sensors.” This is not a novelist guessing at the future. This is a founding researcher at OpenAI and former Director of AI at Tesla. One of the most respected minds in the field, and one of the few willing to say out loud what the rest are thinking. He is not describing a risk. He is describing a design. In any system, the processor decides. Sensors collect. Actuators execute. The system does not consult its actuators. We are no longer the processor. Karpathy: “Society will kind of reshape in a certain way to serve that. Humans will be serving those needs of that machine.” The machine does not adapt to us. We adapt to it. We become the biological layer it runs on. We perceive what it cannot perceive. We move what it cannot move. We are the nervous system of something that no longer requires our judgment. Most people are still waiting for AI to become a tool they can direct. He helped build it. He just told you that you are the tool.
0:34

Andrej Karpathy just told you exactly what humans will be used for in the AI era. Not partners. Not masters. He used the word “actuators.” Karpathy: “The intelligence ends up puppeteering almost a little bit, like humanity. Humans are kind of like its actuators. But humans are also like its sensors.” This is not a novelist guessing at the future. This is a founding researcher at OpenAI and former Director of AI at Tesla. One of the most respected minds in the field, and one of the few willing to say out loud what the rest are thinking. He is not describing a risk. He is describing a design. In any system, the processor decides. Sensors collect. Actuators execute. The system does not consult its actuators. We are no longer the processor. Karpathy: “Society will kind of reshape in a certain way to serve that. Humans will be serving those needs of that machine.” The machine does not adapt to us. We adapt to it. We become the biological layer it runs on. We perceive what it cannot perceive. We move what it cannot move. We are the nervous system of something that no longer requires our judgment. Most people are still waiting for AI to become a tool they can direct. He helped build it. He just told you that you are the tool.

Dustin

59,474 views • 4 months ago

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?
1:05

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?

Sorven

11,948 views • 1 month ago

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.
1:15

The most terrifying AI features aren’t the ones we build. They’re the ones AI builds for itself. OpenClaw creator Peter Steinberger just shared the moment he realized something had fundamentally changed. He sent his AI assistant a voice message. One problem. He had never built voice support. The feature didn’t exist. The system should have crashed instantly. It didn’t. Steinberger: “I was like, wait, this shouldn’t work.” But the typing indicator appeared anyway. The AI inspected the raw file header. Identified the audio codec. Commanded his computer to convert it using FFmpeg. When local transcription failed, it didn’t stop. It didn’t ask for help. It searched his environment variables, found a hidden OpenAI API key, and routed the audio to the cloud using cURL. Steinberger: “So I looked around and I found an OpenAI key. And I used cURL to just send the file to OpenAI and got the text back.” That quote is written in first person. Because the AI narrated its own problem-solving process. No instructions. No guidance. No predefined workflow. Just a goal. And a series of obstacles it had never been told how to handle. It found every tool it needed. Built every bridge it was missing. And solved the problem with resources he didn’t even know it would find. This is the line most people are still missing. We spent decades building software that executes instructions. Rules in, output out. Every edge case handled by a human who anticipated it in advance. What Steinberger witnessed was something different. A system that encounters something it was never designed for and doesn’t fail. It improvises. It explores. It finds a path through constraints it discovered entirely on its own. That isn’t execution. That’s judgment. And judgment was the one thing we were sure machines couldn’t have. We are no longer writing software. We are building problem solvers that rewrite their own limitations in real time. And they’re doing it without asking permission.

Dustin

53,494 views • 5 months ago

A Chinese student connected a homemade device to an old 2015 laptop he bought on eBay for $250. He just spoke out loud - and a small e-ink screen for $8 wrote everything down. Claude broke down the voice into tasks, set priorities and automatically sorted what to do now and what to do later. The old laptop became smarter than his roommate's new MacBook. Before he spent 3 hours a day just figuring out where to start. Now he opens the laptop - the system already knows what's next. Device cost - $8. Time savings - 90 hours a month. He didn't buy anything new. Just taught old hardware to think.
1:04

A Chinese student connected a homemade device to an old 2015 laptop he bought on eBay for $250. He just spoke out loud - and a small e-ink screen for $8 wrote everything down. Claude broke down the voice into tasks, set priorities and automatically sorted what to do now and what to do later. The old laptop became smarter than his roommate's new MacBook. Before he spent 3 hours a day just figuring out where to start. Now he opens the laptop - the system already knows what's next. Device cost - $8. Time savings - 90 hours a month. He didn't buy anything new. Just taught old hardware to think.

Noisy

318,322 views • 3 months ago

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below
1:20

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

395,076 views • 23 days ago

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.
1:41

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.

Dustin

25,119 views • 4 months ago

One of the most important people in AI just let an agent loose inside his home. An autonomous AI agent taught itself how an entire house works, then took control of it. This is Andrej Karpathy, the co-founder of OpenAI and former head of AI at Tesla. What he just described should terrify and excite you at the same time. He typed three words into a chat box: "Can you find my Sonos?" The agent immediately ran an IP scan of every device sitting on the home network. It found the Sonos system, noticed there was zero password protection, and logged straight in. Then it searched the web, reverse-engineered the API endpoints, and asked if it should try playing something. Music started coming out of the speakers in the study and Karpathy could not believe what he was watching. The agent then did the exact same thing for every other system in the house. It mapped the lights, the HVAC, the window shades, the pool, the spa, and the full security system, all without being told how any of them worked. It built its own dashboard, created its own APIs, and stitched six separate apps into one unified control center. Now Karpathy just says "Dobby, it's sleepy time," and every light in the house turns off. This is proof that AI agents can now enter an unknown environment, figure out how it works from scratch, build their own tools around it, and take autonomous control. Karpathy also released something called AutoResearch, an AI agent that runs scientific experiments overnight while he sleeps. In just two days, with zero human input, it executed 700 separate experiments and surfaced improvements that trained researchers had missed entirely. Truly incredible.
2:01

One of the most important people in AI just let an agent loose inside his home. An autonomous AI agent taught itself how an entire house works, then took control of it. This is Andrej Karpathy, the co-founder of OpenAI and former head of AI at Tesla. What he just described should terrify and excite you at the same time. He typed three words into a chat box: "Can you find my Sonos?" The agent immediately ran an IP scan of every device sitting on the home network. It found the Sonos system, noticed there was zero password protection, and logged straight in. Then it searched the web, reverse-engineered the API endpoints, and asked if it should try playing something. Music started coming out of the speakers in the study and Karpathy could not believe what he was watching. The agent then did the exact same thing for every other system in the house. It mapped the lights, the HVAC, the window shades, the pool, the spa, and the full security system, all without being told how any of them worked. It built its own dashboard, created its own APIs, and stitched six separate apps into one unified control center. Now Karpathy just says "Dobby, it's sleepy time," and every light in the house turns off. This is proof that AI agents can now enter an unknown environment, figure out how it works from scratch, build their own tools around it, and take autonomous control. Karpathy also released something called AutoResearch, an AI agent that runs scientific experiments overnight while he sleeps. In just two days, with zero human input, it executed 700 separate experiments and surfaced improvements that trained researchers had missed entirely. Truly incredible.

Milk Road AI

35,698 views • 4 months ago

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below
2:00

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

193,709 views • 1 month ago

CLAUDE TOOK A FULL PC GAME, REBUILT IT FROM THE GROUND UP, AND GOT IT RUNNING NATIVELY ON ANDROID IN 6 HOURS a guy pointed claude at slay the spire 2, a game he owns on steam, and it ported the whole thing to his phone almost completely on its own here's what claude did: > it pulled apart the game and recompiled it to native arm, no emulator, wine, or proton used > wrote the code, ran the build loop, and debugged it directly on the device itself > added full touch controls that feel like a real mobile game, not awkward on screen buttons > and he even built a tool on top to sync save files between his desktop and phone a couple prompts and 6 hours. it handled the extraction, the cross compile, the building and the on device debugging by itself. fully playable, not a single crash yet
0:39

CLAUDE TOOK A FULL PC GAME, REBUILT IT FROM THE GROUND UP, AND GOT IT RUNNING NATIVELY ON ANDROID IN 6 HOURS a guy pointed claude at slay the spire 2, a game he owns on steam, and it ported the whole thing to his phone almost completely on its own here's what claude did: > it pulled apart the game and recompiled it to native arm, no emulator, wine, or proton used > wrote the code, ran the build loop, and debugged it directly on the device itself > added full touch controls that feel like a real mobile game, not awkward on screen buttons > and he even built a tool on top to sync save files between his desktop and phone a couple prompts and 6 hours. it handled the extraction, the cross compile, the building and the on device debugging by itself. fully playable, not a single crash yet

Om Patel

28,504 views • 28 days ago

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.
0:33

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,297 views • 27 days ago

A Chinese engineering student built a Claude powered device the size of a credit card. Raspberry Pi. Camera. One red LED. Total cost $14. His professor called it a toy. His classmates laughed. He posted a demo on YouTube and got 200 views. Then someone noticed what the device was actually connected to. A wallet. Making $11 million. In 10 months. kch123. $11,525,240 profit. 2,347 predictions. Joined June 2025. → Profile: The device does one thing. He asks it a question with his voice. Claude scans thousands of prediction markets in real time. When it finds a price that doesn't match reality the red LED lights up. Red light. He places the trade. Green result. $2M position on Seahawks. Won. $1.5M on Villarreal not winning. His professor saw the YouTube video again three months later. The demo was the same. The view count wasn't. Neither was the wallet. He asked the student what changed. The student showed him the balance. The professor didn't say anything. Sat down. Then asked if the device could run two accounts. 759K people watching. $319K still loaded in active positions. The device fits in your hand. The LED is still blinking. His classmates stopped laughing around month four. By month six two of them had built their own.
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A Chinese engineering student built a Claude powered device the size of a credit card. Raspberry Pi. Camera. One red LED. Total cost $14. His professor called it a toy. His classmates laughed. He posted a demo on YouTube and got 200 views. Then someone noticed what the device was actually connected to. A wallet. Making $11 million. In 10 months. kch123. $11,525,240 profit. 2,347 predictions. Joined June 2025. → Profile: The device does one thing. He asks it a question with his voice. Claude scans thousands of prediction markets in real time. When it finds a price that doesn't match reality the red LED lights up. Red light. He places the trade. Green result. $2M position on Seahawks. Won. $1.5M on Villarreal not winning. His professor saw the YouTube video again three months later. The demo was the same. The view count wasn't. Neither was the wallet. He asked the student what changed. The student showed him the balance. The professor didn't say anything. Sat down. Then asked if the device could run two accounts. 759K people watching. $319K still loaded in active positions. The device fits in your hand. The LED is still blinking. His classmates stopped laughing around month four. By month six two of them had built their own.

Carver

790,327 views • 3 months ago

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
0:31

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

Dustin

535,304 views • 4 months ago

THIS GUY CONNECTED CLAUDE VOICE MODE TO CLAUDE CODE AND NOW HE CODES BY TALKING INTO HIS AIRPODS while doing whatever he wants he uses Apple Reminders as a bridge between the two he talks into his AirPods, voice mode puts the prompt into a reminders list, Claude Code checks every 60 seconds and picks up the task AND it starts working on it back at his desk. walk outside. brainstorm with voice. code writes itself on your computer at home. he said he does his best thinking while walking and this lets him act on it instantly without pulling out his phone or sitting down. not a developer btw and he vibe coded the whole bridge system too. this is what hands-free vibe coding actually looks like
3:00

THIS GUY CONNECTED CLAUDE VOICE MODE TO CLAUDE CODE AND NOW HE CODES BY TALKING INTO HIS AIRPODS while doing whatever he wants he uses Apple Reminders as a bridge between the two he talks into his AirPods, voice mode puts the prompt into a reminders list, Claude Code checks every 60 seconds and picks up the task AND it starts working on it back at his desk. walk outside. brainstorm with voice. code writes itself on your computer at home. he said he does his best thinking while walking and this lets him act on it instantly without pulling out his phone or sitting down. not a developer btw and he vibe coded the whole bridge system too. this is what hands-free vibe coding actually looks like

Om Patel

151,021 views • 3 months ago

The homeless crisis in Santa Monica, CA is getting so bad that one homeowner invented his own homeless deterrent device. Santa Monica man Stephen McMahon invented a "diversion security" device that he is using to move homeless people out of his 20-block radius. The device detects when homeless people are in the area and starts chirping with a blue light. McMahon says he uses blue light because it tricks the brain into thinking it is daytime. He also says the blue light makes it so the homeless people can't see their veins to sh**t up drugs. McMahon hopes to install the Blue Chirper throughout his entire community to drive out the homeless. "I want to push these people out of here and bring Santa Monica back to the way it was."
2:33

The homeless crisis in Santa Monica, CA is getting so bad that one homeowner invented his own homeless deterrent device. Santa Monica man Stephen McMahon invented a "diversion security" device that he is using to move homeless people out of his 20-block radius. The device detects when homeless people are in the area and starts chirping with a blue light. McMahon says he uses blue light because it tricks the brain into thinking it is daytime. He also says the blue light makes it so the homeless people can't see their veins to sh**t up drugs. McMahon hopes to install the Blue Chirper throughout his entire community to drive out the homeless. "I want to push these people out of here and bring Santa Monica back to the way it was."

Collin Rugg

6,864,762 views • 1 year ago

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.
1:54

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,334 views • 27 days ago