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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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11,375 views • 4 days 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.

57,251 views • 12 days 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

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.
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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

274,518 views • 1 month 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

94,334 views • 3 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

59,409 views • 2 months 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

53,470 views • 3 months 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 • 2 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

317,731 views • 1 month 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 • 2 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

192,966 views • 11 days 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

533,600 views • 2 months 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.
0:47

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

790,041 views • 2 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

150,694 views • 2 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

6,863,761 views • 1 year ago

8-year-old Laksh is building Hardvare, an agent that builds your hardware over a phone call. (Listen) No software. No IDEs. No libraries. Just call. Say what you want to build. The agent validates pins, writes the code, and flashes the device. One call. Done. (Video - part 1)
1:58

8-year-old Laksh is building Hardvare, an agent that builds your hardware over a phone call. (Listen) No software. No IDEs. No libraries. Just call. Say what you want to build. The agent validates pins, writes the code, and flashes the device. One call. Done. (Video - part 1)

Capt Venkat 🇮🇳

66,305 views • 3 months ago

Luis Arraez talking through his routine and the importance of keeping his head down. No selling out to hit PRs in the cage. Training consistency that shows up in the game. Does the same routine EVERY DAY. It takes what it takes.
0:46

Luis Arraez talking through his routine and the importance of keeping his head down. No selling out to hit PRs in the cage. Training consistency that shows up in the game. Does the same routine EVERY DAY. It takes what it takes.

Xen Penny

120,852 views • 1 year ago

Marc Andreessen on Elon Musk: “Why don't more CEOs operate the way that he does? The top-line thing is just this incredible devotion from the leader of the company to deeply understand what the company does, and to be completely knowledgeable about every aspect of it. To be in the trenches and talking directly to the people who do the work, deeply understanding the issues, and being the key, lead problem solver in the organization. What Elon does is he shows up every week at each of his companies, he identifies the biggest problem that the company's having that week, and he fixes it. He does that every week for 52 weeks in a row. And then each of his companies has solved the 52 biggest problems that year, in that year. He attracts many of the best people in the world to work with him."
2:24

Marc Andreessen on Elon Musk: “Why don't more CEOs operate the way that he does? The top-line thing is just this incredible devotion from the leader of the company to deeply understand what the company does, and to be completely knowledgeable about every aspect of it. To be in the trenches and talking directly to the people who do the work, deeply understanding the issues, and being the key, lead problem solver in the organization. What Elon does is he shows up every week at each of his companies, he identifies the biggest problem that the company's having that week, and he fixes it. He does that every week for 52 weeks in a row. And then each of his companies has solved the 52 biggest problems that year, in that year. He attracts many of the best people in the world to work with him."

DogeDesigner

24,515 views • 4 months ago

Brett Favre once explained his practice mentality in one sentence: “As far back as I can remember, when I went out to practice, every throw that I made, I wanted it to be the best throw you’ve ever seen.” That is not a casual practice mindset. That is a standard. Favre did not become one of the greats because every throw was perfect. He became one of the greats because every throw mattered to him. Practice was not just a place to get through the script, warm up the arm, or wait for Sunday. It was where he trained his intent, his confidence, and his competitive identity. Quarterbacks reveal themselves in practice. Ball carriage discipline, base integrity, stride control, repeatable stroke, and accuracy all show up before the ball is ever judged by the result. When a quarterback treats every rep like evidence, the room feels it. The receivers feel it. The coaches feel it. That level of determination compounds. Greatness is rarely built in the highlight. It is usually built on a Tuesday, during a routine throw, when nobody in the stands is watching and the quarterback still demands the ball leave his hand with purpose.
0:43

Brett Favre once explained his practice mentality in one sentence: “As far back as I can remember, when I went out to practice, every throw that I made, I wanted it to be the best throw you’ve ever seen.” That is not a casual practice mindset. That is a standard. Favre did not become one of the greats because every throw was perfect. He became one of the greats because every throw mattered to him. Practice was not just a place to get through the script, warm up the arm, or wait for Sunday. It was where he trained his intent, his confidence, and his competitive identity. Quarterbacks reveal themselves in practice. Ball carriage discipline, base integrity, stride control, repeatable stroke, and accuracy all show up before the ball is ever judged by the result. When a quarterback treats every rep like evidence, the room feels it. The receivers feel it. The coaches feel it. That level of determination compounds. Greatness is rarely built in the highlight. It is usually built on a Tuesday, during a routine throw, when nobody in the stands is watching and the quarterback still demands the ball leave his hand with purpose.

Nate Longshore

103,592 views • 1 month ago

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.
0:33

A guy put an XFRA box outside his house and now gets paid $2,500 a month for doing nothing The box is about the size of an AC unit and mounts right on the wall. Inside it connects your home to a distributed AI compute network. Companies pay to run their AI workloads through it around the clock. He just plugged it in and that was it. Now $2,500 lands in his account every single month. That is $30,000 a year from a box he never touches. It is not Nvidia. It is not a government program. It is XFRA and regular homes are quietly becoming AI infrastructure. Every house a node. Every node earning. The people who move first lock in the best rates. Save this before your neighborhood figures it out before you.

winkle.

16,624 views • 10 days ago

From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”
2:22

From Dan Lorenc on the malware attack that almost took down the entire internet last year: “There’s a popular compression library that’s used in almost every piece of software. And it had been maintained by one person in his spare time for the last 20 years. And then a couple years ago, somebody just decided to start helping him. They jumped in, fixed a bunch of bugs, and did a lot of great work. And then that first person got tired of working on it. So he handed the whole project over to this other person. It turned out that other person was just a pseudonym and was not a real person. And within six months of getting control of the project, they had put in a carefully orchestrated set of malware that was really hard to detect and no one noticed. And because it was so widely used, the exploit would've basically given that person remote access to any computer running that piece of software, which was basically everything connected to the Internet. But because it was open source and the code was transparent, some random engineer just happened to be running some benchmarks on a weekend. And he noticed that program was a little bit slower than it used to be, and that it was making a weird cryptographic operation to check something. And right before this thing got widely deployed across every device, he dug in, and discovered that there was a backdoor put in. This was the closest thing to a full-blown internet crisis that we’ve ever had. And they still have no idea who did it. It was just an anonymous email account. No one ever traced it back to an individual. And that's the long game. This person spent years just doing good work and earning the communities trust.”

The Peel

47,683 views • 1 year ago