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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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Cortex

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11,462 просмотров • 3 месяцев назад •via X (Twitter)

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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 просмотров • 5 месяцев назад

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

276,199 просмотров • 4 месяцев назад

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

94,485 просмотров • 6 месяцев назад

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.
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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 просмотров • 5 месяцев назад

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.
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Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 просмотров • 13 дней назад

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.
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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 просмотров • 6 месяцев назад

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

318,711 просмотров • 5 месяцев назад

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
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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

397,670 просмотров • 2 месяцев назад

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 просмотров • 5 месяцев назад

STARLINK CHARGES $120/MO TO GET ONLINE OFF-GRID. HE DOES IT FOR $0 - A $30 PANEL ANTENNA ON A MAST REACHING A USED GPU 80KM OUT. RUNS OPUS-5 AND GPT-5.6 LOCALLY he clamped a directional panel and a tp-link adapter to a telescoping mast and ran it up past the roofline, whole rig cost him $30 in used marketplace parts the higher it goes the farther it hears -> it locks a node 80km out -> the prompt tunnels down to the used gpu in his closet -> a local kimi k3 answers before the page finishes loading one node it kept pulling in stayed online through last month's grid blackout, turned out to be a repeater on a water tower a county away running off a small solar pack height is the whole cheat, lift a directional antenna over the treeline and line-of-sight does the rest, every node it reaches is just another road home to his own gpu $200 opus-5 and $200 gpt-5.6 pro stopped hitting his card the week that mast went up, a $30 antenna and a used gpu do what anthropic and openai charged $400 a month for full breakdown of the 5-device stack that zeroes your ai bill in the article below
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STARLINK CHARGES $120/MO TO GET ONLINE OFF-GRID. HE DOES IT FOR $0 - A $30 PANEL ANTENNA ON A MAST REACHING A USED GPU 80KM OUT. RUNS OPUS-5 AND GPT-5.6 LOCALLY he clamped a directional panel and a tp-link adapter to a telescoping mast and ran it up past the roofline, whole rig cost him $30 in used marketplace parts the higher it goes the farther it hears -> it locks a node 80km out -> the prompt tunnels down to the used gpu in his closet -> a local kimi k3 answers before the page finishes loading one node it kept pulling in stayed online through last month's grid blackout, turned out to be a repeater on a water tower a county away running off a small solar pack height is the whole cheat, lift a directional antenna over the treeline and line-of-sight does the rest, every node it reaches is just another road home to his own gpu $200 opus-5 and $200 gpt-5.6 pro stopped hitting his card the week that mast went up, a $30 antenna and a used gpu do what anthropic and openai charged $400 a month for full breakdown of the 5-device stack that zeroes your ai bill in the article below

starmex

144,506 просмотров • 1 месяц назад

SemiAnalysis' Jordan Nanos reveals the OpenAI engineers scrolling their own kernel code had no idea what it did line by line, and it didn't matter because the AI does "It's not slop, because it performs, but it's literally just AI-generated assembly basically puked out in Gluon, this low-level kernel programming language that they built on top of Triton, which uses this really interesting programming model." "The point is, the guys who were scrolling through this code with us, it was kind of clear that they know a whole bunch about hardware, they know a whole bunch about the concepts in the system, and they just have no idea what this MLA kernel that they're showing us for DeepSeek actually does." "You can go line by line and it's like nope, nope, nope." "But it doesn't matter. The AI understands it, the AI tests it, and you see the results. It produces correct kernels that perform really well." "And I think this is just a sign of what's to come again." "That actual code is not necessarily something that a human has to reason about deeply, if the AI knows how to manipulate the data movement and the processing elements on the hardware that you've given it." ________ More takeaways from OpenAI's Jalapeno development:
1:11

SemiAnalysis' Jordan Nanos reveals the OpenAI engineers scrolling their own kernel code had no idea what it did line by line, and it didn't matter because the AI does "It's not slop, because it performs, but it's literally just AI-generated assembly basically puked out in Gluon, this low-level kernel programming language that they built on top of Triton, which uses this really interesting programming model." "The point is, the guys who were scrolling through this code with us, it was kind of clear that they know a whole bunch about hardware, they know a whole bunch about the concepts in the system, and they just have no idea what this MLA kernel that they're showing us for DeepSeek actually does." "You can go line by line and it's like nope, nope, nope." "But it doesn't matter. The AI understands it, the AI tests it, and you see the results. It produces correct kernels that perform really well." "And I think this is just a sign of what's to come again." "That actual code is not necessarily something that a human has to reason about deeply, if the AI knows how to manipulate the data movement and the processing elements on the hardware that you've given it." ________ More takeaways from OpenAI's Jalapeno development:

Fireside Alpha

4,520,070 просмотров • 16 дней назад

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 просмотров • 5 месяцев назад

🚨 A WOMAN IN IRAN JUST PAINTED THIS ON A WALL AND SENT IT TO ME: TRUMP, MEET PAHLAVI. It reached me a few hours ago. She went out in the dark, wrote it herself, filmed it, and said it out loud: "Trump, meet Pahlavi. In memory of those killed on the 8th and 9th of January." The 8th and 9th of January are the two nights the regime shot people in the streets and shut down the internet of an entire country so that nobody could film what they were doing. That is who she dedicated the wall to. She keeps her own face out of the shot and keeps the street in it, so nobody can tell you this is not Iran. She is not asking for another round of talks with the men who did that. She is asking the President of the United States to meet Prince Reza Pahlavi, and to say out loud that he is the man the Iranian people have chosen. It would take him one afternoon. It is also the single thing this regime fears more than anything else. Iranians already chose. The only one missing is Trump. Do you agree? Tell me why or why not. I read every reply. #TrumpMeetPahlavi
0:36

🚨 A WOMAN IN IRAN JUST PAINTED THIS ON A WALL AND SENT IT TO ME: TRUMP, MEET PAHLAVI. It reached me a few hours ago. She went out in the dark, wrote it herself, filmed it, and said it out loud: "Trump, meet Pahlavi. In memory of those killed on the 8th and 9th of January." The 8th and 9th of January are the two nights the regime shot people in the streets and shut down the internet of an entire country so that nobody could film what they were doing. That is who she dedicated the wall to. She keeps her own face out of the shot and keeps the street in it, so nobody can tell you this is not Iran. She is not asking for another round of talks with the men who did that. She is asking the President of the United States to meet Prince Reza Pahlavi, and to say out loud that he is the man the Iranian people have chosen. It would take him one afternoon. It is also the single thing this regime fears more than anything else. Iranians already chose. The only one missing is Trump. Do you agree? Tell me why or why not. I read every reply. #TrumpMeetPahlavi

Armin Navabi

43,082 просмотров • 4 дней назад

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 просмотров • 2 месяцев назад

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

194,375 просмотров • 3 месяцев назад

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.
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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

89,575 просмотров • 2 месяцев назад

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

790,401 просмотров • 5 месяцев назад

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.
1:31

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.

Born to gamble

347,428 просмотров • 3 месяцев назад

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

536,136 просмотров • 5 месяцев назад