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This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+...

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Ridark

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211,874 次观看 • 1 个月前 •via X (Twitter)

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This is what AI subscription companies are afraid of. A guy with a screwdriver, a $700 pile of components, and one free weekend. They built their entire business model on you never figuring out that the same models run locally. On hardware you own. No rate limits. No data leaving the room. No invoice at the end of the month. He figured it out on a Saturday. By Sunday night six agents were online. Research, writing, QA, publishing, monitoring. All running locally for $23 a month in electricity. By the following month he had eight clients paying $2,500 each to run the same system for their businesses. $20,000 a month. Built on a PC he assembled himself in a weekend. They're still sending him renewal emails. He stopped opening them.
0:45

This is what AI subscription companies are afraid of. A guy with a screwdriver, a $700 pile of components, and one free weekend. They built their entire business model on you never figuring out that the same models run locally. On hardware you own. No rate limits. No data leaving the room. No invoice at the end of the month. He figured it out on a Saturday. By Sunday night six agents were online. Research, writing, QA, publishing, monitoring. All running locally for $23 a month in electricity. By the following month he had eight clients paying $2,500 each to run the same system for their businesses. $20,000 a month. Built on a PC he assembled himself in a weekend. They're still sending him renewal emails. He stopped opening them.

Superior

22,686 次观看 • 15 天前

Microsoft spent $13 billion and 3 years building an AI that knows your work context. Every time you open it, it still asks what you're working on. This developer set up a plain text file in 2 minutes. The file is called CLAUDE.md. It loads before every session. Before he types a single word. It already knows his name. It already knows his writing style. It already knows what he's building, who it's for, and what he never wants to see in a response. He doesn't introduce himself anymore. He doesn't explain his preferences anymore. He doesn't correct the same mistakes twice. He just works. No $30/month Copilot subscription. No Microsoft 365. No IT approval. No data sharing agreement. No onboarding. Just a plain text file, a free text editor, and 21 instructions a developer distilled from Andrej Karpathy's research. Those 21 instructions moved Claude's coding accuracy from 65% to 94%. The file hit #1 on GitHub with 82,000 stars. Most people using Claude right now have never heard of it. Microsoft has 221,000 employees, $13 billion invested in OpenAI, and a direct integration into every Windows laptop sold on the planet.. they built an AI assistant most companies pay $30/user/month for that still doesn't know your name. This developer has a laptop, a text file and a 2-minute setup.. he built something that knows more about how he works than any enterprise AI on the market. The $50 billion AI personalization industry just got embarrassed by a .md file. full breakdown down below
0:44

Microsoft spent $13 billion and 3 years building an AI that knows your work context. Every time you open it, it still asks what you're working on. This developer set up a plain text file in 2 minutes. The file is called CLAUDE.md. It loads before every session. Before he types a single word. It already knows his name. It already knows his writing style. It already knows what he's building, who it's for, and what he never wants to see in a response. He doesn't introduce himself anymore. He doesn't explain his preferences anymore. He doesn't correct the same mistakes twice. He just works. No $30/month Copilot subscription. No Microsoft 365. No IT approval. No data sharing agreement. No onboarding. Just a plain text file, a free text editor, and 21 instructions a developer distilled from Andrej Karpathy's research. Those 21 instructions moved Claude's coding accuracy from 65% to 94%. The file hit #1 on GitHub with 82,000 stars. Most people using Claude right now have never heard of it. Microsoft has 221,000 employees, $13 billion invested in OpenAI, and a direct integration into every Windows laptop sold on the planet.. they built an AI assistant most companies pay $30/user/month for that still doesn't know your name. This developer has a laptop, a text file and a 2-minute setup.. he built something that knows more about how he works than any enterprise AI on the market. The $50 billion AI personalization industry just got embarrassed by a .md file. full breakdown down below

Dep

14,042 次观看 • 2 个月前

A 22-year-old automated his entire YouTube channel using Claude. $8,000 a month. He doesn't even touch the videos. No camera. No editing. No burnout. Just Claude. He opens it once and tells it what to do. Claude handles everything: > finds trending topics in his niche > writes titles and descriptions built for the algorithm > generates full video scripts > creates tags, timestamps, promotional posts One prompt. One channel. One person. $8,000 a month running on autopilot. The channel works while he sleeps. While he eats. While he lives his life. Most people still think YouTube means filming, editing, grinding for years before seeing a dollar. He just taught an AI to do it for him. And while you're reading this — his channel is already getting views. The craziest part? He's not a developer. Not a tech guy. Not some Silicon Valley genius. He's just someone who stopped overcomplicating it. The tools exist. The opportunity is wide open. The only question is why it's still not you.
1:28

A 22-year-old automated his entire YouTube channel using Claude. $8,000 a month. He doesn't even touch the videos. No camera. No editing. No burnout. Just Claude. He opens it once and tells it what to do. Claude handles everything: > finds trending topics in his niche > writes titles and descriptions built for the algorithm > generates full video scripts > creates tags, timestamps, promotional posts One prompt. One channel. One person. $8,000 a month running on autopilot. The channel works while he sleeps. While he eats. While he lives his life. Most people still think YouTube means filming, editing, grinding for years before seeing a dollar. He just taught an AI to do it for him. And while you're reading this — his channel is already getting views. The craziest part? He's not a developer. Not a tech guy. Not some Silicon Valley genius. He's just someone who stopped overcomplicating it. The tools exist. The opportunity is wide open. The only question is why it's still not you.

Shelpid.WI3M

181,887 次观看 • 2 个月前

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.
1:24

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,403 次观看 • 6 个月前

A 16-year-old American student made $7,000 in his first month after publishing his first game on Google Play - and GPT-5.5 built the whole thing for him. He had zero programming knowledge and no $50,000 dev team. He just opened GPT-5.5, described what he wanted to build, and went to play while the AI wrote every line of code for him. GPT-5.5 built a complete 3D game called Hormuz Escape from scratch - obstacle avoidance mechanics, high score system, particle effects, AdMob integration for ads, and a Firebase leaderboard. All from one prompt in a week of work. A Google Play Developer account cost $25 once and forever. 10,000 active players at $0.03 a day through AdMob is already $9,000 a month in passive income. He stopped at $7,000 in the first month and the number keeps growing. 3 billion Android devices in the world and the Play Store is open to anyone with $25 and an idea. AI removed the last barrier - needing to know how to code. $25 to register, One week of work. $7,000 in the first month.
1:00

A 16-year-old American student made $7,000 in his first month after publishing his first game on Google Play - and GPT-5.5 built the whole thing for him. He had zero programming knowledge and no $50,000 dev team. He just opened GPT-5.5, described what he wanted to build, and went to play while the AI wrote every line of code for him. GPT-5.5 built a complete 3D game called Hormuz Escape from scratch - obstacle avoidance mechanics, high score system, particle effects, AdMob integration for ads, and a Firebase leaderboard. All from one prompt in a week of work. A Google Play Developer account cost $25 once and forever. 10,000 active players at $0.03 a day through AdMob is already $9,000 a month in passive income. He stopped at $7,000 in the first month and the number keeps growing. 3 billion Android devices in the world and the Play Store is open to anyone with $25 and an idea. AI removed the last barrier - needing to know how to code. $25 to register, One week of work. $7,000 in the first month.

Sprytix

85,583 次观看 • 2 个月前

JENSEN HUANG PUT 1 PETAFLOP AND 128GB OF GPU MEMORY IN A $150,000 BOX. THE $249 JETSON RUNS THE SAME MODELS AND CUTS A $200/MONTH AI BILL TO $2 IN ELECTRICITY the $249 jetson orin nano super runs llama 3, mistral and deepseek locally at 67 trillion operations per second. zero api fees, zero data leaving your machine, $2 a month in electricity andrej karpathy says 99% of ai users are missing 7 basics that have nothing to do with the hardware. a proper config file, a /raw and /wiki folder, saved reference pages. the model stops guessing and starts helping most people pay $200 a month and still get bad output because the problem was never the model. it was the workflow around it. fix the system and a $249 box handles 80% of what you open chatgpt for the jetson breaks even in 6 weeks at $100/month api spend. after that every month is $98 back in your pocket. first year that's over $1,000 saved from a one-time $249 purchase the people building local ai infrastructure in 2026 with a proper workflow are going to look very far ahead in 2028 bookmark this and read the article below
0:33

JENSEN HUANG PUT 1 PETAFLOP AND 128GB OF GPU MEMORY IN A $150,000 BOX. THE $249 JETSON RUNS THE SAME MODELS AND CUTS A $200/MONTH AI BILL TO $2 IN ELECTRICITY the $249 jetson orin nano super runs llama 3, mistral and deepseek locally at 67 trillion operations per second. zero api fees, zero data leaving your machine, $2 a month in electricity andrej karpathy says 99% of ai users are missing 7 basics that have nothing to do with the hardware. a proper config file, a /raw and /wiki folder, saved reference pages. the model stops guessing and starts helping most people pay $200 a month and still get bad output because the problem was never the model. it was the workflow around it. fix the system and a $249 box handles 80% of what you open chatgpt for the jetson breaks even in 6 weeks at $100/month api spend. after that every month is $98 back in your pocket. first year that's over $1,000 saved from a one-time $249 purchase the people building local ai infrastructure in 2026 with a proper workflow are going to look very far ahead in 2028 bookmark this and read the article below

starmex

87,065 次观看 • 1 个月前

Pieter Levels makes over $250,000 a month -> alone, from a laptop, with zero employees no co-founder. no investors. no team. just him and a backpack, moving between countries he started by daring himself to launch 12 startups in 12 months. then he kept going -> 70+ products over the years, and most of them flopped he doesn't plan. no roadmaps, no polishing for months. he ships before it's "ready" and kills anything that gets no traction within weeks one throwaway experiment -> an AI photo app he built the day after a tweet went viral -> hit $77,000/month within a year. today it does over $130,000/month on its own he still writes everything in old-school PHP. every error gets piped straight to his Telegram. ~95% of the revenue is pure profit even Andrej Karpathy said this is the model for the future: one person running multiple companies the clip below is him explaining exactly how ↓
36:41

Pieter Levels makes over $250,000 a month -> alone, from a laptop, with zero employees no co-founder. no investors. no team. just him and a backpack, moving between countries he started by daring himself to launch 12 startups in 12 months. then he kept going -> 70+ products over the years, and most of them flopped he doesn't plan. no roadmaps, no polishing for months. he ships before it's "ready" and kills anything that gets no traction within weeks one throwaway experiment -> an AI photo app he built the day after a tweet went viral -> hit $77,000/month within a year. today it does over $130,000/month on its own he still writes everything in old-school PHP. every error gets piped straight to his Telegram. ~95% of the revenue is pure profit even Andrej Karpathy said this is the model for the future: one person running multiple companies the clip below is him explaining exactly how ↓

Ridark

479,487 次观看 • 1 个月前

This guy makes $80,000 a month working 30 hours a week. He launched his business on a Sunday and had $10,000 in recurring revenue by Monday. I know what you're thinking and no-- - He didn't have an audience. - He didn't have followers. And he kept his full-time job for four years while running this on the side. He was making more per month in profit than he was making per year at his day job. At one point he was bringing in $200,000 a month with only $4,000 in expenses. Startup cost? He built the entire thing in a weekend using Webflow and launched it on Product Hunt. That's it. What is it? A productized design agency called Design Joy. He sells unlimited design work as a monthly subscription. No contracts. No calls. And Clients pay $6,000 a month and get designs back in a day. In this episode Brett: - Breaks down how he went from $449/month to $8,000/month per client - Shows me the AI tools he uses to work faster than traditional agencies - Tells me why speed matters more than quality - Gives me two productized service ideas anyone can copy right now - Explains why he thinks branding agencies are the next big opportunity Certified banger. Brett, thank you for coming on!
1:28

This guy makes $80,000 a month working 30 hours a week. He launched his business on a Sunday and had $10,000 in recurring revenue by Monday. I know what you're thinking and no-- - He didn't have an audience. - He didn't have followers. And he kept his full-time job for four years while running this on the side. He was making more per month in profit than he was making per year at his day job. At one point he was bringing in $200,000 a month with only $4,000 in expenses. Startup cost? He built the entire thing in a weekend using Webflow and launched it on Product Hunt. That's it. What is it? A productized design agency called Design Joy. He sells unlimited design work as a monthly subscription. No contracts. No calls. And Clients pay $6,000 a month and get designs back in a day. In this episode Brett: - Breaks down how he went from $449/month to $8,000/month per client - Shows me the AI tools he uses to work faster than traditional agencies - Tells me why speed matters more than quality - Gives me two productized service ideas anyone can copy right now - Explains why he thinks branding agencies are the next big opportunity Certified banger. Brett, thank you for coming on!

Chris Koerner

375,377 次观看 • 2 个月前

19-year-old Vietnamese student built himself a second brain through Claude and Obsidian - and made $18,000 in his first month. Every morning Claude already knows who he is, what he's working on, which tasks are open and which ideas were captured a month ago - no reminders, no manual searching. He drops an article into a folder - Claude reads it, writes a summary, updates the index and links 10-15 related pages in one pass. Set up a cron job once at 7:30 AM - now a briefing with all his tasks and new materials is waiting before he even gets out of bed. Published the system as a course on Gumroad - $97 for access, 186 sales in the first month. Vannevar Bush described this idea back in 1945 and called it the Memex - but couldn't solve one question: who does the maintenance. Now the answer is known - Claude. And a 19-year-old student from Hanoi is already selling that answer for $18,000 a month.
5:00

19-year-old Vietnamese student built himself a second brain through Claude and Obsidian - and made $18,000 in his first month. Every morning Claude already knows who he is, what he's working on, which tasks are open and which ideas were captured a month ago - no reminders, no manual searching. He drops an article into a folder - Claude reads it, writes a summary, updates the index and links 10-15 related pages in one pass. Set up a cron job once at 7:30 AM - now a briefing with all his tasks and new materials is waiting before he even gets out of bed. Published the system as a course on Gumroad - $97 for access, 186 sales in the first month. Vannevar Bush described this idea back in 1945 and called it the Memex - but couldn't solve one question: who does the maintenance. Now the answer is known - Claude. And a 19-year-old student from Hanoi is already selling that answer for $18,000 a month.

Cortex

24,389 次观看 • 2 个月前

Do you actually understand what happened to Twitter's servers. He found one listed online for almost nothing. Bought it before anyone else figured out what it was. Spent a weekend putting it together in an old case he had sitting in the garage. He bought one. Paired it with 512 gigabytes of RAM across eight channels. Dropped in a 3090 for image and video generation on the side. The setup runs DeepSeek 671B, the entire model, no quantization tricks, loaded fully into memory. The people selling $412/month AI subscriptions are running hardware like this in data centers and charging you monthly to access a fraction of it. He just owns his fraction now.
2:51

Do you actually understand what happened to Twitter's servers. He found one listed online for almost nothing. Bought it before anyone else figured out what it was. Spent a weekend putting it together in an old case he had sitting in the garage. He bought one. Paired it with 512 gigabytes of RAM across eight channels. Dropped in a 3090 for image and video generation on the side. The setup runs DeepSeek 671B, the entire model, no quantization tricks, loaded fully into memory. The people selling $412/month AI subscriptions are running hardware like this in data centers and charging you monthly to access a fraction of it. He just owns his fraction now.

Superior

73,266 次观看 • 16 天前

THIS CHINESE DEVELOPER VISUALIZED WHAT 300 KIMI K2.6 AGENTS LOOK LIKE IN ACTION - AND IT LOOKS EXACTLY LIKE A BRAIN WORKING FOR YOU every line on screen is a connection firing in real time - hundreds of neurons across multiple layers, activations lighting up, signals passing through the network simultaneously in both directions this is not a diagram and not a concept - this is the actual mechanics of what happens inside the model every time it processes your request now multiply that by 300 parallel agents running 4,000 coordinated steps at the same time - while you drink coffee the entire system fires neurons and does the work for you a team paying $62,000/month on Claude Opus cut their bill to $129 by switching to Kimi K2.6 as the execution layer - Opus plans, Kimi executes, $54,000/month stays in the business what looks like fire on screen is your new employee who never sleeps, never asks for a raise and never goes on vacation most people pay for subscriptions that forget everything tomorrow - he built a system that works and compounds while he sleeps
0:30

THIS CHINESE DEVELOPER VISUALIZED WHAT 300 KIMI K2.6 AGENTS LOOK LIKE IN ACTION - AND IT LOOKS EXACTLY LIKE A BRAIN WORKING FOR YOU every line on screen is a connection firing in real time - hundreds of neurons across multiple layers, activations lighting up, signals passing through the network simultaneously in both directions this is not a diagram and not a concept - this is the actual mechanics of what happens inside the model every time it processes your request now multiply that by 300 parallel agents running 4,000 coordinated steps at the same time - while you drink coffee the entire system fires neurons and does the work for you a team paying $62,000/month on Claude Opus cut their bill to $129 by switching to Kimi K2.6 as the execution layer - Opus plans, Kimi executes, $54,000/month stays in the business what looks like fire on screen is your new employee who never sleeps, never asks for a raise and never goes on vacation most people pay for subscriptions that forget everything tomorrow - he built a system that works and compounds while he sleeps

Noisy

138,389 次观看 • 1 个月前

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.
0:27

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

Sudo su

36,251 次观看 • 4 个月前

A 23-year-old made $5,000 last month from AI. No employees. No office. He didn't write a single line of code. While you're still "thinking about getting into AI," he picked three tiny lanes and quietly took them. Lane 1 — Micro SaaS. He found one thing people pay to do by hand. Built one AI tool that does it in a click. Not a platform. One feature. $19/month, 300 people paying, and he hasn't touched it in weeks. Lane 2 — Productized AI agents. He built an AI agent that answers a dentist's phone, books the patient, and texts the confirmation. Set up once. The clinic pays $1,200 a month to keep it alive. He has nine of them now. He's never met a single patient. Lane 3 — Prompt libraries. He packaged the prompts that actually work "30 hooks that go viral," "50 cold emails that get replies" - into one file. Made it once. Sells it forever. Same file, every day, while he sleeps. Now the part that should sting a little. He didn't invent anything. The tools are free. The skills are free. AI does the hard part now. The only thing left to own is the niche and there are millions of them sitting wide open while everyone fights to build the next big app nobody asked for. He's not smarter than you. He just stopped scrolling and picked a lane. You're still scrolling.
0:49

A 23-year-old made $5,000 last month from AI. No employees. No office. He didn't write a single line of code. While you're still "thinking about getting into AI," he picked three tiny lanes and quietly took them. Lane 1 — Micro SaaS. He found one thing people pay to do by hand. Built one AI tool that does it in a click. Not a platform. One feature. $19/month, 300 people paying, and he hasn't touched it in weeks. Lane 2 — Productized AI agents. He built an AI agent that answers a dentist's phone, books the patient, and texts the confirmation. Set up once. The clinic pays $1,200 a month to keep it alive. He has nine of them now. He's never met a single patient. Lane 3 — Prompt libraries. He packaged the prompts that actually work "30 hooks that go viral," "50 cold emails that get replies" - into one file. Made it once. Sells it forever. Same file, every day, while he sleeps. Now the part that should sting a little. He didn't invent anything. The tools are free. The skills are free. AI does the hard part now. The only thing left to own is the niche and there are millions of them sitting wide open while everyone fights to build the next big app nobody asked for. He's not smarter than you. He just stopped scrolling and picked a lane. You're still scrolling.

Rich

11,331 次观看 • 1 个月前

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.
8:02

Alibaba just dropped a free AI that codes like a $150/hr developer. And it runs on your laptop. Qwen 3 Coder Next has 80 billion parameters but only uses 3 billion at a time. So you get massive model power with tiny model speed. The real kicker? It handles 256,000 tokens of context. That's your entire codebase. Every file. All at once. People are building 2D games in seconds. Full IDE integrations that write, test, and fix code automatically. Custom business tools without hiring devs. It works with OpenClaw, Claude Code, and every major coding tool. No complicated setup. Just download and go. Completely open source. Zero API queues. You own it. Your competitors are using this right now.

Julian Goldie SEO

30,259 次观看 • 5 个月前

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.
0:55

THIS CHINESE BUILDER TURNED 8 RETIRED TESLA P40s INTO A 192GB PRIVATE AI SERVER THAT CAN WORK 5,760 GPU-HOURS EVERY MONTH. 00:06 he holds up one used Tesla P40 while seven more sit behind it, then installs the full stack inside a massive dual-Xeon server chassis. each card carries 24GB of VRAM, giving the system 192GB across eight GPUs for local models, video processing, transcription, private documents and AI agents. the real advantage is parallel work. some cards can serve a local model while the rest generate images, process files and run overnight automation at the same time. eight GPUs running continuously deliver 5,760 GPU-hours every 30 days. rent that workload from the cloud and the meter keeps moving for every minute the server stays online. a $412 monthly AI stack already costs $4,944 a year before serious GPU rental. he bought retired hardware once, kept the data local and built a machine that keeps working after the browser closes.

Gipp 🦅

42,332 次观看 • 17 天前

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 次观看 • 2 个月前

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.
1:16

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.

Blaze

93,219 次观看 • 1 个月前

18-year-old American found a roofing company on Google Maps with 4.9 stars and no website, copied their reviews and pasted them into ChatGPT 5.5. 2 minutes later - a complete brief. Pasted it into AI and just waited while the system built a full website with all pages, reviews and a booking button. Called the owner and showed him the live preview. He said yes immediately because he'd been meaning to fix this for years and never had the time. Invoice for $1,000. 47 minutes of work from the first search to a closed deal. Then he built a machine. AI pulls 200 businesses from Google Maps in 10 minutes, writes a personalized email for each one with their real business data - 500 emails a day, 3% respond. Month one - $4,000, month six - $15,000-20,000. Five million businesses on Google Maps are still waiting for that call.
0:47

18-year-old American found a roofing company on Google Maps with 4.9 stars and no website, copied their reviews and pasted them into ChatGPT 5.5. 2 minutes later - a complete brief. Pasted it into AI and just waited while the system built a full website with all pages, reviews and a booking button. Called the owner and showed him the live preview. He said yes immediately because he'd been meaning to fix this for years and never had the time. Invoice for $1,000. 47 minutes of work from the first search to a closed deal. Then he built a machine. AI pulls 200 businesses from Google Maps in 10 minutes, writes a personalized email for each one with their real business data - 500 emails a day, 3% respond. Month one - $4,000, month six - $15,000-20,000. Five million businesses on Google Maps are still waiting for that call.

Rahul

7,536,528 次观看 • 1 个月前

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch
47:23

Andrew Wilkinson owns 40+ businesses. He just showed me how he's using OpenClaw, Claude Code and AI agents to run latest business, start new ones, and automate everything. Here's what I learned: 1. In December 2025, something clicked. He started waking up at 3AM with a smile, sitting in terminal with 10 Claude Code tabs open. He hasn't stopped since. He calls it chasing the dragon. 2.He built a full SaaS product called Deep Personality. A 40-minute personality test that generates a 100-page report written like Robert Greene. $20 000 in revenue. Zero employees. The entire business runs on AI agents. 3. He has agents for support, marketing, and dev. When a support ticket comes in, the agent either handles it or sends it to the dev agent. If it's critical, the agent fixes the bug and merges the PR before he wakes up. Then it emails the customer back. 4. His marketing agent is connected to PostHog, manages Meta and Reddit ads, creates ad creative, runs multivariate tests, and sets budgets. He's about to give it a $100 k/month ad budget and see what happens. 5. He forgot his laptop on a trip to Arizona. He ran his entire business from the back of Ubers using OpenClaw. Nobody picked up that every single email was written by AI. 6. His take on vibe coding: the worst part about business is people. Between your vision and execution are 100 people you have to convince. Vibe coding removes all of them. For the first time he can do every part of building a product himself. 7. He was trying to build OpenClaw before OpenClaw existed. Now he uses a tool called Harbor, which is basically a GUI for managing multiple agents. You can see all your agents, their status, knowledge bases, and databases in one place. 8. He built a custom AI for his relationship. He and his girlfriend took 15 psychological tests, put the results into ChatGPT, and asked it to analyze their relationship. It nailed every fight they've ever had. That became the product idea for Deep Personality. 9. His honest take: he spends 50% of his time debugging, 30% improving the setup, and 20% being productive. It's a treadmill. But the 20% that works is so powerful he can't stop. 10. His prediction: we're 3-6 months from being able to hand basic businesses off to AI to run entirely. And pretty soon Anthropic and OpenAI are going to launch AI CEOs. This is an inside look at how a serious operator Andrew Wilkinson is using AI agents in the real world. The good, the bad, the debugging, all of it. Most people don't show you this. Episode is live on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

144,005 次观看 • 2 个月前