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HE STRAPPED A BATTERY TO A $599 MAC MINI AND TURNED A DESK COMPUTER INTO A 14-HOUR PORTABLE AI WORKSTATION 00:03 the battery slides onto the side of the mac mini and the whole setup stops behaving like a desk machine. now it can run from a backpack, power...

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Gipp 🦅

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1,788,765 次观看 • 2 个月前 •via X (Twitter)

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This guy from China makes $12,500 a month walking around crypto conferences with a backpack and a Mac Mini inside it. The setup is a Mac Mini running OpenClaw on a CUKTECH 10 Mini battery. Every other creator at these events waits until they're back at the hotel to process and upload. By then the alpha is gone and the thread is late. His portable AI catches keynote audio on the spot, extracts what matters and posts while everyone else is still in the room listening.
0:10

This guy from China makes $12,500 a month walking around crypto conferences with a backpack and a Mac Mini inside it. The setup is a Mac Mini running OpenClaw on a CUKTECH 10 Mini battery. Every other creator at these events waits until they're back at the hotel to process and upload. By then the alpha is gone and the thread is late. His portable AI catches keynote audio on the spot, extracts what matters and posts while everyone else is still in the room listening.

0xMarioNawfal

69,076 次观看 • 1 个月前

the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below
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the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below

John Doe

26,096 次观看 • 2 个月前

1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.
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1 mini PC that fits in a palm, with no monthly token bill attached to it. Most people run their AI through a browser tab. Every prompt, every document, every half-finished idea leaves the machine and lands on someone else's server. This ORICO mini PC runs the model locally. The assistant lives inside the box, not in a data center 2,000 miles away. Small enough to slide into a bag. Powerful enough that you stop noticing it is not a desktop. You pay once for hardware instead of every month for limits. No outage that takes your assistant down with it. No terms of service deciding what your files are worth. Plug it in anywhere, and the assistant is already there, offline, waiting. The smallest machine on the desk is the only one that keeps its mouth shut.

AiMind

82,128 次观看 • 15 天前

THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.
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THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.

Gipp 🦅

69,445 次观看 • 3 个月前

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.
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This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

Gipp 🦅

591,405 次观看 • 3 个月前

SEVEN RTX 3090S IN A WATER TANK FOR AI SERVER it is a private AI server with the power bill moved into your room. not a clean Mac mini. not a quiet box under a monitor. loose vertical GPUs sit inside a transparent tank. bubbles rise through distilled water. ALLIED CONTROL is printed on the side. it looks closer to a lab accident than a normal workstation. but the logic is obvious: seven RTX 3090s = seven 24GB cards. that is the used-market shortcut for people who want local inference without paying cloud tax on every run. put Ollama, llama.cpp, vLLM, Open WebUI, Tailscale, Qwen, DeepSeek, or Llama on top. now the box can handle client files, code agents, scraping jobs, evals, transcription, and boring overnight work. not because it beats frontier cloud models. because it changes the bill shape. no rate limit. no per-token anxiety. no sensitive client context leaving the building. no monthly stack quietly turning into rent. the ugly part is physical. seven 3090s can pull serious power, dump serious heat, and punish lazy cooling. distilled water is the weird visual, not a setup tip. real immersion rigs live or die on coolant chemistry, insulation, pumps, maintenance, and whether the room can handle the heat. local AI PCs are becoming less like gaming builds and more like small private data centers. the early question is not: can it run ChatGPT? it is: what work is repetitive, private, expensive in the cloud, and worth owning in hardware?
0:14

SEVEN RTX 3090S IN A WATER TANK FOR AI SERVER it is a private AI server with the power bill moved into your room. not a clean Mac mini. not a quiet box under a monitor. loose vertical GPUs sit inside a transparent tank. bubbles rise through distilled water. ALLIED CONTROL is printed on the side. it looks closer to a lab accident than a normal workstation. but the logic is obvious: seven RTX 3090s = seven 24GB cards. that is the used-market shortcut for people who want local inference without paying cloud tax on every run. put Ollama, llama.cpp, vLLM, Open WebUI, Tailscale, Qwen, DeepSeek, or Llama on top. now the box can handle client files, code agents, scraping jobs, evals, transcription, and boring overnight work. not because it beats frontier cloud models. because it changes the bill shape. no rate limit. no per-token anxiety. no sensitive client context leaving the building. no monthly stack quietly turning into rent. the ugly part is physical. seven 3090s can pull serious power, dump serious heat, and punish lazy cooling. distilled water is the weird visual, not a setup tip. real immersion rigs live or die on coolant chemistry, insulation, pumps, maintenance, and whether the room can handle the heat. local AI PCs are becoming less like gaming builds and more like small private data centers. the early question is not: can it run ChatGPT? it is: what work is repetitive, private, expensive in the cloud, and worth owning in hardware?

kocer

591,573 次观看 • 1 个月前

THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf
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THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf

Fokki

12,933 次观看 • 2 个月前

iPadOS 26, M4 iPad Pro, and Magic Keyboard are now better than a MacBook for day-to-day tasks. - Face ID to unlock - 120hz Tandem OLED - 5G Connectivity - All-day Battery Life - Small, lightweight, and portable I am using mine daily, and it can do almost everything a 'real' Mac can. The animations, the purpose-built apps - it feels like this is the future of laptop interfaces. If you have access to the beta, try it now - you will not be disappointed!
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iPadOS 26, M4 iPad Pro, and Magic Keyboard are now better than a MacBook for day-to-day tasks. - Face ID to unlock - 120hz Tandem OLED - 5G Connectivity - All-day Battery Life - Small, lightweight, and portable I am using mine daily, and it can do almost everything a 'real' Mac can. The animations, the purpose-built apps - it feels like this is the future of laptop interfaces. If you have access to the beta, try it now - you will not be disappointed!

John Schoenith

457,714 次观看 • 1 年前

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

Sweep

11,610 次观看 • 2 个月前

On June 22, the United States signed one executive order to build a quantum computer and, the same day, another to defend against what a quantum computer can do. The first establishes a national effort to build a machine powerful enough to open a new era of scientific discovery, delivered to a Department of Energy lab. The second orders an accelerated national migration to post-quantum cryptography, because the same physics that makes the machine useful makes today's encryption breakable. Read the two orders against each other and the asymmetry is the entire story. The order to build the machine sets no delivery date and is explicitly subject to the availability of appropriations. The order to defend the data sets hard deadlines: a migration pilot due December 31, 2027, and high-value systems moved to post-quantum cryptography by 2030 and 2031. The machine is a national aspiration. The migration is a national deadline. This is the part almost no one is pricing. Even the government is treating the regulatory clock as the binding one, while the market keeps watching the hardware clock. A product does not need to be broken to lose its value. It only needs to become uncertifiable, and the date it becomes uncertifiable is now written into federal policy. The machine can stay distant. The proofs are already on the clock.
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On June 22, the United States signed one executive order to build a quantum computer and, the same day, another to defend against what a quantum computer can do. The first establishes a national effort to build a machine powerful enough to open a new era of scientific discovery, delivered to a Department of Energy lab. The second orders an accelerated national migration to post-quantum cryptography, because the same physics that makes the machine useful makes today's encryption breakable. Read the two orders against each other and the asymmetry is the entire story. The order to build the machine sets no delivery date and is explicitly subject to the availability of appropriations. The order to defend the data sets hard deadlines: a migration pilot due December 31, 2027, and high-value systems moved to post-quantum cryptography by 2030 and 2031. The machine is a national aspiration. The migration is a national deadline. This is the part almost no one is pricing. Even the government is treating the regulatory clock as the binding one, while the market keeps watching the hardware clock. A product does not need to be broken to lose its value. It only needs to become uncertifiable, and the date it becomes uncertifiable is now written into federal policy. The machine can stay distant. The proofs are already on the clock.

Shanaka Anslem Perera ⚡

35,467 次观看 • 2 个月前

this is f**king dangerous A normal American student just bought an iPad and Mac Mini for $2,200. Connected them to his MacBook. Three computers on one desk - dorm roommates thought he was mining crypto. He just set up the automation and went to sleep. In the morning the system had already processed hundreds of leads, written personalized emails to each one and filled the CRM without a single touch. The team that did this before him- cost $7,000 a month He paid $2,200 once. There are 360 million companies in the world and 310 of them still pay people for what a machine does better. And only 100,000 people on the planet know how to use AI and set this up.
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this is f**king dangerous A normal American student just bought an iPad and Mac Mini for $2,200. Connected them to his MacBook. Three computers on one desk - dorm roommates thought he was mining crypto. He just set up the automation and went to sleep. In the morning the system had already processed hundreds of leads, written personalized emails to each one and filled the CRM without a single touch. The team that did this before him- cost $7,000 a month He paid $2,200 once. There are 360 million companies in the world and 310 of them still pay people for what a machine does better. And only 100,000 people on the planet know how to use AI and set this up.

Khairallah AL-Awady

1,478,555 次观看 • 17 天前

Everyone assumes serious AI compute means renting someone else's datacenter This is the datacenter 1,000 Mac Mini M4s on steel shelves $599 each, paid once Replacing a $14K a month cloud GPU bill It started much smaller A dev posted his Claude Code bill, $170 in 10 days The top reply got bigger than the post "I bought a Mac Mini M4, haven't paid Anthropic since" 10-30 watts a box instead of 300-500 120 GB/s unified memory, the model loads once Ollama now speaks the Anthropic Messages API So Claude Code can point at the box under your desk instead of Anthropic's servers Nobody claims these beat frontier models They claim most of your prompts never needed one The bill was never the price of intelligence It's the price of renting it for work that never needed renting Full breakdown below
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Everyone assumes serious AI compute means renting someone else's datacenter This is the datacenter 1,000 Mac Mini M4s on steel shelves $599 each, paid once Replacing a $14K a month cloud GPU bill It started much smaller A dev posted his Claude Code bill, $170 in 10 days The top reply got bigger than the post "I bought a Mac Mini M4, haven't paid Anthropic since" 10-30 watts a box instead of 300-500 120 GB/s unified memory, the model loads once Ollama now speaks the Anthropic Messages API So Claude Code can point at the box under your desk instead of Anthropic's servers Nobody claims these beat frontier models They claim most of your prompts never needed one The bill was never the price of intelligence It's the price of renting it for work that never needed renting Full breakdown below

Skaly_Bull

10,581 次观看 • 25 天前

THAT'S CRAZY, THIS CHINESE FOUNDER BUILT A MASSIVE MAC MINI FARM AND EACH ONE RUNNING ITS OWN HERMES AI AGENT LIKE A FULL-TIME EMPLOYEE He's not running one AI assistant. He's running an entire workforce. The stack: Mac Mini + Hermes, scaled out across a full physical farm. Every single Mac Mini in the rack runs its own instance of Hermes Agent – and each one has its own dedicated job. Not duplicated tasks. Actual division of labor, machine by machine, the way you'd structure a real team. No salaries. No sick days. No onboarding. Just racks of hardware, each one handling its own piece of the business, running in parallel, 24/7. This is what it looks like when "AI agent" stops being one chatbot on your laptop and starts being an actual operation. Most people are running one AI tool. This guy built a company out of them. Bookmark this post. Full setup in the video below.
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THAT'S CRAZY, THIS CHINESE FOUNDER BUILT A MASSIVE MAC MINI FARM AND EACH ONE RUNNING ITS OWN HERMES AI AGENT LIKE A FULL-TIME EMPLOYEE He's not running one AI assistant. He's running an entire workforce. The stack: Mac Mini + Hermes, scaled out across a full physical farm. Every single Mac Mini in the rack runs its own instance of Hermes Agent – and each one has its own dedicated job. Not duplicated tasks. Actual division of labor, machine by machine, the way you'd structure a real team. No salaries. No sick days. No onboarding. Just racks of hardware, each one handling its own piece of the business, running in parallel, 24/7. This is what it looks like when "AI agent" stops being one chatbot on your laptop and starts being an actual operation. Most people are running one AI tool. This guy built a company out of them. Bookmark this post. Full setup in the video below.

SCOTTY BEAM

20,503 次观看 • 1 个月前

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.
0:14

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

Gipp 🦅

24,836 次观看 • 2 个月前

Anthropic didn't build his memory system. He built it himself, and what you're looking at is the proof. Same source, no gatekeeping. The Obsidian CEO published it, anyone can install it. That's not a screensaver on his second monitor. It's his vault. Every point of light is a note. Every cluster is a project his AI already understands without being told again. He doesn't even touch the keyboard to move through it. One hand up, and the whole cluster tilts and spins, like he's turning a globe that happens to be his own head. A year ago he opened a new AI tab every morning and explained himself from scratch. Who he is, what he's building, what he wants. 200+ hours a year, just re-introducing himself to a machine. Then he stopped writing prompts and started writing one file instead. It loaded before every session. His goals, his projects, his blind spots. Claude read it first, every time, without asking. Now the cluster on that screen keeps growing on its own. New notes link themselves. Nothing gets explained twice. He's not showing off code. He's showing off the machine that finally stopped forgetting him.
0:13

Anthropic didn't build his memory system. He built it himself, and what you're looking at is the proof. Same source, no gatekeeping. The Obsidian CEO published it, anyone can install it. That's not a screensaver on his second monitor. It's his vault. Every point of light is a note. Every cluster is a project his AI already understands without being told again. He doesn't even touch the keyboard to move through it. One hand up, and the whole cluster tilts and spins, like he's turning a globe that happens to be his own head. A year ago he opened a new AI tab every morning and explained himself from scratch. Who he is, what he's building, what he wants. 200+ hours a year, just re-introducing himself to a machine. Then he stopped writing prompts and started writing one file instead. It loaded before every session. His goals, his projects, his blind spots. Claude read it first, every time, without asking. Now the cluster on that screen keeps growing on its own. New notes link themselves. Nothing gets explained twice. He's not showing off code. He's showing off the machine that finally stopped forgetting him.

Superior

108,125 次观看 • 1 个月前

This guy bought a $400 Mac mini and walked into a coffee shop with $2,100 for installing an AI agent that never touches the internet. I had to rewatch this because the pitch is almost too simple. He shows up, plugs in the Mac mini, installs a local AI agent running entirely through Ollama, done in under an hour. Zero cloud dependency, no API bills, no monthly charges bleeding the client dry. The coffee shop owner gets a private AI system that keeps working even if the WiFi dies, and he walks out with $2,100 for an install that takes less time than a long lunch. The hardware cost him $400. One deployment and the machine's already paid for five times over. Then he locks in a monthly retainer for support, somewhere around $100-150, which means every client after the first is close to pure margin stacking on top of recurring revenue. Most people hear "AI business" and think they need to build a SaaS platform or learn to code or raise funding from someone. This skips all of that and goes straight to walking into coffee shops with a Mac mini under one arm. Not a company that scales to a billion dollars, obviously, but a solo operator clearing a few thousand a month from local installs while everyone else is still arguing about which LLM is best on Twitter.
0:11

This guy bought a $400 Mac mini and walked into a coffee shop with $2,100 for installing an AI agent that never touches the internet. I had to rewatch this because the pitch is almost too simple. He shows up, plugs in the Mac mini, installs a local AI agent running entirely through Ollama, done in under an hour. Zero cloud dependency, no API bills, no monthly charges bleeding the client dry. The coffee shop owner gets a private AI system that keeps working even if the WiFi dies, and he walks out with $2,100 for an install that takes less time than a long lunch. The hardware cost him $400. One deployment and the machine's already paid for five times over. Then he locks in a monthly retainer for support, somewhere around $100-150, which means every client after the first is close to pure margin stacking on top of recurring revenue. Most people hear "AI business" and think they need to build a SaaS platform or learn to code or raise funding from someone. This skips all of that and goes straight to walking into coffee shops with a Mac mini under one arm. Not a company that scales to a billion dollars, obviously, but a solo operator clearing a few thousand a month from local installs while everyone else is still arguing about which LLM is best on Twitter.

BrainRul

4,697,798 次观看 • 1 个月前

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.
0:18

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,587 次观看 • 1 个月前

THIS DEVELOPER OPENED HERMES ON A LAPTOP, SPENT $0 ON API SETUP, SAVED 7 WORKFLOWS, AND CUT 2-HOUR CLIENT TASKS DOWN TO 14 MINUTES he is not giving a polished demo. he is just filming the laptop while Hermes runs, and that is why the clip works. you can see the terminal, the workspace, the task history, and the moment a normal chat tool starts looking like a local operating layer most people still run AI like a vending machine: 1 prompt, 1 answer, 1 reset. Hermes is different. after 5-10 repeated jobs, the useful steps start living inside skills instead of getting rewritten every morning the money math is where it gets ugly. $20 for Claude, $40-90 in API usage, $50 for wrappers, $29 for automation tools, and you are already near $140-190/month before you even sell the first report he used the same flow for 9 small research tasks: 18 competitor pages, 126 review snippets, 9 pricing checks, 9 summary drafts. the first one took 43 minutes. later runs were mostly review, edit, send that is the part people miss about Hermes. it is not trying to win the prettiest chatbot contest. it is trying to make repeated work stop leaking out of the machine every time the session ends
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THIS DEVELOPER OPENED HERMES ON A LAPTOP, SPENT $0 ON API SETUP, SAVED 7 WORKFLOWS, AND CUT 2-HOUR CLIENT TASKS DOWN TO 14 MINUTES he is not giving a polished demo. he is just filming the laptop while Hermes runs, and that is why the clip works. you can see the terminal, the workspace, the task history, and the moment a normal chat tool starts looking like a local operating layer most people still run AI like a vending machine: 1 prompt, 1 answer, 1 reset. Hermes is different. after 5-10 repeated jobs, the useful steps start living inside skills instead of getting rewritten every morning the money math is where it gets ugly. $20 for Claude, $40-90 in API usage, $50 for wrappers, $29 for automation tools, and you are already near $140-190/month before you even sell the first report he used the same flow for 9 small research tasks: 18 competitor pages, 126 review snippets, 9 pricing checks, 9 summary drafts. the first one took 43 minutes. later runs were mostly review, edit, send that is the part people miss about Hermes. it is not trying to win the prettiest chatbot contest. it is trying to make repeated work stop leaking out of the machine every time the session ends

Gipp 🦅

14,593 次观看 • 3 个月前

You could spend a ton making this without AI… or just use video models like a normal person. Prompt : A cinematic one-shot begins inside a silent grand museum at night. The camera moves toward a massive painting of a storm at sea. As it nears the canvas, the painted waves begin moving, and the frame transforms continuously without cuts: the painted ocean becomes real water flooding the gallery floor, the gallery becomes a ship deck in a hurricane, the sailcloth becomes desert tents, the tents become war banners on a battlefield, the banners become laundry lines in a sunlit village, the village walls become canyon cliffs, the cliffs become bookshelves in an endless library. Every transition must come from shared shapes and motion. A museum guard walks through all of it in disbelief, but the camera never leaves him. Frames become windows, windows become doorways, doorways become portals into new realities. Final transformation: the guard realizes he is now inside a painting, frozen in brushstroke texture, while museum visitors admire him from outside.
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You could spend a ton making this without AI… or just use video models like a normal person. Prompt : A cinematic one-shot begins inside a silent grand museum at night. The camera moves toward a massive painting of a storm at sea. As it nears the canvas, the painted waves begin moving, and the frame transforms continuously without cuts: the painted ocean becomes real water flooding the gallery floor, the gallery becomes a ship deck in a hurricane, the sailcloth becomes desert tents, the tents become war banners on a battlefield, the banners become laundry lines in a sunlit village, the village walls become canyon cliffs, the cliffs become bookshelves in an endless library. Every transition must come from shared shapes and motion. A museum guard walks through all of it in disbelief, but the camera never leaves him. Frames become windows, windows become doorways, doorways become portals into new realities. Final transformation: the guard realizes he is now inside a painting, frozen in brushstroke texture, while museum visitors admire him from outside.

Umesh

48,290 次观看 • 5 个月前

this creator just built a zero-delay auto-aim system on an $8 microcontroller he deployed a custom local AI algorithm on a cheap ESP-32 to track human movement with absolute 0-pixel accuracy. the system completely eliminates standard computation delay. it processes the bounding box and moves the sniper reticle instantly, locking onto the target in real time. the next step is hooking it up to a physical robotic arm. it proves you don't need a massive GPU rig to run complex computer vision. 1. runs locally on a microcontroller 2. local AI inference for zero latency 3. 100% targeting precision when an eight-dollar chip can track movement with zero latency, AI becomes dangerous local infrastructure. this article breaks down exactly how the ESP-32 is powering this physical hardware shift.
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this creator just built a zero-delay auto-aim system on an $8 microcontroller he deployed a custom local AI algorithm on a cheap ESP-32 to track human movement with absolute 0-pixel accuracy. the system completely eliminates standard computation delay. it processes the bounding box and moves the sniper reticle instantly, locking onto the target in real time. the next step is hooking it up to a physical robotic arm. it proves you don't need a massive GPU rig to run complex computer vision. 1. runs locally on a microcontroller 2. local AI inference for zero latency 3. 100% targeting precision when an eight-dollar chip can track movement with zero latency, AI becomes dangerous local infrastructure. this article breaks down exactly how the ESP-32 is powering this physical hardware shift.

ard

1,185,553 次观看 • 1 个月前