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This guy put an AI data center into a box that fits on a desk It doesn't look like a serious machine It looks like a mini PC you could hide behind a monitor But with enough unified memory it can run local models, answer over your own files,...

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Asteri

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12,199 görüntüleme • 3 ay önce •via X (Twitter)

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taOS profil fotoğrafı
taOS3 ay önce

Install

Todd Darimont profil fotoğrafı
Todd Darimont3 ay önce

Now use the software that’ll bring out its best

Hussain Hashim | Building SundayBack profil fotoğrafı
Hussain Hashim | Building SundayBack3 ay önce

@Asteri_eth that's wild! imagine having that kind of power just sitting on your desk lol

Oleg Bratko profil fotoğrafı
Oleg Bratko3 ay önce

Honest version of this take — it actually admits “the cloud still wins for the hardest tasks.” That caveat is what most of these posts drop. Local handles the daily 80%, not a frontier replacement.

kvinsi profil fotoğrafı
kvinsi3 ay önce

privacy alone is worth it for a lot of people

Charlie · 云马AI · 免🪜稳连 Claude/GPT/Gemini API profil fotoğrafı
Charlie · 云马AI · 免🪜稳连 Claude/GPT/Gemini API3 ay önce

这个方向确实挺有意思的。说实话我现在越来越觉得，本地小盒子跑模型不一定是为了多快，更多是图安心。公司资料、聊天记录、个人文档这些，能不出门就不出门，体验差一点我都能接受。

grafanastu1975 profil fotoğrafı
grafanastu19753 ay önce

ok but who merges when their vltes conflict bro

Solvaix profil fotoğrafı
Solvaix3 ay önce

local AI setups are getting more accessible every day

Yarchi profil fotoğrafı
Yarchi3 ay önce

solid setup man

Oszynthh profil fotoğrafı
Oszynthh3 ay önce

It’s interesting to see just how much of a factor privacy is. Many people are switching to local models not only for financial reasons, but also because they don’t want to upload sensitive documents to third-party servers.

Kenny1st profil fotoğrafı
Kenny1st3 ay önce

"The assistant you rent every month can become hardware they own." That line says it all. The M4 Pro chips completely flipped the script on what local hardware can do. Brilliant breakdown.

Nekt0 profil fotoğrafı
Nekt03 ay önce

That is a useful reminder

Valeri profil fotoğrafı
Valeri3 ay önce

the cloud still wins for the hardest tasks which turns out to be a much smaller list than people think

vanvster profil fotoğrafı
vanvster3 ay önce

convenience is a hell of a drug haha

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A GIRL BOUGHT A $599 APPLE BOX AND CUT HER AI COSTS FROM $459/MONTH TO $23/MONTH. THE MAC MINI M4 IS QUIETLY BECOMING THE CHEAPEST AI SETUP IN 2026 she didn’t buy it because it looked nice on her desk. she bought it because paying for claude, chatgpt, cursor and api usage every month was getting ridiculous the setup is simple. mac mini m4, ollama, open webui, and local models like qwen, deepseek and llama. for most daily work, that’s enough to write, code, summarize, search notes, and run private workflows without sending everything to the cloud that’s why the math looks so good. a heavy ai stack can hit $459 a month, or $5,508 a year. the mac mini starts at $599 and uses around $3 a month in electricity. if it handles even 70 to 80% of the workload, it pays for itself fast install ollama, point your tools to localhost, and the workflow changes immediately. no token stress, no rate limits, no wondering where your files are going you still keep one cloud model for the hardest tasks but once a small box on your desk does most of the work, paying full price for everything starts to feel stupid

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Nekt0

54,445 görüntüleme • 3 ay önce

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$3,800 for a Mini PC with 96GB of GPU memory sounds ridiculous. Until you see what AMD is actually doing. The GMKtec EVO-X3 has: Ryzen AI MAX+ 395 128GB LPDDR5X Up to 96GB allocated to the Radeon 8060S 8000 MT/s memory No discrete GPU. That means a tiny box can run local models that would normally push you toward a workstation with a huge GPU. And it can still play games. The interesting part isn't the 126 TOPS number. It's the fact that 96GB of GPU memory now fits inside a mini PC. The question is no longer whether this can run a big model. It's how fast it can run it. The EVO-X3 is available here:

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A 23-YEARS-OLD MADE $22,500 USING MAC MINI M4 THAT CUT DEVELOPER AI CREDITS BY 80 PERCENT A typical developer spending $150 to $200 a month on Claude or OpenAI API credits can route routine work to local models and keep cloud models for hard reasoning. The stack is simple : Mac Mini M4, Ollama, LiteLLM, Open WebUI, Qwen, Llama, Gemma or Phi. The math is the hook. Electricity costs $3 to $5 a month, while cron jobs, summaries, formatting, code reviews and draft emails stop burning paid tokens. The edge is not replacing Claude. It is using Claude only where Claude actually matters.
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A 23-YEARS-OLD MADE $22,500 USING MAC MINI M4 THAT CUT DEVELOPER AI CREDITS BY 80 PERCENT A typical developer spending $150 to $200 a month on Claude or OpenAI API credits can route routine work to local models and keep cloud models for hard reasoning. The stack is simple : Mac Mini M4, Ollama, LiteLLM, Open WebUI, Qwen, Llama, Gemma or Phi. The math is the hook. Electricity costs $3 to $5 a month, while cron jobs, summaries, formatting, code reviews and draft emails stop burning paid tokens. The edge is not replacing Claude. It is using Claude only where Claude actually matters.

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THEY RAN STATE OF THE ART AI ON A 26 YEAR OLD IMAC WITH ZERO INTERNET not a demo. not a benchmark a real answer in near instant time on hardware older than most people reading this your subscription money is going to a data center that doesn't need it the future already runs locally on dead hardware from 1999 bookmark this before you pay for another month of cloud AI you don't need

leopardracer

2,766,120 görüntüleme • 4 ay önce

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1:05

THIS DEVELOPER TURNED A $599 MAC MINI AND $110 OF EXTRA HARDWARE INTO A PRIVATE AI COMPUTER THAT FITS IN A BACKPACK 00:49 he takes the complete setup out of the bag, connects it to a nearby screen and restores the same desktop, files and running tools in less than a minute. instead of syncing a new laptop every time he moves, the mac mini carries the original obsidian vault, claude code sessions, local models and unfinished agent tasks between every desk. the 10,000mah battery can keep the machine running for roughly 2 to 4 hours during writing, research and file organization, or around 60 to 90 minutes under heavier transcription and local ai workloads. the full build costs close to $710 if a screen is already available. that includes the mac mini, a 55w battery, the side mount and the cables needed to connect it to an ipad, hotel tv or office monitor. one box can carry 12,000 notes, 300gb of private files and several active agents without sending the data to another cloud account or rebuilding the workspace every time he changes locations.

Gipp 🦅

30,550 görüntüleme • 3 ay önce

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below
16:10

You can run local AI models up to 120B without a $10,000 Mac Studio This phone-sized device is your own server for open-source models. 100% local and private. And you can use it to: - Power an agent like OpenClaw 24/7 - Completely replace a chatbot - Literally anything that requires an API It’s called Tiiny, and, once again, you can run the latest open-source models on it. Link below

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41,356 görüntüleme • 7 ay önce

A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓
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A $170 AI bill in 10 days is not a cloud problem. It is a routing problem. Most devs send every task to the most expensive brain available. summaries small refactors docs scripts log parsing boilerplate That is how the bill gets stupid. The Mac Mini M4 trade is simple: run routine work locally keep private code on your desk pay once for hardware save frontier models for hard calls Local AI does not beat Claude at everything. It does not need to. It only needs to replace the boring prompts that never needed a frontier model in the first place. Cloud AI stays. The default route changes. Full breakdown below ↓

Fluixo

35,295 görüntüleme • 1 ay önce

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0:32

$5,280 a year on AI subscriptions. Claude Max, ChatGPT Pro, Gemini, Cursor. $440 every month going to the cloud. a mini PC for $1,700 does the same thing on your desk. GMKtec EVO-X2 - a box the size of a hardcover book. inside it the chip that Lisa Su personally brought on stage at CES and signed with her name. 128GB of memory in one pool. runs models that cloud subscriptions behind $200 paywalls throttle during peak hours. here there are no limits - your hardware, your rules. NVIDIA's RTX 5080 at $1,000+ lost to this box by 3x on a real AI workload. a discrete GPU beaten by a mini PC the size of a lunchbox. electricity - $9 a month. pays for itself in 10 months. after that $440 every month stays in your pocket. the cloud made sense when nothing on a desk could compete. now it can.

Ventry

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EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.

RetroChainer

14,184 görüntüleme • 2 ay önce

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 görüntüleme • 5 ay önce

THE NEXT AI WORKER MAY COST $500 AND SIT BEHIND YOUR MONITOR. Mini PCs are becoming the cheapest path to private AI. A Mac Mini M4 starts at $599, Geekom AX8 Pro starts near $529, and MinisForum pushes up to 128GB RAM for local AI workloads. Ollama turns that hardware into a personal model server. No cloud fee. No data storage bill. No waiting for another SaaS product to decide your limits. Market shift is simple -> computer is moving back to the desk. Owning a mini PC is starting to look less like buying hardware and more like hiring a quiet AI operator.
0:21

THE NEXT AI WORKER MAY COST $500 AND SIT BEHIND YOUR MONITOR. Mini PCs are becoming the cheapest path to private AI. A Mac Mini M4 starts at $599, Geekom AX8 Pro starts near $529, and MinisForum pushes up to 128GB RAM for local AI workloads. Ollama turns that hardware into a personal model server. No cloud fee. No data storage bill. No waiting for another SaaS product to decide your limits. Market shift is simple -> computer is moving back to the desk. Owning a mini PC is starting to look less like buying hardware and more like hiring a quiet AI operator.

Lummox

394,894 görüntüleme • 3 ay önce

If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.
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If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.

Yarchi

32,840 görüntüleme • 3 ay önce

I tried MUZIM by OpenSoul on my Mac this week. Most “AI for your files” tools want you to upload everything into someone else’s cloud. MUZIM is different. It is a local AI for your photo and video library. Not another chatbot. Your raw files stay on your machine. I imported a messy media dump and searched the way I actually remember things. Not filenames. Vibes. I typed “that rainy cafe by the window.” It found the shot. It can even land on a moment inside a video. And after the model downloads, that kind of search keeps working offline. Then the Smart Organization kicked in. I did not rebuild folders. MUZIM built Collections on its own from the dump. Nature, outfits, travel vibes. The library went from chaos to something I could browse. The part that surprised me most is what happens next. Once the library is organized, the AI Agent helps turn it into something I can actually ship. Captions. An X thread. A short highlight concept with hooks. Cold storage becomes a private workspace. You can also plug in your own Claude or GPT key if you want cloud AI on top of local files. Still your library. Still your control. This is the kind of AI I want. More understanding. Less uploading everything to the cloud. Try it:
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I tried MUZIM by OpenSoul on my Mac this week. Most “AI for your files” tools want you to upload everything into someone else’s cloud. MUZIM is different. It is a local AI for your photo and video library. Not another chatbot. Your raw files stay on your machine. I imported a messy media dump and searched the way I actually remember things. Not filenames. Vibes. I typed “that rainy cafe by the window.” It found the shot. It can even land on a moment inside a video. And after the model downloads, that kind of search keeps working offline. Then the Smart Organization kicked in. I did not rebuild folders. MUZIM built Collections on its own from the dump. Nature, outfits, travel vibes. The library went from chaos to something I could browse. The part that surprised me most is what happens next. Once the library is organized, the AI Agent helps turn it into something I can actually ship. Captions. An X thread. A short highlight concept with hooks. Cold storage becomes a private workspace. You can also plug in your own Claude or GPT key if you want cloud AI on top of local files. Still your library. Still your control. This is the kind of AI I want. More understanding. Less uploading everything to the cloud. Try it:

Robert Scoble

64,009 görüntüleme • 1 ay önce

A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.
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A 24-YEAR-OLD CHINESE DEVELOPER FROM HANGZHOU TURNED RTX 4090 / 3090-CLASS GPU RACKS INTO HIS OWN PRIVATE AI CLOUD. HIS $740/MONTH AI BILL DROPPED TO $31 IN ELECTRICITY he got tired of paying for chatgpt, claude, cursor, openai api credits and every “pro” tool that quietly turns into another monthly tax. long context runs, codebase scans, document parsing, agent loops. every workflow ended with a new invoice so he built a local llm rack instead. used server hardware, RTX 4090 / 3090-class GPU boxes, ollama for automation, lm studio for testing models, llama.cpp for heavier local runs. around $6,200 upfront, but after that the cost is mostly power and maintenance now his scripts hit localhost instead of a cloud api. code reviews, private docs, chinese contracts, sql cleanup, support replies and research tasks stay inside the room. no token panic, no rate-limit wall, no sensitive files leaving his own machines the funny part is that he did not replace claude completely. he just stopped using frontier models for dumb volume work. 65% of daily ai tasks do not need the smartest model alive. they need cheap tokens, privacy and a machine that can run all night cloud ai is still the brain. local ai is the engine room. once he separated those two, his monthly ai stack stopped looking like subscriptions and started looking like infrastructure by 2027, owning your own local ai rack will not look extreme. it will look like the moment people realized renting intelligence forever was the expensive option.

Gipp 🦅

21,320 görüntüleme • 4 ay önce