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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.show more

Gipp 🦅

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24,836 次观看 • 2 个月前 •via X (Twitter)

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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.

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 🦅

591,167 次观看 • 2 个月前

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.

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

Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.

Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.

Shelpid.WI3M

976,008 次观看 • 2 个月前

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 a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

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 a screen, hold local files and keep working without asking for an outlet. that changes the use case completely. instead of renting another cloud box, one silent computer can handle research dumps, meeting notes, scraped pages, voice transcripts and small automation jobs from almost anywhere. with claude connected, it becomes a moving command center. 45-minute calls become summaries, 120 saved links become organized notes, and messy project folders get cleaned while the machine quietly keeps working in the background. the interesting number is not the battery size. it is the avoided rent. one portable local box can replace $25 storage, $39 automation, $20 transcription and another $30 vps bill if the workflow is built correctly. this is no longer just a desktop. it becomes a portable ai machine that keeps working long after you leave the desk. bookmark this before portable ai becomes the new normal.

Gipp 🦅

1,788,304 次观看 • 1 个月前

Nvidia just put a $250,000 cloud workload on your desk for $2,999 - and killed your $1,900/month AWS bill in the process You don't rent it, you don't manage it, you don't pay a single cloud bill - you just plug it in and let it eat the workloads you used to wire to AWS every month It looks like a small Mac mini, it's actually a full GB10 Grace Blackwell stack with 128GB of unified memory running models up to 200B parameters It's called DGX Spark, the consumer version of the rack Nvidia ships to OpenAI The reason Nvidia did this is simple Cloud GPU pricing is a tax on every developer building AI right now $1,900/month per seat, billions in margin flowing to AWS, Lambda, and CoreWeave Nvidia just cut themselves in by removing the cloud entirely Their solution is to skip the middleman, ship the rack to your desk, and let you keep every dollar of margin you used to wire to a hyperscaler This is much cheaper, faster, and you own the asset at the end But there is still a question nobody is answering yet, what happens to AWS, GCP, and Lambda when 500,000 developers move their inference back to a $2,999 box on their desk Also, technically you can stack four of these and run a 1.6 trillion parameter model locally for under $12,000 Even a single Spark out-performs the cloud subscription Anthropic engineers were running two years ago bookmark this, it pays back in 60 days 👇

Nvidia just put a $250,000 cloud workload on your desk for $2,999 - and killed your $1,900/month AWS bill in the process You don't rent it, you don't manage it, you don't pay a single cloud bill - you just plug it in and let it eat the workloads you used to wire to AWS every month It looks like a small Mac mini, it's actually a full GB10 Grace Blackwell stack with 128GB of unified memory running models up to 200B parameters It's called DGX Spark, the consumer version of the rack Nvidia ships to OpenAI The reason Nvidia did this is simple Cloud GPU pricing is a tax on every developer building AI right now $1,900/month per seat, billions in margin flowing to AWS, Lambda, and CoreWeave Nvidia just cut themselves in by removing the cloud entirely Their solution is to skip the middleman, ship the rack to your desk, and let you keep every dollar of margin you used to wire to a hyperscaler This is much cheaper, faster, and you own the asset at the end But there is still a question nobody is answering yet, what happens to AWS, GCP, and Lambda when 500,000 developers move their inference back to a $2,999 box on their desk Also, technically you can stack four of these and run a 1.6 trillion parameter model locally for under $12,000 Even a single Spark out-performs the cloud subscription Anthropic engineers were running two years ago bookmark this, it pays back in 60 days 👇

ZEUS⚡️

85,803 次观看 • 2 个月前

THIS GUY BOUGHT A $2,400 NVIDIA BOX AND SAVED $18,700/YEAR ON CLOUD GPUS WITHOUT RENTING SERVERS AGAIN the entire setup runs on one rule - stop paying every time you want to test something most people run 20 small AI experiments in the cloud and think it’s cheap because each one looks harmless - then the invoice comes in and suddenly their “side project” has the same monthly cost as a car payment he made the same mistake for months and it slowly killed the way he worked one box, one desk, local models - and now he can run tests overnight without thinking about hourly GPU prices $18,700/year saved by a little NVIDIA box he can literally hold in his hands

THIS GUY BOUGHT A $2,400 NVIDIA BOX AND SAVED $18,700/YEAR ON CLOUD GPUS WITHOUT RENTING SERVERS AGAIN the entire setup runs on one rule - stop paying every time you want to test something most people run 20 small AI experiments in the cloud and think it’s cheap because each one looks harmless - then the invoice comes in and suddenly their “side project” has the same monthly cost as a car payment he made the same mistake for months and it slowly killed the way he worked one box, one desk, local models - and now he can run tests overnight without thinking about hourly GPU prices $18,700/year saved by a little NVIDIA box he can literally hold in his hands

Gipp 🦅

12,484 次观看 • 2 个月前

ONE OPERATOR STACKED 300 GPUS ACROSS TWO APARTMENTS IN THE SAME BUILDING AND RUNS A $48K/MONTH AI INFERENCE FARM ON VAST AI FROM HIS LIVING ROOM 00:17 he walks past stacks of GPU boxes, "and probably another 100 GPU boxes in the second apartment, let me know in the comments if you want to see them" he rents 2 units in the same building, one as his living space with 200 GPUs in the bedroom and hallway, the second is dedicated and climate controlled just for the other 100 cards a 300 RTX 4090 setup pulls 135 kilowatts fully loaded, his power bill runs $9,800 a month at $0.10 per kwh, on vast ai the same fleet clears $48,000 in gross monthly rental income he never built this in a warehouse because residential electricity in his city is cheaper than commercial under 150 kw, the split apartment trick keeps him under that ceiling while doubling his rack space the same hardware would have cleared maybe $9,000 a month mining ethereum classic in 2022, vast ai pays 5 times that for AI inference because nobody can ship enough H100s to meet startup demand bookmark this and read the article below

ONE OPERATOR STACKED 300 GPUS ACROSS TWO APARTMENTS IN THE SAME BUILDING AND RUNS A $48K/MONTH AI INFERENCE FARM ON VAST AI FROM HIS LIVING ROOM 00:17 he walks past stacks of GPU boxes, "and probably another 100 GPU boxes in the second apartment, let me know in the comments if you want to see them" he rents 2 units in the same building, one as his living space with 200 GPUs in the bedroom and hallway, the second is dedicated and climate controlled just for the other 100 cards a 300 RTX 4090 setup pulls 135 kilowatts fully loaded, his power bill runs $9,800 a month at $0.10 per kwh, on vast ai the same fleet clears $48,000 in gross monthly rental income he never built this in a warehouse because residential electricity in his city is cheaper than commercial under 150 kw, the split apartment trick keeps him under that ceiling while doubling his rack space the same hardware would have cleared maybe $9,000 a month mining ethereum classic in 2022, vast ai pays 5 times that for AI inference because nobody can ship enough H100s to meet startup demand bookmark this and read the article below

starmex

11,545 次观看 • 1 个月前

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

beamnxw ./

30,361 次观看 • 2 个月前

THE CLOUD BILL WAS $14,000 A MONTH. EVERY MONTH. JUST FOR GPU ACCESS. No ownership. No hardware. Just renting someone else's chips and watching the invoice grow. So this startup did the math and bought 1,000 Mac Mini M4s instead. $599 each. One-time. Total: ~$599,000 upfront. Sounds insane. Until you do the math the other way. $14K/mo in cloud GPUs is $168K a year. In 3.5 years you've burned through $599K and own nothing. The meter just keeps running. These guys own the hardware. Same output. Less power draw. And after year 4, every month of compute is basically free. The M4 was never built for data centers. But its performance per watt is so good that someone looked at a rack of a thousand of them and thought "why not." That's when it clicks. The AI infrastructure race isn't about who has the biggest GPU anymore. It's about who figured out they were overpaying for one. Would you make this switch?

THE CLOUD BILL WAS $14,000 A MONTH. EVERY MONTH. JUST FOR GPU ACCESS. No ownership. No hardware. Just renting someone else's chips and watching the invoice grow. So this startup did the math and bought 1,000 Mac Mini M4s instead. $599 each. One-time. Total: ~$599,000 upfront. Sounds insane. Until you do the math the other way. $14K/mo in cloud GPUs is $168K a year. In 3.5 years you've burned through $599K and own nothing. The meter just keeps running. These guys own the hardware. Same output. Less power draw. And after year 4, every month of compute is basically free. The M4 was never built for data centers. But its performance per watt is so good that someone looked at a rack of a thousand of them and thought "why not." That's when it clicks. The AI infrastructure race isn't about who has the biggest GPU anymore. It's about who figured out they were overpaying for one. Would you make this switch?

Framez

18,004 次观看 • 13 天前

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.

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.

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

4,688,762 次观看 • 22 天前

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

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

25,981 次观看 • 2 个月前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 1 个月前

A dual-socket Xeon server with 512GB of ECC RAM sells for under $2,000 after AWS retires it. Two open chassis sit on a desk. Heatsinks the size of a fist. Every RAM slot filled. Fiber cables still warm. Network cards and PCIe risers that once pushed millions of requests a day. This is not a lab. It is someone’s workspace. Hardware built for five years of 24/7 load leaves the datacenter after three or four. A machine that cost $50,000 new appears on the used market for $500–2,000. The CPUs still work. The memory still works. Only the balance sheet changed. Dual-socket Xeons and hundreds of gigabytes of ECC RAM load models that choke consumer boards. Multiple VMs, databases, and inference jobs run at once without strain. That is the exact job these boards were designed for. The cost is power and noise. 500–1,000 watts under load. A jet-engine soundtrack. The electricity bill notices. Most people spend $2,000 on a mini PC with 128GB. This one cost less and delivers 512GB+ of server-grade memory plus dual enterprise CPUs that were processing production traffic six months earlier. A Mac Mini looks like a toy next to the heatsinks.

A dual-socket Xeon server with 512GB of ECC RAM sells for under $2,000 after AWS retires it. Two open chassis sit on a desk. Heatsinks the size of a fist. Every RAM slot filled. Fiber cables still warm. Network cards and PCIe risers that once pushed millions of requests a day. This is not a lab. It is someone’s workspace. Hardware built for five years of 24/7 load leaves the datacenter after three or four. A machine that cost $50,000 new appears on the used market for $500–2,000. The CPUs still work. The memory still works. Only the balance sheet changed. Dual-socket Xeons and hundreds of gigabytes of ECC RAM load models that choke consumer boards. Multiple VMs, databases, and inference jobs run at once without strain. That is the exact job these boards were designed for. The cost is power and noise. 500–1,000 watts under load. A jet-engine soundtrack. The electricity bill notices. Most people spend $2,000 on a mini PC with 128GB. This one cost less and delivers 512GB+ of server-grade memory plus dual enterprise CPUs that were processing production traffic six months earlier. A Mac Mini looks like a toy next to the heatsinks.

HodlReaper

77,912 次观看 • 15 天前

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,441 次观看 • 1 个月前

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.

ard

425,950 次观看 • 29 天前

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

Varun

309,320 次观看 • 4 个月前

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

THREE 3090s ON ONE BOARD GIVE YOU 72GB OF VRAM AND KILL YOUR $200 CLAUDE CODE AND $200 OPENAI BILL people are pulling three used 3090s off ebay for around $2,100 total and stacking them in one tower to build a dedicated ai rig. that pools 72gb of vram for less than what a single rtx 5090 retails for alibaba shipped qwen 3.6 27b in april under apache 2.0. on realworldqa vision it scores 84.1 against claude 4.5 opus at 77.0. on ifbench instructions it lands at 76.5 against claude's 58.0 a single 3090 already runs qwen 3.6 27b with eight gigs of headroom. three of them in parallel handle larger models like deepseek r1 70b and qwen 235b without breaking a sweat a heavy ai user pays $200 claude code, $200 chatgpt pro plus $40 cursor and gemini. that's $5,280 a year and the rig pays itself off before month nine on $8 a month in electricity setup is one shell command for ollama, one to pull the model, one environment variable to point claude code at localhost. cli stays identical, nothing leaves the network, requests stop costing money bookmark this and read the article below

starmex

16,719 次观看 • 2 个月前

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

Argona

22,355 次观看 • 2 个月前

$I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.$

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,494 次观看 • 5 个月前