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THIS BUILDER JUST DROPPED 256GB OF RAM INTO A MONSTER THREADRIPPER WORKSTATION TO RUN UNFILTERED LOCAL AI Imagine trying to fit a computer setup into a chassis that is basically the size of a mini fridge. That is the Corsair 1000D tower. The builder crammed an ASUS Pro WS... WRX80E-SAGE motherboard inside and slapped a 64-core AMD Threadripper PRO 5995WX right into the socket Why go this heavy? Simple. To make sure local open weights models don't instantly choke standard desktop hardware during heavy reasoning tasks Then things get downright ridiculous with the memory configuration. He unboxes eight separate Kingston DDR4 modules, pinning a massive 256 gigabytes of system RAM directly to the board. Having that kind of local memory headroom is an absolute necessity if a team wants to handle massive datasets or run dense training loops without constantly swapping data to the storage drives Speaking of storage, the system relies on two lightning fast 2TB Samsung 990 PRO NVMe drives For the graphics pipeline, he drops in a top-tier ASUS ROG RTX 4090 boasting 24 gigabytes of VRAM. That is pretty much the gold standard right now if you want to run quick local inference cycles and completely stop paying corporate cloud token fees to OpenAI or Anthropic Powering this whole grid requires a monstrous ASUS ROG 1600W Thor Gen 2 power supply. And to prevent the entire workstation from turning into a space heater under full load, the builder went all out with a 360mm AIO liquid cooling setup and an insane cluster of sixteen Lian Li SL-Infinity RGB fans It looks incredibly flashy, probably sounds like a jet engine when the cores push maximum load What to buy for local AI? => my guide below Bookmark this so you don't lose itshow more

beamnxw ./

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47,366 次观看 • 1 个月前 •via X (Twitter)

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THIS $1,300 CARD HAS THE SAME 32GB OF VRAM AS A $2,000+ RTX 5090 - FOR LOCAL AI, THAT'S THE SPEC THAT DECIDES EVERYTHING. that clip is the gigabyte radeon ai pro r9700 ai top, 32gb of vram, built for local ai. the pitch is simple: the same memory capacity as an rtx 5090, for roughly a third of what a 5090 actually sells for. here's why that matters, and where it doesn't. for ai, vram is the gate. a model either fits in memory or it doesn't. 32gb loads models a 16gb card can't touch, full stop. so for running local llms and image models, this card does the one thing that counts, cheap. honest line: this is not a 5090 for gaming. the 5090 is far faster at raster and ray tracing. they only match on how much vram they carry. if you want frames, buy the 5090. if you want to fit big models locally, you just found the same 32gb for a third of the money. no $2,000+ price tag, no cloud gpu rental, no model too big for 16gb. the uncomfortable part isn't that amd is cheaper. it's that for local ai you were about to overpay for gaming power you'd never use. follow RetroChainer for more on paying for the spec that matters, and bookmark this before your next gpu.

THIS $1,300 CARD HAS THE SAME 32GB OF VRAM AS A $2,000+ RTX 5090 - FOR LOCAL AI, THAT'S THE SPEC THAT DECIDES EVERYTHING. that clip is the gigabyte radeon ai pro r9700 ai top, 32gb of vram, built for local ai. the pitch is simple: the same memory capacity as an rtx 5090, for roughly a third of what a 5090 actually sells for. here's why that matters, and where it doesn't. for ai, vram is the gate. a model either fits in memory or it doesn't. 32gb loads models a 16gb card can't touch, full stop. so for running local llms and image models, this card does the one thing that counts, cheap. honest line: this is not a 5090 for gaming. the 5090 is far faster at raster and ray tracing. they only match on how much vram they carry. if you want frames, buy the 5090. if you want to fit big models locally, you just found the same 32gb for a third of the money. no $2,000+ price tag, no cloud gpu rental, no model too big for 16gb. the uncomfortable part isn't that amd is cheaper. it's that for local ai you were about to overpay for gaming power you'd never use. follow RetroChainer for more on paying for the spec that matters, and bookmark this before your next gpu.

RetroChainer

38,844 次观看 • 5 天前

THIS BUILDER JUST LINKED FORTY RTX 3090s IN HIS BEDROOM TO BYPASS THE CLOUD AND RUN 1TB OF LOCAL VRAM. WHY DOES HE HAVE SO MUCH?? Let that number sink in for a minute. Nearly a full terabyte of raw, unadulterated VRAM sitting right next to someone's bed. With forty RTX 3090s linked up in a custom rack, you aren't just running basic local chat models anymore You are essentially operating a localized data center. It completely changes the math on what you can actually execute without a cloud subscription For starters, you can comfortably host giant, fully unquantized open weights models. I'm talking about running massive enterprise reasoning architectures like DeepSeek-R1 671B or dense Llama 405B models at full precision. Most people have to slice these models down, compressing them until they lose their edge. On a rig like this, they run completely uncompressed But it gets weirder. You can launch massive multi-agent autonomous swarms. Imagine spinning up 300 to 400 distinct AI agents simultaneously ➜ each running its own heavy coding or data-scraping loops ➜ and letting them interact in real time + You can build a massive, real-time semantic search engine over your entire digital life. You could feed decades of personal data, code repositories, thousands of books, and full video transcriptions into a localized vector database. The system can keep the entire index permanently hot in the graphics memory. It gives you instant, sub-millisecond semantic search across millions of data points. A true, zero-latency second brain that never phones home and never risks a data leak Bookmark this so you don't lose it

THIS BUILDER JUST LINKED FORTY RTX 3090s IN HIS BEDROOM TO BYPASS THE CLOUD AND RUN 1TB OF LOCAL VRAM. WHY DOES HE HAVE SO MUCH?? Let that number sink in for a minute. Nearly a full terabyte of raw, unadulterated VRAM sitting right next to someone's bed. With forty RTX 3090s linked up in a custom rack, you aren't just running basic local chat models anymore You are essentially operating a localized data center. It completely changes the math on what you can actually execute without a cloud subscription For starters, you can comfortably host giant, fully unquantized open weights models. I'm talking about running massive enterprise reasoning architectures like DeepSeek-R1 671B or dense Llama 405B models at full precision. Most people have to slice these models down, compressing them until they lose their edge. On a rig like this, they run completely uncompressed But it gets weirder. You can launch massive multi-agent autonomous swarms. Imagine spinning up 300 to 400 distinct AI agents simultaneously ➜ each running its own heavy coding or data-scraping loops ➜ and letting them interact in real time + You can build a massive, real-time semantic search engine over your entire digital life. You could feed decades of personal data, code repositories, thousands of books, and full video transcriptions into a localized vector database. The system can keep the entire index permanently hot in the graphics memory. It gives you instant, sub-millisecond semantic search across millions of data points. A true, zero-latency second brain that never phones home and never risks a data leak Bookmark this so you don't lose it

beamnxw ./

28,263 次观看 • 1 个月前

A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.

A 2TB HOME AI SUPERCOMPUTER CAN RUN MODELS MOST PEOPLE STILL RENT FROM THE CLOUD The video is not about a clean desk setup. It is a local AI rack: multiple mini-machines stitched together, 2TB of memory, enough compute to run heavyweight models like DeepSeek 671B without asking AWS for permission every hour. That matters because the cloud version of AI looks cheap until you use it like infrastructure. Agents running all day. Private docs. Long context. Local storage. Experiments. Fine-tuning. Automation. Suddenly the bill is not $20/month anymore. This setup is the opposite trade. Pay upfront, own the machine, keep the data local, and stop treating every serious AI workload like a subscription meter. The article is pointing at the same bigger shift: serious builders are moving from "which chatbot is best" to "where does my intelligence actually run." Cloud is convenient. Local compute is control. The next edge is not just better prompts. It is owning the box that keeps thinking when the browser tab is closed.

Nekt0

54,212 次观看 • 1 个月前

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below

THIS GUY WIRED GMKTEC EVO-X2 BOXES, A $40 USB-C BRICK AND AN RTX 3090 INTO A $6,800/MONTH OFFLINE AI FARM it looks like a messy rack with purple cables, mini pcs and a gpu lying on the desk. but the real point is brutal: one $1,800 gmktec evo-x2 can replace a $440/month ai stack, then the rack turns it into a local backend. the evo-x2 runs amd’s ryzen ai max+ 395 with 128gb of unified memory shared between cpu, gpu and npu. on linux, that gives up to 110gb of usable vram for local models without renting cloud compute. that is why the setup gets weird. qwen3-coder 30b, llama 3.3 70b, deepseek v3 and 200b-class models can run from boxes smaller than consoles. the rtx 3090 becomes a 24gb side node, not the whole system. the $40 anker nano ii 65w brick is the funny part. cheap power, tiny machines, one old gpu and local inference. the whole stack starts looking less like a workstation and more like a private ai grid. the subscription math is ugly. claude code max is $200/month, chatgpt pro is $200/month, cursor is $20/month and gemini adds another $20. that is $5,280/year before api usage even starts. point claude code at localhost, pull models through ollama and run agents all day: code reviews, scraping, transcription, research reports and client automations. 3 clients at $2,200/month turns this into a $6,800/month backend. bookmark this and read the article below

Gipp 🦅

20,031 次观看 • 1 个月前

Don't Buy a Mac Mini for Clawdbot: The Secret $10,000 Architecture That Costs You Nothing clawdbot might be the reason you feel like you need a ten thousand dollar computer right now but i am about to show you why that fomo is going to leave you broke. if you have been watching everyone rush out to buy mac minis and mac studios just to run open claw or some local models you are witnessing a massive transfer of wealth from your pocket to apple for no reason. there is a specific setup i use that costs almost nothing and keeps my main machine safe from whatever these autonomous agents are doing. if you stick with me i will walk you through the exact architecture of a professional trading system that handles the heavy lifting without you needing to drop a single rack on hardware most people are scared of running these bots on their main computer because they don't want an agent messing with their personal files or browser sessions. instead of buying a second mac mini for six hundred dollars you can just go to the top left of your screen and create a brand new user profile. this acts like a completely isolated sandbox where you can install all your trading tools and agents without them ever seeing your main data. it is essentially like getting a free computer for the price of five minutes of clicking around your settings but what if you aren't on a mac or you need to access your system while you are traveling without carrying three laptops in your backpack. this is where the first loop of professional automation starts to close because i use something called chrome remote desktop to bridge the gap. this allows me to leave a dedicated machine running in a safe place while i access the full desktop environment from a tablet or a cheap laptop anywhere in the world. it solves the mobility issue but it still doesn't solve the problem of those massive ten thousand dollar price tags for high end mac pros if you are a pc user or just someone who doesn't want to own physical hardware yet you should look into a windows vps through a provider like contabo. most developers will tell you to use a linux terminal but if you aren't a coder yet you need a visual interface you can actually see. getting a windows server allows you to log in and see a desktop just like your home computer for about fifteen dollars a month. i usually recommend at least twelve gigabytes of ram to keep things from getting janky when you are running multiple browser windows and agents at once now you might be thinking that the whole point of the big hardware was to run local models like kimi or glm to save on api costs. i spent years thinking i had to own the machines myself and i even spent hundreds of thousands on developers before i realized i could just do this myself. the secret to running those massive open source models without the ten thousand dollar investment is renting gpu power by the hour. sites like lambda labs let you spin up a monster machine that can run any model in existence for just a couple dollars an hour this is the ultimate pivot because it allows you to test if your strategy actually prints money before you commit to the hardware. you can turn the server on when you are iterating and turn it off the second you are done which keeps your overhead near zero. if you haven't proven that your bot can pay for itself yet then buying a mac studio is just an expensive hobby rather than a business move. there is a much bigger loophole involving the anthropic subscriptions that most people are completely overlooking right now right now i am using a specific plan with claude code that costs about two hundred dollars a month but it lets me run open claw all day without hitting api limits. if i were paying for those same tokens through the standard api i would probably be spending hundreds of dollars every single day. it is a massive cost savings that allows you to iterate and fail until you find a winning strategy without draining your bank account. even if they eventually close this loophole or snitch on the usage patterns it serves as the perfect training ground for a data dog the goal is to find a system that works with a smaller or cheaper model like haiku before you ever try to scale up to the heavy weights. if you can make a strategy profitable using a less intelligent and cheaper model then you know you have found real alpha. once you have that foundation you can decide if it finally makes sense to build your own custom pc rig which will always be half the price of an apple machine. i am an apple guy so i usually pay the tax anyway but i only do it once the system is already generating enough to cover the cost ten times over i believe that code is the great equalizer because it took me from losing money and getting liquidated to having fully automated systems doing the work for me. i had to learn to live with the iterations and the failures on youtube to get to this point of clarity. the universe tends to get out of your way once you make a non negotiable contract with yourself to see the process through to the end. you don't need the flashy hardware or the most expensive setup to start winning in this game stay focused on the logic and the data rather than the hype and the fomo that everyone else is falling for. if you can master the bridge between renting power and owning your logic you will be ahead of ninety nine percent of the people in this space. the path to a fully automated life isn't paved with expensive gadgets but with the discipline to iterate until the system finally prints

Moon Dev

17,382 次观看 • 6 个月前

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.

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 次观看 • 1 个月前

$THIS IS WHERE "THE CLOUD" ACTUALLY LIVES - A ROOM OF DELL STORAGE ARRAYS YOU RENT A SLIVER OF EVERY MONTH. that clip is a real datacenter aisle, rack after rack of dell powerstore arrays, every hexagon panel packed with drives. this is "the cloud." not a metaphor, this room, plus the power, cooling and staff keeping it alive. when you pay for cloud storage, you rent a few gigabytes of these arrays. forever. the bill grows with every photo. here's the part they'd rather you not connect. these enterprise drives run on a schedule. data centers swap them out long before they fail, by the pallet. those decommissioned drives get tested and resold as recertified, the same 20tb enterprise disk for a fraction of new. so the exact hardware humming in this room ends up in a nas on your desk, minus the monthly rent. no cloud subscription, no data on someone else's array, no bill that grows with your library. the uncomfortable part isn't that the cloud is huge. it's that "the cloud" was always just this room, and you can buy a piece of it once instead of renting it forever. follow RetroChainer for what the cloud doesn't want you to picture, and bookmark this before your next storage renewal.$

THIS IS WHERE "THE CLOUD" ACTUALLY LIVES - A ROOM OF DELL STORAGE ARRAYS YOU RENT A SLIVER OF EVERY MONTH. that clip is a real datacenter aisle, rack after rack of dell powerstore arrays, every hexagon panel packed with drives. this is "the cloud." not a metaphor, this room, plus the power, cooling and staff keeping it alive. when you pay for cloud storage, you rent a few gigabytes of these arrays. forever. the bill grows with every photo. here's the part they'd rather you not connect. these enterprise drives run on a schedule. data centers swap them out long before they fail, by the pallet. those decommissioned drives get tested and resold as recertified, the same 20tb enterprise disk for a fraction of new. so the exact hardware humming in this room ends up in a nas on your desk, minus the monthly rent. no cloud subscription, no data on someone else's array, no bill that grows with your library. the uncomfortable part isn't that the cloud is huge. it's that "the cloud" was always just this room, and you can buy a piece of it once instead of renting it forever. follow RetroChainer for what the cloud doesn't want you to picture, and bookmark this before your next storage renewal.

RetroChainer

33,053 次观看 • 4 天前

ELON MUSK: The only way to reach 1,000 terawatts of AI power is a mass driver on the Moon. "In order to get to 1,000x from a terawatt per year. The only way that we can really achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics and radiators on the moon, maybe you bring the chips from Earth, or you could conceivably make the chips on the moon, and but you need most of the mass to be made on the moon, so you don't have to transport it to the moon from Earth, and then because the moon has no atmosphere and only 1/6 Earth's gravity, you can accelerate the AI satellites into deep space without a rocket, so you can basically shoot them into space using an electromagnetic gun, like a, like a rail gun type. I mean, just, it's basically a linear electric motor, as a way to think about it."

ELON MUSK: The only way to reach 1,000 terawatts of AI power is a mass driver on the Moon. "In order to get to 1,000x from a terawatt per year. The only way that we can really achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics and radiators on the moon, maybe you bring the chips from Earth, or you could conceivably make the chips on the moon, and but you need most of the mass to be made on the moon, so you don't have to transport it to the moon from Earth, and then because the moon has no atmosphere and only 1/6 Earth's gravity, you can accelerate the AI satellites into deep space without a rocket, so you can basically shoot them into space using an electromagnetic gun, like a, like a rail gun type. I mean, just, it's basically a linear electric motor, as a way to think about it."

DogeDesigner

122,377 次观看 • 1 个月前

This is Serious 🚨 US Government voting on new regulations this November that are so expensive they will force small ranchers out of business Mandating new equipment that will cost up to $2.3 million plus $500k to install and regulate each year “This is an attack on our food system” “So this November, the EPA is gonna be voting on a bill to implement new water treatment programs into all local butcher shops all the way up to the big packers. Now these new water treatment programs are gonna be costing up to $2,300,000 just for all the new equipment and an additional $500,000 to get them installed and regulated into each year. This is an attack on our food system if you were trying to buy locally or from your local producers or from the local butcher shops. They simply do not have the money or the revenue to support this. So in a roundabout way, they're taking away your freedom and your right and our right to sell meat directly to the consumer. They're taking away our rights to buy what we want and consume and put in our bodies what we would like. They're forcing us into a box and forcing you guys to buy from the big packers that control 85% of the industry right now. I'm curious to see what y'all think about this. And as always, guys, buy American and buy local.”

This is Serious 🚨 US Government voting on new regulations this November that are so expensive they will force small ranchers out of business Mandating new equipment that will cost up to $2.3 million plus $500k to install and regulate each year “This is an attack on our food system” “So this November, the EPA is gonna be voting on a bill to implement new water treatment programs into all local butcher shops all the way up to the big packers. Now these new water treatment programs are gonna be costing up to $2,300,000 just for all the new equipment and an additional $500,000 to get them installed and regulated into each year. This is an attack on our food system if you were trying to buy locally or from your local producers or from the local butcher shops. They simply do not have the money or the revenue to support this. So in a roundabout way, they're taking away your freedom and your right and our right to sell meat directly to the consumer. They're taking away our rights to buy what we want and consume and put in our bodies what we would like. They're forcing us into a box and forcing you guys to buy from the big packers that control 85% of the industry right now. I'm curious to see what y'all think about this. And as always, guys, buy American and buy local.”

Wall Street Apes

495,915 次观看 • 2 年前

THIS GUY IS WALKING INTO LOCAL SHOPS WITH A MAC MINI IN A HARD CASE AND WALKING OUT WITH $1,200 CHECKS FOR INSTALLING AI THAT RUNS WITHOUT THE INTERNET I had to rewatch this twice because the business model is so stupidly simple my brain kept looking for the catch. He shows up. Plugs in a Mac Mini. Installs a private AI network running entirely on Ollama. The whole thing is done in under an hour. Zero cloud dependency. No API bills. No monthly charges bleeding the client dry. The shop owner gets a local AI system that works even if the WiFi dies, and this guy walks out with $1,200 for an installation that takes less time than a long lunch. The hardware costs $1,300. One deployment and the machine is paid for. Then he locks in a $149 monthly retainer for support, which means every client after the first is pure margin stacking on top of recurring revenue that compounds every single month. Most people hear "AI business" and think they need to build a SaaS platform or learn to code or raise funding from someone. This guy skipped all of that and went straight to walking into physical stores with a box under his arm. This is not going to scale to a billion dollar company. Obviously. But a solo operator clearing $5-10K a month from local installs while everyone else is still arguing about which LLM is best on Twitter is the kind of quiet hustle that does not make headlines until someone does a breakdown of their year-end numbers. The surface area of what AI can do right now is expanding faster than anyone can map it. One guy is doing local hardware installs. Another is generating AI personas that pull five figures a month. Someone else is running AI video pipelines off a laptop. Same wave of tools. None of them competing with each other. Hit play. Watch him unbox the setup and walk through the numbers. Then tell me you could not do this in your city by next Friday.

THIS GUY IS WALKING INTO LOCAL SHOPS WITH A MAC MINI IN A HARD CASE AND WALKING OUT WITH $1,200 CHECKS FOR INSTALLING AI THAT RUNS WITHOUT THE INTERNET I had to rewatch this twice because the business model is so stupidly simple my brain kept looking for the catch. He shows up. Plugs in a Mac Mini. Installs a private AI network running entirely on Ollama. The whole thing is done in under an hour. Zero cloud dependency. No API bills. No monthly charges bleeding the client dry. The shop owner gets a local AI system that works even if the WiFi dies, and this guy walks out with $1,200 for an installation that takes less time than a long lunch. The hardware costs $1,300. One deployment and the machine is paid for. Then he locks in a $149 monthly retainer for support, which means every client after the first is pure margin stacking on top of recurring revenue that compounds every single month. Most people hear "AI business" and think they need to build a SaaS platform or learn to code or raise funding from someone. This guy skipped all of that and went straight to walking into physical stores with a box under his arm. This is not going to scale to a billion dollar company. Obviously. But a solo operator clearing $5-10K a month from local installs while everyone else is still arguing about which LLM is best on Twitter is the kind of quiet hustle that does not make headlines until someone does a breakdown of their year-end numbers. The surface area of what AI can do right now is expanding faster than anyone can map it. One guy is doing local hardware installs. Another is generating AI personas that pull five figures a month. Someone else is running AI video pipelines off a laptop. Same wave of tools. None of them competing with each other. Hit play. Watch him unbox the setup and walk through the numbers. Then tell me you could not do this in your city by next Friday.

Framez

929,243 次观看 • 17 天前

Just so everyone is aware... At this present-time in America, if you want to set up a protest against the data center construction in your town, and call out your local representative while doing so that is now an ARRESTABLE OFFENSE. It doesn't matter what you actually say on social media, it is apparently up to the state DA, and local police. Arrested...WITHOUT a warrant, or probable cause because they don't like what you said online and twisted it to turn it into a "threat". If you protest your enslavement by the Technocratic NWO, and especially if you get others to join you, you will be treated like a CRIMINAL. This is what we all have to look forward to in the "land of the free" and why I cannot bring myself to celebrate our 250 years of "freedom".

Just so everyone is aware... At this present-time in America, if you want to set up a protest against the data center construction in your town, and call out your local representative while doing so that is now an ARRESTABLE OFFENSE. It doesn't matter what you actually say on social media, it is apparently up to the state DA, and local police. Arrested...WITHOUT a warrant, or probable cause because they don't like what you said online and twisted it to turn it into a "threat". If you protest your enslavement by the Technocratic NWO, and especially if you get others to join you, you will be treated like a CRIMINAL. This is what we all have to look forward to in the "land of the free" and why I cannot bring myself to celebrate our 250 years of "freedom".

The Patriot Voice

65,370 次观看 • 1 个月前

$Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic$

Extropic founder Beff (e/acc) on why running probabilistic AI on deterministic hardware is quietly the biggest local optimum in computing "You have Landauer's principle. Essentially you have a minimum cost of erasing a bit." "Every time you reset a bit in a classical computer, your bit is actually random. You don't know what state it is, and then you have to reset it to a deterministic state. So you have a Maxwell's demon for every bit, for every clock cycle. I'm tired of paying the demon tax." "If you go with fractional bits, you don't need to do full bit operations. Running hardware probabilistically is just much more power efficient." "All the algorithms we want to run these days, whether it's PageRank, whether it's recommender systems, whether it's diffusion models, whether it's transformers, they're all probabilistic algorithms." "Why are we paying the Maxwell's demon tax to run probabilistic algorithms on deterministic hardware? It just makes zero sense, and we've just been caught in a local optima." "It takes a true set of autists that are like, screw the whole stack, we're going to start from scratch. This has the bones to eat the world." Extropic

MTS

33,856 次观看 • 9 天前

INTEL JUST SHIPPED A WORKSTATION CARD WITH TWO GPUs ON ONE PCB AND 48GB OF VRAM. FOUR CARDS GIVE A SINGLE MOTHERBOARD 192GB OF POOLED INFERENCE MEMORY FOR THE PRICE OF TWO RTX 5090s 00:14 he holds one card up to the camera, two GPU dies side by side under the cooler, each one running its own x8 PCIe lane back to the chipset the cluster sees eight discrete accelerators in software. intel's Battle Matrix stack shards a model across all eight, so a 235B parameter network loads in slices and answers requests in parallel what 192GB of VRAM unlocks: an entire 200B class model in memory without quantization. a vision agent reading 100 invoices at once. a research box that holds three frontier models loaded simultaneously, switching between them in under a second intel is the slow side of inference. nvidia is faster per token, that is the honest tradeoff. but the only other path to this much VRAM is a $40,000 nvidia rack or three networked Mac Studios four B60 cards plus the chassis lands under $5,000. power draw averages 800 watts, $55 a month in electricity. one engineer paying $400 a month for combined ChatGPT Pro and Claude Code Max pays the hardware off in less than a year

INTEL JUST SHIPPED A WORKSTATION CARD WITH TWO GPUs ON ONE PCB AND 48GB OF VRAM. FOUR CARDS GIVE A SINGLE MOTHERBOARD 192GB OF POOLED INFERENCE MEMORY FOR THE PRICE OF TWO RTX 5090s 00:14 he holds one card up to the camera, two GPU dies side by side under the cooler, each one running its own x8 PCIe lane back to the chipset the cluster sees eight discrete accelerators in software. intel's Battle Matrix stack shards a model across all eight, so a 235B parameter network loads in slices and answers requests in parallel what 192GB of VRAM unlocks: an entire 200B class model in memory without quantization. a vision agent reading 100 invoices at once. a research box that holds three frontier models loaded simultaneously, switching between them in under a second intel is the slow side of inference. nvidia is faster per token, that is the honest tradeoff. but the only other path to this much VRAM is a $40,000 nvidia rack or three networked Mac Studios four B60 cards plus the chassis lands under $5,000. power draw averages 800 watts, $55 a month in electricity. one engineer paying $400 a month for combined ChatGPT Pro and Claude Code Max pays the hardware off in less than a year

NO1ennn

48,054 次观看 • 1 个月前

LAWRENCE LEPARD: "This is one of the best investment opportunities out there. It actually is the best in my opinion, if you can handle the volatility... having a zero allocation to it is just absolutely nuts." "When you buy into a network that's going to get adopted by everybody, the returns are just insane. My opinion is that Bitcoin is the monetary network of the future." "...And when it is... guess what, these are 5 million a coin or more." "The people that run the government, their major goal is to keep themselves in power and to keep the system running. They don't care about inflation. They say they do, but they don't. And they lie to us about that. And we know that if and when the sharp end of the stick comes for them, they're going to print the money to keep things going."

LAWRENCE LEPARD: "This is one of the best investment opportunities out there. It actually is the best in my opinion, if you can handle the volatility... having a zero allocation to it is just absolutely nuts." "When you buy into a network that's going to get adopted by everybody, the returns are just insane. My opinion is that Bitcoin is the monetary network of the future." "...And when it is... guess what, these are 5 million a coin or more." "The people that run the government, their major goal is to keep themselves in power and to keep the system running. They don't care about inflation. They say they do, but they don't. And they lie to us about that. And we know that if and when the sharp end of the stick comes for them, they're going to print the money to keep things going."

Simply Bitcoin

31,954 次观看 • 1 个月前

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Unsloth AI and NVIDIA are Revolutionizing Local LLM Fine-Tuning: From RTX Desktops to DGX Spark Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance on massive, generalized cloud models and entering the era of local, agentic AI. Whether it is tuning a chatbot to handle hyper-specific product support or building a personal assistant that manages intricate schedules, the potential for generative AI on local hardware is boundless. However, developers face a persistent bottleneck: How do you get a Small Language Model (SLM) to punch above its weight class and respond with high accuracy for specialized tasks? The answer is Fine-Tuning, and the tool of choice is Unsloth. Unsloth provides an easy and high-speed method to customize models. Optimized for efficient, low-memory training on NVIDIA GPUs, Unsloth scales effortlessly from GeForce RTX desktops and laptop all the way to the DGX Spark, the world’s smallest AI supercomputer...... Full analysis: NVIDIA NVIDIA AI NVIDIA AIDev NVIDIAnewsroom Unsloth AI Unsloth

Marktechpost AI Dev News ⚡

31,551 次观看 • 7 个月前

.Matt Gorman: "There's a very simple remedy to [the unrest in Minneapolis]. Let state and local law enforcement let ICE into the jails to get the worst of the worst out. This is happening because they aren't cooperating and we're seeing this in Virginia as well. One of the first things Abigail Spanberger did was revoke a Youngkin executive order so state and local are not compelled to enforced immigration law. The goal of Democrats is to make all this enforcement as messy and as complicated as possible so you get a lot of these scenes. And, look, for the last couple years Democrats have preached that we're a nation of laws, we're a nation of laws except when it comes to enforcing immigration laws or, in the Clintons, the need to comply with a subpoena. Then, the law is merely a suggestion. If you wanna comply with it, it's up to you." Marie Harf: "So, you're okay with these videos?"

.Matt Gorman: "There's a very simple remedy to [the unrest in Minneapolis]. Let state and local law enforcement let ICE into the jails to get the worst of the worst out. This is happening because they aren't cooperating and we're seeing this in Virginia as well. One of the first things Abigail Spanberger did was revoke a Youngkin executive order so state and local are not compelled to enforced immigration law. The goal of Democrats is to make all this enforcement as messy and as complicated as possible so you get a lot of these scenes. And, look, for the last couple years Democrats have preached that we're a nation of laws, we're a nation of laws except when it comes to enforcing immigration laws or, in the Clintons, the need to comply with a subpoena. Then, the law is merely a suggestion. If you wanna comply with it, it's up to you." Marie Harf: "So, you're okay with these videos?"

Curtis Houck

27,575 次观看 • 6 个月前

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 次观看 • 25 天前

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

EIGHT USED RTX 3090s. 192GB OF VRAM. ONE PRIVATE BOX BUILT TO RUN 70B AI MODELS WITHOUT RENTING A DATACENTER. 00:06 the team lowers all eight GPUs into the chassis at once, turning a pile of old gaming cards into the core of a serious local AI server. each 3090 contributes 24GB of memory. combined, the rack has enough VRAM for heavyweight models, large document collections, long context and multiple jobs running side by side. these cards are five years old, but AI economics work differently from gaming. memory capacity matters more than having the newest badge, which is why used 3090s remain valuable. the server can keep one model loaded while separate GPUs handle video, images, transcription and overnight agents without sending private company data into the cloud. bookmark this build before yesterday’s gaming GPUs become tomorrow’s cheapest AI infrastructure.

Gipp 🦅

118,857 次观看 • 1 个月前

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

Chris Tate

42,558 次观看 • 1 年前

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Google Brain founder, Andrew Ng: "One of the most important skills of the future is the ability to tell a computer exactly what you want so it can do it for you." In this talk, he explains why the biggest opportunity isn't just using AI. It's learning how to turn work into agentic workflows, break tasks into steps, give agents the right tools and context, add evals, and make the whole system reliable. That's the difference between prompting an AI and engineering an agent that actually works. Watch it today, then read the full guide in the article below.

Rahul

11,553 次观看 • 4 天前