Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

4 MAC MINIS RUNNING A 671B PARAMETER MODEL AS A CLUSTER No data center, no Cloud, no expensive hardware and not a single API call.. Just 4 Mac minis connected through EXO running DeepSeek v3.1 671b locally and actually fast. The part nobody talks about is that you don’t...

slash1sol's profile picture

slash1s

8,250 subscribers

58,724 görüntüleme • 5 ay önce •via X (Twitter)

Bilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below
0:55

THIS GUY STACKED MAC MINIS TO RUN AI LOCALLY AND IT CODES FOR FREE no API. no subscription. no cloud. what he's running: - EXO across multiple Mac Minis - Qwen 2.5 Coder 7B locally - Zed editor connected to the model - GPU usage visible in real time the math: - Mac Mini M4: $599 one-time - electricity: $3/month - API costs: $0 vs $459/month in subscriptions if you want Claude Code to run at this level ClaudeKit gives it the structure to do that ( full breakdown in the article below

Madni Aghadi

26,421 görüntüleme • 4 ay önce

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.
0:25

100 MAC MINIS ON ONE METAL SHELF. IN THE CLOUD, EACH OF THOSE RENTS FOR $120 TO $500 A MONTH. that clip is a homemade rack: wire shelving packed with mac minis, each running macos, wired into one cluster. why macs and not a normal server? two reasons. you can only build, sign, and notarize ios and mac apps on apple hardware. cloud providers know it and charge for it. and apple silicon sips power and runs near-silent, so you can stack a lot of them on a shelf in a room, not a datacenter. the rent they're escaping: a single mac in the cloud runs ~$120 to $500 a month (macstadium up to aws) one engineer documented saving $4,000+ a month by self-hosting mac minis instead of cloud ci runners a used or new mini is a few hundred dollars once, then a few dollars of electricity what a shelf like this actually runs: ios and mac build farms, render jobs, app and device testing automation, and increasingly local ai inference. own the boxes, rent nothing. the uncomfortable part: the cloud was never the only option. it was the convenient one. the people who did the math bought the shelf. the honest caveat: it's real capex up front, real heat and power, and you become the sysadmin. no rented rack means no one to call at 3am. worth it at scale, overkill for one build a week. no rack rental, no per-hour metering, no fleet you don't own. save this before your cloud invoice renews again.

RetroChainer

21,055 görüntüleme • 3 ay önce

CHINESE BUSINESSMAN BOUGHT 4 MAC MINIS FOR $1,699 AND CONNECTED THEM TO HIS MACBOOK - NOW THE CORNER OF HIS DESK MAKES MORE THAN MOST OFFICES MacBook Pro 16" runs everything - assigns tasks, controls the flow, holds context - 4 Mac Minis in the corner execute inference in parallel and don't know what downtime means he connected them through EXO - an open source framework that combines multiple Apple Silicon devices into one unified cluster - and together they run models no single device could handle alone while everyone else rents cloud GPUs for $1,900/month he paid $1,699 once and the entire cluster now runs on $20/month in electricity and nothing else clients pay for inference, data never leaves the room and the cluster scales by simply placing one more Mac Mini on top the MacBook is what he works on - the Mac Minis are what work for him around the clock without days off $1,699 invested once - and a corner of a desk that makes more than most companies with a rented office
0:20

CHINESE BUSINESSMAN BOUGHT 4 MAC MINIS FOR $1,699 AND CONNECTED THEM TO HIS MACBOOK - NOW THE CORNER OF HIS DESK MAKES MORE THAN MOST OFFICES MacBook Pro 16" runs everything - assigns tasks, controls the flow, holds context - 4 Mac Minis in the corner execute inference in parallel and don't know what downtime means he connected them through EXO - an open source framework that combines multiple Apple Silicon devices into one unified cluster - and together they run models no single device could handle alone while everyone else rents cloud GPUs for $1,900/month he paid $1,699 once and the entire cluster now runs on $20/month in electricity and nothing else clients pay for inference, data never leaves the room and the cluster scales by simply placing one more Mac Mini on top the MacBook is what he works on - the Mac Minis are what work for him around the clock without days off $1,699 invested once - and a corner of a desk that makes more than most companies with a rented office

Noisy

22,391 görüntüleme • 5 ay önce

Speed running my home AI cluster running distributed inference across 2 MacBooks and 2 Mac Minis. exo intern displays a real-time network topology as devices discover each other over the local network. Code is open source 👇
0:41

Speed running my home AI cluster running distributed inference across 2 MacBooks and 2 Mac Minis. exo intern displays a real-time network topology as devices discover each other over the local network. Code is open source 👇

Alex Cheema

110,105 görüntüleme • 2 yıl önce

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below
2:00

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

194,375 görüntüleme • 5 ay önce

Have you been mass buying Mac Minis for your new OpenClaw🦞 workflows? One man who skated to puck is Alex Cheema, founder of EXO Labs! He joins TWiST today to show @jason how his open source software helps users connect all of their Mac Minis. Check out how you can start running your own AI compute cluster!
5:26

Have you been mass buying Mac Minis for your new OpenClaw🦞 workflows? One man who skated to puck is Alex Cheema, founder of EXO Labs! He joins TWiST today to show @jason how his open source software helps users connect all of their Mac Minis. Check out how you can start running your own AI compute cluster!

This Week in Startups

680,613 görüntüleme • 9 ay önce

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables
21:59

APPLE RESEARCH SCIENTIST JUST SHOWED HOW 4 MAC STUDIOS RUN A TRILLION PARAMETER MODEL LOCALLY ZERO COSTS 13:18 she shows the main thing - connect 4 Mac Studios and you get 1TB of shared memory - exactly enough to run a trillion parameter model right on your desk Apple's library - and four machines start working as one cluster tensor parallelism: every machine holds part of every layer - all process the same token simultaneously - speed increases 3x compared to a single device fine-tuning: one Mac Studio processes 180 tokens per second four together process 600 and not a single byte of data leaves the room one command in the terminal - and a trillion parameter model answers from your desk and runs 24/7 data centers took years to build to run models like this - Apple did it with four Thunderbolt cables

Noisy

49,021 görüntüleme • 4 ay önce

running a 405-billion-parameter model used to mean a $30,000 GPU rack. one developer made it run on a single 8GB card the kind that's been sitting in gaming laptops for years. it's called AirLLM, and the trick is almost stupidly simple: instead of loading the whole model, it keeps one layer on the GPU at a time and streams the rest through. / Llama 3.1 405B on 8GB / DeepSeek-V3, all 671B of it, on about 12GB / no quantization, no distillation, no pruning the full-precision model / works with Llama, Qwen, DeepSeek, Mistral, Gemma, and more it's Apache 2.0 and already past 22,600 stars. → bookmark it.
0:21

running a 405-billion-parameter model used to mean a $30,000 GPU rack. one developer made it run on a single 8GB card the kind that's been sitting in gaming laptops for years. it's called AirLLM, and the trick is almost stupidly simple: instead of loading the whole model, it keeps one layer on the GPU at a time and streams the rest through. / Llama 3.1 405B on 8GB / DeepSeek-V3, all 671B of it, on about 12GB / no quantization, no distillation, no pruning the full-precision model / works with Llama, Qwen, DeepSeek, Mistral, Gemma, and more it's Apache 2.0 and already past 22,600 stars. → bookmark it.

dunik

151,882 görüntüleme • 3 ay önce

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.
1:15

Google just shipped a free dictation app for Mac and iPhone called AI Edge Eloquent, and the model running it is Gemma 4 12B, entirely on your device. What it actually does: you speak, it transcribes, removes filler words, and polishes the result before dropping clean text into whatever app you are using. You can also give it voice commands to reformat, like "translate to Hindi" or "make this a bullet list." Audio and video file transcription is built in too. The interesting part is the privacy angle. Most AI writing tools send your audio to a server. This one never leaves your machine, which matters if you are dictating anything sensitive. The tradeoff is hardware: you need a Mac with at least 16GB of RAM to run a 12B parameter model comfortably.

Aditya ⚡Rao

85,151 görüntüleme • 5 ay önce

Someone put a Claude Code sticker on a $599 Mac Mini and accidentally built the most productive setup in tech. Dedicated machine, zero distractions, Claude 4.7 Opus running 24/7 with one prompt on the screen. What do you want the computer to do? Fork a repo, write the code, create the file, find the article, handle whatever comes next, repeat. No laptop, no cloud subscription, no context switching, no "let me pull that up real quick" Just a box the size of a hardcover book sitting on a desk doing the work of a full time hire around the clock. People stopped using Mac Minis as computers, they're using them as Claude, $599 once and it never clocks out.
1:36

Someone put a Claude Code sticker on a $599 Mac Mini and accidentally built the most productive setup in tech. Dedicated machine, zero distractions, Claude 4.7 Opus running 24/7 with one prompt on the screen. What do you want the computer to do? Fork a repo, write the code, create the file, find the article, handle whatever comes next, repeat. No laptop, no cloud subscription, no context switching, no "let me pull that up real quick" Just a box the size of a hardcover book sitting on a desk doing the work of a full time hire around the clock. People stopped using Mac Minis as computers, they're using them as Claude, $599 once and it never clocks out.

Defileo🔮

70,389 görüntüleme • 6 ay önce

This is all you need for running Deepseek R1 671B at home. Her the model with Q4 running on a $2000 Local Machine - 3.5 to 4 tokens per second. 512GB DDR4 RAM Video from "Digital Spaceport" YT Channel (link in comment)
0:59

This is all you need for running Deepseek R1 671B at home. Her the model with Q4 running on a $2000 Local Machine - 3.5 to 4 tokens per second. 512GB DDR4 RAM Video from "Digital Spaceport" YT Channel (link in comment)

Rohan Paul

312,156 görüntüleme • 1 yıl önce

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours
22:35

THIS DEVELOPER JUST RAN A TRILLION PARAMETER MODEL ON 4 MAC STUDIOS - 10X FASTER AND 5X CHEAPER THAN CLOUD CODE 19:00 he says it out loud. "we just ran a trillion parameter model. 30 something tokens per second. wow." RDMA over Thunderbolt made the cluster 10x faster than before. tensor parallelism splits the model between machines in parallel instead of sequentially DeepSeek V3 full size without quantization. 183GB per machine. still 25 tokens per second. faster than most people can read Kimi K2 with trillion parameters and 256,000 context window. loaded, responded and adapted memory usage dynamically based on prompt size 4 machines consuming 66 watts total. cloud GPU for the same workload costs $1,900/month a cluster that used to cost millions now assembled from Mac Studios in a few hours

Noisy

80,087 görüntüleme • 4 ay önce

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product
2:51

THIS VETERAN DEVELOPER PUT 2 MAC MINIS ON HIS DESK FOR $1,198 AND TURNED HERMES INTO A LOCAL AI WORKSPACE THAT DOESN’T NEED A $210/MONTH AGENT STACK he is not flexing hardware. he is showing the part most people still miss: once Hermes runs locally, the laptop stops being a chat window and starts acting like a workspace with memory, tools and saved skills two Mac minis, one local setup, zero cloud agent dashboard. research tasks, summaries, client notes and workflow steps stay on his machine instead of disappearing every time a session ends most people keep paying for Claude, wrappers, API calls and automation tools just to repeat the same instructions every day. Hermes cuts the waste by saving the process once and pulling it back when the same job returns the math is ugly for cloud tools. $210/month turns into $2,520/year before you even count extra tokens. his version is hardware once, local workflows after, and no panic when rate limits hit the quiet winners will not be the people with the cleanest chatbot tab. it will be the people who own the machine, the memory and the workflow before everyone else realizes that is the product

Gipp 🦅

60,173 görüntüleme • 5 ay önce

FOUR BOXES ON A DESK ARE NOW A FRONTIER AI CLUSTER This is four NVIDIA DGX Spark units wired together, sitting on a single desk and running Qwen3-235B end to end. Each box holds 128GB of unified memory. Stacked together, that is half a terabyte of memory pooled across the cluster, all from hardware you could fit in a backpack. A year ago a setup like this lived in a data center behind a badge reader. Now it runs next to a monitor and a coffee cup. Here is the part people get wrong. The four boxes do not magically merge into one giant GPU. The model is sharded across all of them, and they pass data over a fast link while every box does its slice of the work. That is the whole trick. No single box could hold a 235B model, but split four ways, it runs. The machines stay quiet and barely warm while doing it, sipping a fraction of the power a rack of server GPUs would burn. The wall between you and a frontier-class model used to be a cloud bill. Now it is just desk space. Bookmark this & Watch it run ↓
1:42

FOUR BOXES ON A DESK ARE NOW A FRONTIER AI CLUSTER This is four NVIDIA DGX Spark units wired together, sitting on a single desk and running Qwen3-235B end to end. Each box holds 128GB of unified memory. Stacked together, that is half a terabyte of memory pooled across the cluster, all from hardware you could fit in a backpack. A year ago a setup like this lived in a data center behind a badge reader. Now it runs next to a monitor and a coffee cup. Here is the part people get wrong. The four boxes do not magically merge into one giant GPU. The model is sharded across all of them, and they pass data over a fast link while every box does its slice of the work. That is the whole trick. No single box could hold a 235B model, but split four ways, it runs. The machines stay quiet and barely warm while doing it, sipping a fraction of the power a rack of server GPUs would burn. The wall between you and a frontier-class model used to be a cloud bill. Now it is just desk space. Bookmark this & Watch it run ↓

slash1s

19,473 görüntüleme • 4 ay önce

SF Compute CEO evan conrad explains why GPU clusters are bad collateral for lenders: "This article was more of a prediction of a trend that is already happening. I probably should’ve said, currently, most startups cannot buy compute. It is only getting harder and harder and harder." "If you want to finance a GPU cluster, you need to first have collateral, which is typically the hardware itself. The problem with that collateral is nobody wants to buy the hardware. They want to buy the hardware in a data center that’s operated by a company that works." "And the situation in which you recoup that hardware is often the situation in which the person operating that hardware is just as likely to blow up. There’s a lot of correlated risk." "If you are the startup trying to buy a cluster, to the eyes of a lender, you look like you’re gonna blow up." San Francisco Compute
1:37

SF Compute CEO evan conrad explains why GPU clusters are bad collateral for lenders: "This article was more of a prediction of a trend that is already happening. I probably should’ve said, currently, most startups cannot buy compute. It is only getting harder and harder and harder." "If you want to finance a GPU cluster, you need to first have collateral, which is typically the hardware itself. The problem with that collateral is nobody wants to buy the hardware. They want to buy the hardware in a data center that’s operated by a company that works." "And the situation in which you recoup that hardware is often the situation in which the person operating that hardware is just as likely to blow up. There’s a lot of correlated risk." "If you are the startup trying to buy a cluster, to the eyes of a lender, you look like you’re gonna blow up." San Francisco Compute

MTS

17,810 görüntüleme • 11 gün önce

NVIDIA just built the laptop chip people are already calling the MacBook killer it is called RTX Spark, a single superchip that runs heavy creative work, real gaming, and private on-device AI agents, all on one machine but before you ditch your MacBook, here is the honest part nobody in the hype is telling you: > on raw memory bandwidth, a maxed macbook is actually still ahead, not behind > the RTX Spark laptops are not out yet, they ship this fall, apple is on shelves today > where nvidia truly wins is cuda, real RTX gaming, and 3D, things apple has no answer for > one is the proven machine you can buy now, the other is the more exciting bet for later so no, the macbook is not dead, and no, this is not just hype, the truth sits in between i broke down the full thing, every spec, who wins where, and which one is actually yours the honest breakdown is in the article below
0:34

NVIDIA just built the laptop chip people are already calling the MacBook killer it is called RTX Spark, a single superchip that runs heavy creative work, real gaming, and private on-device AI agents, all on one machine but before you ditch your MacBook, here is the honest part nobody in the hype is telling you: > on raw memory bandwidth, a maxed macbook is actually still ahead, not behind > the RTX Spark laptops are not out yet, they ship this fall, apple is on shelves today > where nvidia truly wins is cuda, real RTX gaming, and 3D, things apple has no answer for > one is the proven machine you can buy now, the other is the more exciting bet for later so no, the macbook is not dead, and no, this is not just hype, the truth sits in between i broke down the full thing, every spec, who wins where, and which one is actually yours the honest breakdown is in the article below

John Doe

107,985 görüntüleme • 5 ay önce

Meta opened the weights this morning. It's already living on a Mac. Muse Glimmer 30B, running locally in Osaurus. It analyzed an image and built a playable game. Your hardware. Your model. No subscription.
0:40

Meta opened the weights this morning. It's already living on a Mac. Muse Glimmer 30B, running locally in Osaurus. It analyzed an image and built a playable game. Your hardware. Your model. No subscription.

Osaurus

23,650 görüntüleme • 2 ay önce

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

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

CyrilXBT

37,120 görüntüleme • 6 ay önce

THIS IS F*CKING OBSCENE opus 5.5 got Fortnite running inside Minecraft and Minecraft running inside GTA 5, with nobody's permission and nobody's documentation three engines, three completely different ways in, and one of them has no way in at all THREE GAMES, THREE DIFFERENT WAYS IN → Minecraft is Java and ships a loader on purpose. that one is the front door. you write against an interface somebody meant for you to use → GTA 5 has no modding api and no front door. you get a compiled binary and nothing else, and the only route in is to read it → Fortnite is a live service with anti cheat sitting on top. there is no way in at all, and that single fact changes the entire job WHAT READING A BINARY ACTUALLY MEANS → no source, no comments, no function names. you recover what code does from what it is, which is the opposite direction from how it was written → the exact build gets pinned before anything else, because one patch moves every address underneath you, and a different version is a different game → nothing counts until it is confirmed inside the running process. launch it, play it, look at it. opus 5.5 does the reading, the pinning and the checking, and that loop is the part that used to be a team and a year WHAT THIS ACTUALLY CHANGES → reverse engineering was the most expensive skill in modding. one person, one binary, months of evenings, and the knowledge died with the project → now it is a loop that runs while you sleep, and what comes out of it is not a guess, it is something that got checked against the game while the game was running → so the hard question stopped being whether you can get into an engine. it is what you do once you are standing in three of them at once one layer was read out of a binary one came through the front door one was never touched at all, and it still had to land right
1:50

THIS IS F*CKING OBSCENE opus 5.5 got Fortnite running inside Minecraft and Minecraft running inside GTA 5, with nobody's permission and nobody's documentation three engines, three completely different ways in, and one of them has no way in at all THREE GAMES, THREE DIFFERENT WAYS IN → Minecraft is Java and ships a loader on purpose. that one is the front door. you write against an interface somebody meant for you to use → GTA 5 has no modding api and no front door. you get a compiled binary and nothing else, and the only route in is to read it → Fortnite is a live service with anti cheat sitting on top. there is no way in at all, and that single fact changes the entire job WHAT READING A BINARY ACTUALLY MEANS → no source, no comments, no function names. you recover what code does from what it is, which is the opposite direction from how it was written → the exact build gets pinned before anything else, because one patch moves every address underneath you, and a different version is a different game → nothing counts until it is confirmed inside the running process. launch it, play it, look at it. opus 5.5 does the reading, the pinning and the checking, and that loop is the part that used to be a team and a year WHAT THIS ACTUALLY CHANGES → reverse engineering was the most expensive skill in modding. one person, one binary, months of evenings, and the knowledge died with the project → now it is a loop that runs while you sleep, and what comes out of it is not a guess, it is something that got checked against the game while the game was running → so the hard question stopped being whether you can get into an engine. it is what you do once you are standing in three of them at once one layer was read out of a binary one came through the front door one was never touched at all, and it still had to land right

Roxx

195,977 görüntüleme • 20 gün önce