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JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that...

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starmex

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54,448 görüntüleme • 2 ay önce •via X (Twitter)

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MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
0:16

MARCUS CHEN STACKED 30 MAC MINIS INTO AN AI SERVER FARM. ONE $599 MAC MINI REPLACES YOUR $200/MONTH CLAUDE CODE BILL WITH $3 IN ELECTRICITY two months ago a developer posted his claude code bill on reddit. $170 in 10 days. someone replied "i bought a mac mini m4. haven't paid anthropic since." apple stores ran out of mac minis the same week the m4 chip has 120 gb/s memory bandwidth and unified memory architecture. cpu and gpu share one pool so the model loads once and both read from it. a $599 mac mini runs ai faster than a $1,500 windows pc with a discrete gpu since january 2026 ollama supports the anthropic messages api format. claude code connects directly to your local mac mini with one environment variable. same interface, zero api costs, $0 per request a heavy developer pays $459 a month across claude code max, chatgpt pro, gemini, cursor and copilot. that's $5,508 a year. the mac mini pays off in 3 months and runs on $3 in electricity after that uber rolled out claude code to 5,000 engineers and burned through their $3.4 billion 2026 ai budget in 4 months. the people who own the hardware in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

357,559 görüntüleme • 2 ay önce

THIS SHELF OF MAC MINIS REPLACES $4,080 A YEAR IN AI SUBSCRIPTIONS 00:02 the camera pans across a shelf of stacked Mac minis and the trick is obvious: that silent little farm runs the models you rent every month most people pay 7 companies for AI and use 3 of the tools. they forget the rest on the credit card and call it a stack the Mac mini M4 ends that. one shared memory pool means a $599 box runs 7B and 8B models faster than Windows machines that cost twice as much ollama pull, one command. open webui in one docker line. point Claude Code at localhost and it just works it draws 10 to 30 watts, sits silent next to a router, and runs 24/7 for $3 a month in power it pays back a $20 ChatGPT Plus sub in 3 months, then saves you $4,000 a year while the frontier still rents you compute every month you wait is another $340 gone for compute that fits on a shelf
0:10

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

Fokki

12,933 görüntüleme • 1 ay önce

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
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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 görüntüleme • 2 ay önce

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
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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 görüntüleme • 2 ay önce

AN AWS ENGINEER QUIETLY BUILT A 2 PETABYTE HOME SERVER FOR $9/MONTH THAT KILLS A $3,400/MONTH CLOUD STORAGE BILL the lenovo thinkstation pgx ships nvidia's gb10 grace blackwell superchip and 128gb of unified memory in a box the size of a mac mini at 1.2kg it runs an 80b qwen3 coder model at 25 to 40 tokens per second and a 196b step-3.5-flash moe model at 20 tokens per second locally the gb10 packs 6,144 cuda cores, 192 fifth-generation tensor cores and rates at 1 petaflop of fp4 with sparsity from a single 240 watt usb-c power supply fine tuning qwen 2.5 7b with lora took 18 minutes and 41gb of unified memory while the gpu pulled 65 watts and peaked at 77 degrees the box pulls a docker container from nvidia's registry and serves a frontier model on your local network with tool calling and zero data leaving your desk bookmark this and read the article below
0:18

AN AWS ENGINEER QUIETLY BUILT A 2 PETABYTE HOME SERVER FOR $9/MONTH THAT KILLS A $3,400/MONTH CLOUD STORAGE BILL the lenovo thinkstation pgx ships nvidia's gb10 grace blackwell superchip and 128gb of unified memory in a box the size of a mac mini at 1.2kg it runs an 80b qwen3 coder model at 25 to 40 tokens per second and a 196b step-3.5-flash moe model at 20 tokens per second locally the gb10 packs 6,144 cuda cores, 192 fifth-generation tensor cores and rates at 1 petaflop of fp4 with sparsity from a single 240 watt usb-c power supply fine tuning qwen 2.5 7b with lora took 18 minutes and 41gb of unified memory while the gpu pulled 65 watts and peaked at 77 degrees the box pulls a docker container from nvidia's registry and serves a frontier model on your local network with tool calling and zero data leaving your desk bookmark this and read the article below

starmex

192,758 görüntüleme • 2 ay önce

> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below
0:15

> 8 GPUs in one server rig > dude went homeless to build it > electrical bill costs more than rent now > while everyone else pays $400/month to openai > a 2 GPU desktop kills the api bill forever > rtx 4080 super + rtx 5060 ti = 32gb vram > runs qwen 3.6 with 100k context locally > no rate limits, no api keys, no data leaving the room > agents loop 400 times for free > claude opus still wins on hard reasoning > but local handles 90% of daily work > $1,200 setup pays itself off in 4 months > bookmark this and read the article below

starmex

167,058 görüntüleme • 3 ay önce

$300/month for AI visuals. replaced by a laptop and $2 electricity bill TouchDesigner + Ollama. local AI model. runs offline. nothing sent to any server. no API key that expires mid-performance > Ollama: 3 commands to install. one line change in existing code > Llama 3.2: real-time parameter calls. fast enough you don’t notice latency > TouchDesigner: hand tracking. audio-reactive. particle systems. generative graphics month one savings: $148-338. every month after: same your laptop. $0/month. a visual studio that runs forever
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$300/month for AI visuals. replaced by a laptop and $2 electricity bill TouchDesigner + Ollama. local AI model. runs offline. nothing sent to any server. no API key that expires mid-performance > Ollama: 3 commands to install. one line change in existing code > Llama 3.2: real-time parameter calls. fast enough you don’t notice latency > TouchDesigner: hand tracking. audio-reactive. particle systems. generative graphics month one savings: $148-338. every month after: same your laptop. $0/month. a visual studio that runs forever

NO1ennn

30,177 görüntüleme • 2 ay önce

PewDiePie just hit 20K GitHub stars in under 24 hours. The project? Odysseus. A self-hosted AI workspace that runs 100% on your machine. • Agents with tools • MCP built in • Persistent memory • File handling • Windows, macOS, Linux Your data never leaves your device. It supports Ollama, llama.cpp, and vLLM locally with OpenAI and OpenRouter support if you want cloud models too. The crazy part? A YouTuber with 110M+ subscribers just out-shipped most AI startups. And he built half of it using AI.
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PewDiePie just hit 20K GitHub stars in under 24 hours. The project? Odysseus. A self-hosted AI workspace that runs 100% on your machine. • Agents with tools • MCP built in • Persistent memory • File handling • Windows, macOS, Linux Your data never leaves your device. It supports Ollama, llama.cpp, and vLLM locally with OpenAI and OpenRouter support if you want cloud models too. The crazy part? A YouTuber with 110M+ subscribers just out-shipped most AI startups. And he built half of it using AI.

Charlie Hills

16,546 görüntüleme • 2 ay önce

THIS FREE CHINESE AI MODEL RUNS LOCALLY FOREVER AND BUILDS ANYTHING YOU CAN IMAGINE - INCLUDING FULL OPEN WORLD GAMES GLM 5.2 - open, local, free forever - no bills, no limits, no subscription want an open world game with multiplayer - describe it, it builds - want business automation - builds that too - literally anything running 24/7 same as Claude and GPT at $200/month - on your own machine, forever free - saves $2,400/year in subscriptions responds in 2-3 seconds - your computer, your speed, nobody in the queue ahead of you whoever finds out about this today saves thousands and ships a real product before everyone still waiting for the perfect tool
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THIS FREE CHINESE AI MODEL RUNS LOCALLY FOREVER AND BUILDS ANYTHING YOU CAN IMAGINE - INCLUDING FULL OPEN WORLD GAMES GLM 5.2 - open, local, free forever - no bills, no limits, no subscription want an open world game with multiplayer - describe it, it builds - want business automation - builds that too - literally anything running 24/7 same as Claude and GPT at $200/month - on your own machine, forever free - saves $2,400/year in subscriptions responds in 2-3 seconds - your computer, your speed, nobody in the queue ahead of you whoever finds out about this today saves thousands and ships a real product before everyone still waiting for the perfect tool

Noisy

23,517 görüntüleme • 2 ay önce

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.
0:18

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,402 görüntüleme • 2 ay önce

Holy sh*t, this is f**king insane😳 i cancelled my higgsfield subscription for this a free repo with 7.9K stars dropped a full AI video studio that runs on your pc it runs on 6gb of vram, even old gpus wan 2.2, ltx-2, hunyuan video and flux built in no uploads, no subscriptions, no watermarks here is how you set it up: 1. git clone the repo 2. run the one-click install script 3. launch it and generate in your browser you will not find a FREE way to make AI Videos this year than this
0:15

Holy sh*t, this is f**king insane😳 i cancelled my higgsfield subscription for this a free repo with 7.9K stars dropped a full AI video studio that runs on your pc it runs on 6gb of vram, even old gpus wan 2.2, ltx-2, hunyuan video and flux built in no uploads, no subscriptions, no watermarks here is how you set it up: 1. git clone the repo 2. run the one-click install script 3. launch it and generate in your browser you will not find a FREE way to make AI Videos this year than this

painn

188,940 görüntüleme • 7 gün önce

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.
0:18

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.

Axel Bitblaze 🪓

47,734 görüntüleme • 1 ay önce

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 👇
0:19

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 görüntüleme • 2 ay önce

Claude + Obsidian + n8n + 316 TB storage built a private second brain that ships AI projects at $3,400 a month. Most people rent cloud space and pray the bills stay low. Data leaks. Models throttle. Projects slow. This stack runs everything local. → Obsidian vault grows without limits. Every note, dataset, fine-tune, client archive links in one graph. → Claude reads the full vault instantly through Projects and MCP. No token caps. No privacy risk. → n8n automates the pipelines. New data drops → auto-ingest → Claude summarizes and links. Nightly fine-tune jobs fire. Client deliverables generate on demand. → One ORICO enclosure starts at 60 TB. Add drives. 180 TB. 300 TB. Final setup hits 316 TB. HDDs for archives. SSDs for active models. Laptop-level speed in a desktop box. Plug, power, done. Month 1: Vault hits 120 GB. First local agent runs end-to-end. Month 2: Private dataset training. Sold one custom workflow for $1,200. No cloud fees. Month 3: Recurring retainers. $3,400. System trains, tests, and deploys while you review. Before: Scattered cloud tabs. Monthly bills. Slow inference. After: 316 TB under your desk. Full control. Zero latency. Projects compound. The second brain does not beg for API keys. It owns the data and prints the income. If this was useful - follow.
0:19

Claude + Obsidian + n8n + 316 TB storage built a private second brain that ships AI projects at $3,400 a month. Most people rent cloud space and pray the bills stay low. Data leaks. Models throttle. Projects slow. This stack runs everything local. → Obsidian vault grows without limits. Every note, dataset, fine-tune, client archive links in one graph. → Claude reads the full vault instantly through Projects and MCP. No token caps. No privacy risk. → n8n automates the pipelines. New data drops → auto-ingest → Claude summarizes and links. Nightly fine-tune jobs fire. Client deliverables generate on demand. → One ORICO enclosure starts at 60 TB. Add drives. 180 TB. 300 TB. Final setup hits 316 TB. HDDs for archives. SSDs for active models. Laptop-level speed in a desktop box. Plug, power, done. Month 1: Vault hits 120 GB. First local agent runs end-to-end. Month 2: Private dataset training. Sold one custom workflow for $1,200. No cloud fees. Month 3: Recurring retainers. $3,400. System trains, tests, and deploys while you review. Before: Scattered cloud tabs. Monthly bills. Slow inference. After: 316 TB under your desk. Full control. Zero latency. Projects compound. The second brain does not beg for API keys. It owns the data and prints the income. If this was useful - follow.

HodlReaper

576,877 görüntüleme • 1 ay önce

The world just paid $2 trillion for a rocket company that lost $4.9 billion last year. And the rockets are not why it lost the money. They are the only part making any. SpaceX went public Friday, the largest IPO in history. Up 19%, a $2 trillion valuation, Elon Musk the first trillionaire. Then you open the filing. Three businesses sit inside it. Starlink, the satellites, brought in $11.4 billion, 61% of all revenue, and $4.4 billion in profit. It is the only piece that earns a dollar. The rockets that land themselves run a small loss reinvesting in Starship. And the AI arm, Grok plus the app once called Twitter, folded in this February, lost $6.4 billion in a single year on $12.7 billion of spending. Read that again. The satellites pay for everything. The AI loses more than the satellites make. And the AI is the part the market fell in love with. It gets bolder. The prospectus claims a total market of $28.5 trillion, the largest any company has ever put in a filing. Larger than the GDP of the United States. That is the number underwriting a $2 trillion price tag built on a division bleeding $6 billion a year. Now the structure. About 4% of the company trades. That sliver sets the price for all of it. Musk is locked up for 366 days and holds roughly 80% of the votes. The public bought a company they cannot steer, priced on the one segment losing the most. This is the whole year in one ticker. The profit is satellites. The story is AI. The market bought the story. The rockets were never the risk. The risk is a $2 trillion price resting on the one bet that has yet to make a cent.
0:10

The world just paid $2 trillion for a rocket company that lost $4.9 billion last year. And the rockets are not why it lost the money. They are the only part making any. SpaceX went public Friday, the largest IPO in history. Up 19%, a $2 trillion valuation, Elon Musk the first trillionaire. Then you open the filing. Three businesses sit inside it. Starlink, the satellites, brought in $11.4 billion, 61% of all revenue, and $4.4 billion in profit. It is the only piece that earns a dollar. The rockets that land themselves run a small loss reinvesting in Starship. And the AI arm, Grok plus the app once called Twitter, folded in this February, lost $6.4 billion in a single year on $12.7 billion of spending. Read that again. The satellites pay for everything. The AI loses more than the satellites make. And the AI is the part the market fell in love with. It gets bolder. The prospectus claims a total market of $28.5 trillion, the largest any company has ever put in a filing. Larger than the GDP of the United States. That is the number underwriting a $2 trillion price tag built on a division bleeding $6 billion a year. Now the structure. About 4% of the company trades. That sliver sets the price for all of it. Musk is locked up for 366 days and holds roughly 80% of the votes. The public bought a company they cannot steer, priced on the one segment losing the most. This is the whole year in one ticker. The profit is satellites. The story is AI. The market bought the story. The rockets were never the risk. The risk is a $2 trillion price resting on the one bet that has yet to make a cent.

Shanaka Anslem Perera ⚡

721,880 görüntüleme • 2 ay önce

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.
0:13

China open-sourced a peanut-sized OCR that parses entire 100-page PDFs in one shot.. It's called Unlimited-OCR. Only 3B params. Runs locally. Every other OCR tool chops your doc into pages and loses the thread. this one reads the whole thing in a single pass. → One-shot "long-horizon" parsing (32K context window) → Multilingual, out of the box → 93% on the standard parsing benchmark (+6 over baseline) → <0.11 error rate past 40 pages → Runs 100% locally on your own hardware → Works with Transformers, vLLM, SGLang, Docker, Ollama, llama.cpp Traditional cloud OCR (Textract, Google Vision, Azure Doc Intelligence) costs $1.50–$15 per 1,000 pages. This runs on your machine. For free. Forever. Baidu built it explicitly to push DeepSeek-OCR one step further. Already at 1.9M downloads on Hugging Face and most people have no idea it exists yet. 100% open source.

Superman

1,098,048 görüntüleme • 1 ay önce

NVIDIA just made paying for AI feel optional. Open model, a million tokens of context, free tier with no per-token cost, runs on your own hardware. Entire codebases, whole data rooms, a year of chat logs, all swallowed in one prompt. No chunking, no RAG, no rate limit theater. The closed-AI premium has 90 days to defend itself. Bookmark this and come back. Open beat closed. Again.
0:17

NVIDIA just made paying for AI feel optional. Open model, a million tokens of context, free tier with no per-token cost, runs on your own hardware. Entire codebases, whole data rooms, a year of chat logs, all swallowed in one prompt. No chunking, no RAG, no rate limit theater. The closed-AI premium has 90 days to defend itself. Bookmark this and come back. Open beat closed. Again.

shmidt

295,692 görüntüleme • 2 ay önce

I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)
0:16

I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)

Chris

34,644 görüntüleme • 4 ay önce

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
0:20

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 görüntüleme • 1 ay önce

The next iPhone will cost more, and the reason has almost nothing to do with Apple. The chip that stores your photos cost Apple about 13 dollars last year. This year it runs around 51. Multiply that across every phone, laptop, and console on earth, and you are looking at the first consumer bill for the AI boom, arriving in the pocket of someone who never asked for it. Tim Cook, who has run Apple's supply chain for forty years, called it a hundred-year flood, something he has never seen. Memory prices have quadrupled in places. The cause is brutally simple. AI data centers are now expected to swallow roughly 70 percent of the world's memory production this year. Seven chips in ten go to server farms. Phones, cars, and laptops fight over the three that are left. This is one force wearing two faces. The same AI demand making the device in your hand more expensive is minting record fortunes for the handful of companies that feed it. Memory makers in Seoul just hit all-time highs in the same week Apple warned you to brace for higher prices. The shortage and the windfall are the identical event, seen from opposite ends. Then comes the part almost no one traces all the way down. Beneath the chips sit rare earth minerals, and one country controls them. China processes around 90 percent of the world's rare earths and makes roughly 94 percent of the high-performance magnets that spin inside every fab and cooling system. The polishing compound that finishes a wafer, the magnets in the machines that build it, run through Beijing. And through 2025, China has been turning that grip into leverage, licensing what leaves. So the chain is complete. AI wants memory, memory needs minerals, and the minerals answer to one government. The price of your phone is now a foreign policy.
0:10

The next iPhone will cost more, and the reason has almost nothing to do with Apple. The chip that stores your photos cost Apple about 13 dollars last year. This year it runs around 51. Multiply that across every phone, laptop, and console on earth, and you are looking at the first consumer bill for the AI boom, arriving in the pocket of someone who never asked for it. Tim Cook, who has run Apple's supply chain for forty years, called it a hundred-year flood, something he has never seen. Memory prices have quadrupled in places. The cause is brutally simple. AI data centers are now expected to swallow roughly 70 percent of the world's memory production this year. Seven chips in ten go to server farms. Phones, cars, and laptops fight over the three that are left. This is one force wearing two faces. The same AI demand making the device in your hand more expensive is minting record fortunes for the handful of companies that feed it. Memory makers in Seoul just hit all-time highs in the same week Apple warned you to brace for higher prices. The shortage and the windfall are the identical event, seen from opposite ends. Then comes the part almost no one traces all the way down. Beneath the chips sit rare earth minerals, and one country controls them. China processes around 90 percent of the world's rare earths and makes roughly 94 percent of the high-performance magnets that spin inside every fab and cooling system. The polishing compound that finishes a wafer, the magnets in the machines that build it, run through Beijing. And through 2025, China has been turning that grip into leverage, licensing what leaves. So the chain is complete. AI wants memory, memory needs minerals, and the minerals answer to one government. The price of your phone is now a foreign policy.

Shanaka Anslem Perera ⚡

58,900 görüntüleme • 1 ay önce