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๐—•๐—ผ๐˜‚๐—ด๐—ต๐˜ ๐—ฎ ๐˜‚๐˜€๐—ฒ๐—ฑ ๐— ๐—ฎ๐—ฐ ๐—ฃ๐—ฟ๐—ผ ๐—ณ๐—ผ๐—ฟ $๐Ÿด๐Ÿฌ๐Ÿฌ ๐—ผ๐—ณ๐—ณ ๐—–๐—ฟ๐—ฎ๐—ถ๐—ด๐˜€๐—น๐—ถ๐˜€๐˜ ๐—ฎ๐—ป๐—ฑ ๐˜€๐—ฝ๐—ฒ๐—ป๐˜ ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜…๐˜ ๐Ÿฏ ๐˜„๐—ฒ๐—ฒ๐—ธ๐˜€ ๐˜๐˜‚๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ถ๐˜ ๐—ถ๐—ป๐˜๐—ผ ๐˜€๐—ผ๐—บ๐—ฒ๐˜๐—ต๐—ถ๐—ป๐—ด ๐—ถ๐—ป๐˜€๐—ฎ๐—ป๐—ฒ Here's what he added: > 192GB RAM upgrade - $340. > Afterburner card for ProRes - $200 used. > 8TB NVMe storage - $280. > eGPU enclosure + RX...

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712,305 gรถrรผntรผleme โ€ข 2 ay รถnce โ€ขvia X (Twitter)

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People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.
36:48

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

Alex Lieberman

57,764 gรถrรผntรผleme โ€ข 2 ay รถnce

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. โ†“ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. โ†“ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. โ†“ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. โ†“ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. โ†“ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. โ†“ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. โ†“ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. โ†“ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. โ†“ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." โ†“ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.
25:22

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. โ†“ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. โ†“ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. โ†“ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. โ†“ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. โ†“ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. โ†“ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. โ†“ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. โ†“ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. โ†“ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." โ†“ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,677 gรถrรผntรผleme โ€ข 5 ay รถnce

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.
0:21

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

932,332 gรถrรผntรผleme โ€ข 1 ay รถnce

AMD CEO Lisa Su just killed Nvidiaโ€™s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMDโ€™s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.
0:50

AMD CEO Lisa Su just killed Nvidiaโ€™s $4,000 AI box with a $1,499 lunchbox. She walked on stage, held it in one hand, and ran a 235 billion parameter model live. No data center. No cloud. No rented GPU. The chip inside is something nobody saw coming. AMDโ€™s Ryzen AI Max+ 395 is the first x86 silicon where CPU and GPU share the same 128GB of memory. That single trick lets a desktop run models that used to need a server rack. Out of those 128GB, Linux hands the GPU 110GB to play with. For context, an RTX 5090 gives you 32GB. A 4090 gives you 24. This box gives you more than three times either of them, in a chassis the size of a thick paperback. The benchmark that broke the room: this chip beat an Nvidia RTX 5080 by more than 3x on DeepSeek R1 inference. A $1,499 lunchbox outrunning a $1,000 discrete graphics card on a real AI workload. Nvidia spent a decade convincing the world you needed their hardware for serious AI. AMD just put that on a desk for half the price. Here is what nobody is telling you. A heavy AI user right now pays $200 for Claude Code Max, $200 for ChatGPT Pro, $20 for Cursor, $20 for Gemini. That is $5,280 a year leaving your account. The box pays itself off in 9 months and then runs free for the rest of its life. Install Ollama. Pull Qwen3 235B. Point Claude Code at localhost. Same interface you already use, except now nothing leaves your machine, nothing costs per request, and no company throttles your usage at 3am when you finally have time to build. This is the moment every AI subscription becomes optional. Lawyers stop fearing OpenAI leaks. Developers stop watching the token meter. Founders stop renting H100s for prototypes that never ship because the bill scared them. The first thousand people to figure this out will own the next two years of private AI consulting. Save this, and read the full breakdown article below you are watching the next shift hit before everyone else does.

AdiiX

3,396,692 gรถrรผntรผleme โ€ข 3 ay รถnce

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.
1:06

China just made Silicon Valley's entire AI industry look like a scam. The US government spent 3 years trying to stop China from building competitive AI. But this backfired HORRIBLY. Here's what happened: Yesterday, a Chinese startup called DeepSeek released a new AI model called V4. It matches the performance of OpenAI and Anthropic's best models. At 1/7th the price. And for the first time ever, it was built on Chinese chips. NOT American ones. That last part is the one that terrifies the west. For context: Since 2022, the US has banned the export of advanced AI chips to China. The entire strategy was built on the assumption that if China can't access Nvidia's best hardware, they can't build frontier AI. But DeepSeek just proved that assumption wrong. Their V4 model was trained and runs on Huawei's Ascend chips. Huawei spent months working directly with DeepSeek to make sure V4 runs across their entire line of AI processors. Jensen Huang even predicted this on a recent podcast: "The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation." That day was yesterday. And the numbers are crazy: DeepSeek V4 costs $3.48 per million output tokens. OpenAI's latest model GPT-5.5 costs $30. Anthropic's Claude charges $25. Same ballpark performance. 7x cheaper. Uber's CTO just admitted they burned through their ENTIRE 2026 AI budget in 4 months using Anthropic's tools. If Uber had used DeepSeek instead, that same budget would have lasted 7 YEARS. 4 months vs 7 years. Same work getting done. But the pricing isn't even the big thing here. The real story is what DeepSeek did with their technical report: They published the benchmarks where they LOSE. Every AI company cherry-picks the tests where their model wins. DeepSeek ran the full comparison against GPT-5.4 and Google's Gemini, found they trail frontier models by 3 to 6 months, and printed it anyway. They literally don't care because the price gap makes the performance gap irrelevant for 90% of use cases. So the US export controls didn't slow China down. They ACCELERATED China's independence. Because Chinese developers were FORCED to train models with limited resources, they had to figure out how to make AI radically more efficient. That constraint became their competitive advantage. Every generation of DeepSeek has gotten dramatically cheaper to train. V4 continues the trend. Meanwhile US companies are going the OPPOSITE direction: OpenAI's GPT-5.5 Pro costs $180 per million output tokens. That's 51x more expensive than DeepSeek V4 for comparable work. The Commerce Secretary confirmed this week that ZERO Nvidia advanced chip shipments have actually gone through to China despite being approved in January. So China built frontier AI anyway. Without American chips. At a fraction of the cost. And the market response tells you everything: Chinese chipmaker SMIC surged 10%. Huahong Semiconductor jumped 15%. DeepSeek's Chinese AI competitors Zhipu AI and MiniMax dropped 9% because V4 is destroying them too. DeepSeek is making Silicon Valley's pricing model look like a scam. US tech companies spent $650 billion on AI infrastructure this year. DeepSeek just showed the world you can match their output for pennies. The export controls were supposed to be America's ace card. Instead they taught China how to win without American chips, at American prices nobody can compete with. Jensen Huang was right. This is a horrible outcome. But it's the outcome America built for itself.

Ricardo

281,190 gรถrรผntรผleme โ€ข 4 ay รถnce

Dear ICP community, the Internet Computer has now been running strong for 5 years ๐Ÿ‘๐Ÿ‘๐Ÿ‘ Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: โ€” Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. โ€” The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. โ€” Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation โ€” where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) โ€” Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. โ€” New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. โ€” Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). โ€” An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. โ€” Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... โ€” You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... โ€” Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. โ€” Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. โ€” Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). โ€” For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. โ€” Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday ๐Ÿ’ช I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years ๐Ÿ‘๐Ÿ‘๐Ÿ‘ Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: โ€” Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. โ€” The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. โ€” Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation โ€” where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) โ€” Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. โ€” New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. โ€” Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). โ€” An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. โ€” Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... โ€” You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... โ€” Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. โ€” Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. โ€” Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). โ€” For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. โ€” Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday ๐Ÿ’ช I'll be back with more news soon!!

dom | icp

315,227 gรถrรผntรผleme โ€ข 4 ay รถnce

dude runs a private AI host out of his basement on 8 stacked 3090s an accounting firm signed the moment they realized their client financials would physically never touch a third party the rig pays his mortgage now the setup is eight RTX 3090s lined up on an open frame down in his basement - fans roaring, yellow zip ties holding cables, a little monitor blinking stats beside a cheap keyboard. it was built as a mining rig and the dashboard still looks the part the accounting firm didnโ€™t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clientsโ€™ financial records would never leave a machine the firm could physically point to that promise closed the deal hereโ€™s the corner they were backed into: accounting firms hold the most sensitive data imaginable - tax filings, payroll, full financial histories. they wanted AI to speed up the tedious work, but routing that data through a cloud model was a liability their partners would never sign. so they sat frozen while the tech moved on without them he handed them the one version that cleared legal: a private model running on hardware in a basement, not a server farm theyโ€™d never see the build behind the rig: eight 3090s stack up to 192GB of combined vram - enough to run a 70B model with real context through vLLM. the firmโ€™s documents get indexed into an isolated vector database, so every answer pulls only from their own files. queries hit a local endpoint, replies come back, nothing ever leaves the basement. used 3090s were the quiet genius move - around $700 a card instead of triple that for new silicon the rig that once mined coins for pennies now serves inference that bills like a service the contract that covers his house: โ†’ managing partner came down for an in-person demo โ†’ asked the only question that mattered: where does the data live โ†’ he pointed at the rig and said โ€œright here, nowhere elseโ€ โ†’ they signed a monthly deal before leaving the basement the economics underneath: โ†’ 8 used 3090s: ~$5,600, paid off in the first two months โ†’ electricity: a few hundred a month under load โ†’ what the firm pays: enough to clear his mortgage every month โ†’ margin: almost all of it, because the data center is under his own house real firms burn a fortune on compliant cloud infrastructure he does the same job in a basement, and the data is safer for the dumbest reason possible - it has nowhere else it can go the rig that looks one loose cable from death is quietly the most trusted machine his client has ever touched
0:42

dude runs a private AI host out of his basement on 8 stacked 3090s an accounting firm signed the moment they realized their client financials would physically never touch a third party the rig pays his mortgage now the setup is eight RTX 3090s lined up on an open frame down in his basement - fans roaring, yellow zip ties holding cables, a little monitor blinking stats beside a cheap keyboard. it was built as a mining rig and the dashboard still looks the part the accounting firm didnโ€™t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clientsโ€™ financial records would never leave a machine the firm could physically point to that promise closed the deal hereโ€™s the corner they were backed into: accounting firms hold the most sensitive data imaginable - tax filings, payroll, full financial histories. they wanted AI to speed up the tedious work, but routing that data through a cloud model was a liability their partners would never sign. so they sat frozen while the tech moved on without them he handed them the one version that cleared legal: a private model running on hardware in a basement, not a server farm theyโ€™d never see the build behind the rig: eight 3090s stack up to 192GB of combined vram - enough to run a 70B model with real context through vLLM. the firmโ€™s documents get indexed into an isolated vector database, so every answer pulls only from their own files. queries hit a local endpoint, replies come back, nothing ever leaves the basement. used 3090s were the quiet genius move - around $700 a card instead of triple that for new silicon the rig that once mined coins for pennies now serves inference that bills like a service the contract that covers his house: โ†’ managing partner came down for an in-person demo โ†’ asked the only question that mattered: where does the data live โ†’ he pointed at the rig and said โ€œright here, nowhere elseโ€ โ†’ they signed a monthly deal before leaving the basement the economics underneath: โ†’ 8 used 3090s: ~$5,600, paid off in the first two months โ†’ electricity: a few hundred a month under load โ†’ what the firm pays: enough to clear his mortgage every month โ†’ margin: almost all of it, because the data center is under his own house real firms burn a fortune on compliant cloud infrastructure he does the same job in a basement, and the data is safer for the dumbest reason possible - it has nowhere else it can go the rig that looks one loose cable from death is quietly the most trusted machine his client has ever touched

regent0x

13,334 gรถrรผntรผleme โ€ข 2 ay รถnce

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
16:04

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 gรถrรผntรผleme โ€ข 7 ay รถnce

The most downloaded AI on earth is now Chinese. Alibaba just gave away a model that matches Claude's flagship, and it literally runs on a $700 used graphics card. The Qwen models crossed 3 BILLION downloads in six months. Hugging Face counted 418 million downloads for Google this year, and 227 million for Meta. Alibaba cleared more than four times both of them combined. Then today it released Qwen3.8-27B under an Apache 2.0 license. The model has 27 billion parameters, native vision, and a 262,000 token context window. Developers are running it locally on 17 gigabytes of memory, on used cards that cost a few hundred dollars. Alibaba's own benchmark table claims it beats Opus 4.6 Max on computer use by 84.3 to 72.7, on mobile use by 81.9 to 62, and on visual math by 94.6 to 65.5. Those numbers come from the vendor and nobody has independently verified them yet, so treat them as a claim. But the generation over generation jumps are harder to wave away: On DeepSWE the score went from 13.3 to 42.2. On software engineering it went from 49.3 to 79.0. That happened in ONE release cycle. And Apache 2.0 means anyone can download the weights, modify them, build products on them, sell those products, and never pay or ask permission. It cannot be revoked. Once the file is on your drive it is yours permanently. 3 billion downloads means those files already sit on machines in every country on Earth. Alibaba could delete everything tomorrow and it would change nothing. Washington spent 4 years building an export control regime around chips, model weights, and entity lists. Every piece of it assumes a chokepoint exists somewhere. A fab, a shipment, a company that can be told no. But there is no chokepoint for a file that has already been copied three billion times. And the copying compounds. Hugging Face counted 151,448 models built on top of Qwen, which is 2.6x Meta's entire footprint and 4.7x the number of Llama repositories. New ones appear at roughly 200 a day. The report says Qwen has become "part of the default workflow for developers deciding what models to fine-tune and deploy." Alibaba is also pushing Qwen through its cloud into Southeast Asia and Africa, markets where American labs have almost no presence, and where a very large share of the next generation of developers will learn to build. Meta and Nvidia have both rushed out new open models in recent weeks. That is what a response looks like when you feel the floor move. And to be clear, these are download and derivative numbers, not usage numbers. ChatGPT and Claude cannot be downloaded at all, so they do not appear in this comparison. What the figures measure is what developers choose to build on top of, which is a different question from what consumers type into a box. That is also why it matters MORE. Consumer habits change in an afternoon. Infrastructure choices last a decade, because everything built on top has to be rewritten to undo them. The American labs are valued on an assumption that frontier intelligence stays scarce, expensive, and rented by the token. Alibaba just made a version of it free, permanent, and small enough to run on hardware people already own. You will not get an announcement when the software you use every day starts running on a Chinese model underneath. Go and count how many of the tools you rely on could be rebuilt on free weights this year.
0:39

The most downloaded AI on earth is now Chinese. Alibaba just gave away a model that matches Claude's flagship, and it literally runs on a $700 used graphics card. The Qwen models crossed 3 BILLION downloads in six months. Hugging Face counted 418 million downloads for Google this year, and 227 million for Meta. Alibaba cleared more than four times both of them combined. Then today it released Qwen3.8-27B under an Apache 2.0 license. The model has 27 billion parameters, native vision, and a 262,000 token context window. Developers are running it locally on 17 gigabytes of memory, on used cards that cost a few hundred dollars. Alibaba's own benchmark table claims it beats Opus 4.6 Max on computer use by 84.3 to 72.7, on mobile use by 81.9 to 62, and on visual math by 94.6 to 65.5. Those numbers come from the vendor and nobody has independently verified them yet, so treat them as a claim. But the generation over generation jumps are harder to wave away: On DeepSWE the score went from 13.3 to 42.2. On software engineering it went from 49.3 to 79.0. That happened in ONE release cycle. And Apache 2.0 means anyone can download the weights, modify them, build products on them, sell those products, and never pay or ask permission. It cannot be revoked. Once the file is on your drive it is yours permanently. 3 billion downloads means those files already sit on machines in every country on Earth. Alibaba could delete everything tomorrow and it would change nothing. Washington spent 4 years building an export control regime around chips, model weights, and entity lists. Every piece of it assumes a chokepoint exists somewhere. A fab, a shipment, a company that can be told no. But there is no chokepoint for a file that has already been copied three billion times. And the copying compounds. Hugging Face counted 151,448 models built on top of Qwen, which is 2.6x Meta's entire footprint and 4.7x the number of Llama repositories. New ones appear at roughly 200 a day. The report says Qwen has become "part of the default workflow for developers deciding what models to fine-tune and deploy." Alibaba is also pushing Qwen through its cloud into Southeast Asia and Africa, markets where American labs have almost no presence, and where a very large share of the next generation of developers will learn to build. Meta and Nvidia have both rushed out new open models in recent weeks. That is what a response looks like when you feel the floor move. And to be clear, these are download and derivative numbers, not usage numbers. ChatGPT and Claude cannot be downloaded at all, so they do not appear in this comparison. What the figures measure is what developers choose to build on top of, which is a different question from what consumers type into a box. That is also why it matters MORE. Consumer habits change in an afternoon. Infrastructure choices last a decade, because everything built on top has to be rewritten to undo them. The American labs are valued on an assumption that frontier intelligence stays scarce, expensive, and rented by the token. Alibaba just made a version of it free, permanent, and small enough to run on hardware people already own. You will not get an announcement when the software you use every day starts running on a Chinese model underneath. Go and count how many of the tools you rely on could be rebuilt on free weights this year.

Ricardo

81,295 gรถrรผntรผleme โ€ข 1 ay รถnce

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.
1:10

Google just launched a direct attack on Nvidia's most valuable asset. Not their chips. Their SOFTWARE. And if this works, Nvidia's $4 trillion empire collapses. Here's what just leaked: Google is building "TorchTPU" - a secret project that makes PyTorch seamlessly run on Google's TPU chips instead of Nvidia GPUs. Why does this matter? PyTorch is the MOST USED AI framework on Earth. Every AI developer uses it. And PyTorch was built around Nvidia's CUDA software. Wall Street analysts call CUDA "Nvidia's strongest defensive wall." It's the reason companies can't easily switch away from Nvidia even when alternatives exist. You don't just buy Nvidia chips. You buy into their entire ecosystem. Switching costs MILLIONS in engineering work. Months of rewrites. Performance drops. So companies stay locked in. Even when Nvidia raises prices. Even when supply runs short. That's not a hardware moat. That's a SOFTWARE prison. And Google just found the escape route. Here's the problem Nvidia created for itself: Google's TPU chips are actually GOOD. Competitive performance. Better availability. Lower cost. But developers won't use them because Google's chips run JAX (Google's internal framework), not PyTorch. That means if you want to use Google TPUs, you have to rewrite your entire codebase. Nobody wants to do that. So Google TPUs sit unused while developers fight over Nvidia chips. Until now. TorchTPU makes PyTorch run natively on Google hardware. No rewrites. No performance loss. No months of engineering. You just... switch. And Google is partnering with META (who built PyTorch) to make it happen. They're even considering OPEN-SOURCING parts of it to speed adoption. Translation: Google is willing to give this away for free just to break Nvidia's lock. The implications are insane: Every company currently paying Nvidia's premium prices suddenly has a way out. Oracle, Microsoft, OpenAI - all locked into Nvidia's ecosystem - can switch to Google. Nvidia's pricing power evaporates overnight. And the timing is perfect: Nvidia is already facing heat. Semiconductor index dropped 3% today. Oracle just lost their biggest investor over AI spending concerns. Companies are realizing AI infrastructure costs are unsustainable. Now Google hands them an alternative. Same performance. Lower cost. Better availability. Jensen Huang knows exactly what this means. CUDA has been Nvidia's untouchable advantage for YEARS. It's why Nvidia trades at 50x earnings while AMD trades at 25x. The software moat justified the premium. But if Google removes that switching cost? Nvidia becomes just another chip company. And chip companies compete on price, not ecosystem lock-in. Here's what happens next: Google needs 12-18 months to make TorchTPU production-ready. If it works, cloud providers will adopt it instantly. They WANT an alternative to Nvidia's monopoly pricing. Amazon already building their own Trainium chips. Microsoft making Maia. They're all trying to escape Nvidia. Google just gave them the software bridge. Nvidia's response options are limited: They can't buy Google. Can't kill PyTorch (Meta owns it). Can't stop open source. Their only play is to keep improving CUDA faster than Google can catch up. But that's a race, not a moat. The market isn't pricing this in yet. Nvidia down 2% today. Google down 2%. Investors think this is just "another competitor." They don't understand this is an attack on the FOUNDATION of Nvidia's valuation. Hardware is replaceable. Software lock-in is what made Nvidia worth $4 trillion. Google is attacking the lock-in. Watch what happens in 2026 when TorchTPU goes live and companies realize they can actually leave Nvidia. The "Nvidia is unstoppable" narrative dies. And a $4 trillion valuation built on software moats gets repriced.

Ricardo

1,617,371 gรถrรผntรผleme โ€ข 9 ay รถnce

A study proved that $40 million was extracted from Polymarket in one year using a single mathematical formula I found a wallet that is using it right now on Iran war markets and made $1.4M in one week. Most people on Polymarket try to predict the future. Will there be a war. Who will win the election. What will happen next. I spent months doing the same thing. Reading news. Watching debates. Building my little models of what I thought should happen. And losing money. Not because I was wrong about events. Because I was wrong about the game itself. The game is not about predictions. And the wallet I'm about to show you is living proof. Three weeks ago I pulled the full trade history of this wallet: What I saw at first didn't make sense. He was opening the same market more than 30 times. US strikes Iran by January 11. US strikes Iran by January 12. January 13. January 14. January 15. January 16. January 17. The same event. Different dates. Over and over. First thought: this person is obsessed with Iran. Second thought: this person doesn't care about Iran at all. Here's what he's actually doing. Polymarket creates separate markets for the same event with different deadlines. Will the US strike Iran by March. By April. By June. These are not independent questions. If the strike happens in March then April and June automatically resolve to YES as well. But Polymarket prices each market separately. And the crowd prices them emotionally. Fear spikes on Tuesday night because someone tweeted something. One market jumps. The others lag behind. For a few minutes and sometimes hours prices on related markets stop converging. When you buy NO across multiple dates and the total cost is 94 cents and the guaranteed payout is $1 regardless of what happens you're not betting. You're collecting a 6% return on mathematical inevitability. That's the entire strategy. He buys dollars for 94 cents. I checked his numbers. On the Iran series alone he pulled $247,000 in realized profit across seven markets with different dates. Average purchase price of NO positions from 72 to 95 cents. Each one resolved at $1. The biggest hit was the government shutdown market. $88,000 in profit. Same logic. Buy both sides when the total cost is less than a dollar. One side pays. Math does the rest. 85% of his capital is in political markets. Wars. Elections. Geopolitics. Not because he has strong geopolitical convictions. Because political markets on Polymarket are where the math breaks most often. Why political markets specifically? Because they generate the most emotion. When CNN runs breaking news about Iran at 11 PM thousands of people rush to buy YES on the nearest date. They overbid the price. They panic. They push one market out of line with the rest. That panic is his paycheck. And now the part that actually matters. I dug deeper into how this type of arbitrage works at scale and found a study that made everything click. A team analyzed every trade on Polymarket over 12 months. They found 17,218 market conditions. 41% of them had an exploitable pricing error. And the total profit extracted by arbitrageurs was $40 million. The top single wallet made $2 million using one algorithm. The Frank-Wolfe method. I'll explain without math because the concept is simple even if the calculations aren't. Imagine you walk into a store that sells lottery tickets for 7 different drawings. Each ticket is priced separately. The store doesn't coordinate prices between drawings. You notice that if you buy a certain combination of tickets across all 7 drawings the total cost is $94 but you're guaranteed to win exactly $100 no matter which drawing hits. You don't need to predict which drawing will win. You just need to notice that the store mispriced the tickets. Here's Frank-Wolfe in one sentence. It scans thousands of related markets simultaneously and finds combinations where the total price is less than the guaranteed payout. Then it calculates the exact amounts to buy on each side to maximize the spread. The reason a human can't do this manually is scale. There are hundreds of active markets on Polymarket. Many are connected by logic. If event A happens then event B must also happen. If candidate X wins state Y then the national result shifts. The number of possible combinations grows exponentially. While you're checking 10 markets by hand the algorithm has scanned 17,000. What anoin123 does is a manual version of this. He picks one cluster of related markets like the Iran date series and runs the logic in his head. Buy NO across seven dates. Total cost less than a dollar. Wait. Collect. The automated version does the same thing but across all markets on the platform simultaneously. My personal takeaway after three weeks of studying this. I spent months trying to be smarter than the crowd. Reading polls. Watching news. Forming opinions. And the whole time there was a category of traders who had zero opinions about anything. They just waited for the crowd to misprice related markets and collected the difference. The uncomfortable realization is that prediction markets are not actually about predictions for those who make the most money. They're about math. And the math breaks every day because people trade on emotions and the platform prices markets independently of each other. I don't have the infrastructure to run Frank-Wolfe at scale. But I don't need to. Wallets like anoin123 do this in plain sight. Every trade on the blockchain. Every entry price. Every exit. Every timestamp. I stopped trying to predict events. I started watching wallets that make money regardless of what happens. The difference in my results is so stark it's uncomfortable to think about. If you want to understand the full math behind this the study is publicly available. Search for Arbitrage in Prediction Markets on arXiv. But the short version is this. Every time the crowd panics about a war or an election and pushes one market out of line with its related markets someone on the other side quietly buys dollars for 94 cents. The question is not whether they'll strike Iran. The question is whether you noticed that seven markets about the same event are priced as if they have nothing to do with each other. That gap is where the money lives.
0:55

A study proved that $40 million was extracted from Polymarket in one year using a single mathematical formula I found a wallet that is using it right now on Iran war markets and made $1.4M in one week. Most people on Polymarket try to predict the future. Will there be a war. Who will win the election. What will happen next. I spent months doing the same thing. Reading news. Watching debates. Building my little models of what I thought should happen. And losing money. Not because I was wrong about events. Because I was wrong about the game itself. The game is not about predictions. And the wallet I'm about to show you is living proof. Three weeks ago I pulled the full trade history of this wallet: What I saw at first didn't make sense. He was opening the same market more than 30 times. US strikes Iran by January 11. US strikes Iran by January 12. January 13. January 14. January 15. January 16. January 17. The same event. Different dates. Over and over. First thought: this person is obsessed with Iran. Second thought: this person doesn't care about Iran at all. Here's what he's actually doing. Polymarket creates separate markets for the same event with different deadlines. Will the US strike Iran by March. By April. By June. These are not independent questions. If the strike happens in March then April and June automatically resolve to YES as well. But Polymarket prices each market separately. And the crowd prices them emotionally. Fear spikes on Tuesday night because someone tweeted something. One market jumps. The others lag behind. For a few minutes and sometimes hours prices on related markets stop converging. When you buy NO across multiple dates and the total cost is 94 cents and the guaranteed payout is $1 regardless of what happens you're not betting. You're collecting a 6% return on mathematical inevitability. That's the entire strategy. He buys dollars for 94 cents. I checked his numbers. On the Iran series alone he pulled $247,000 in realized profit across seven markets with different dates. Average purchase price of NO positions from 72 to 95 cents. Each one resolved at $1. The biggest hit was the government shutdown market. $88,000 in profit. Same logic. Buy both sides when the total cost is less than a dollar. One side pays. Math does the rest. 85% of his capital is in political markets. Wars. Elections. Geopolitics. Not because he has strong geopolitical convictions. Because political markets on Polymarket are where the math breaks most often. Why political markets specifically? Because they generate the most emotion. When CNN runs breaking news about Iran at 11 PM thousands of people rush to buy YES on the nearest date. They overbid the price. They panic. They push one market out of line with the rest. That panic is his paycheck. And now the part that actually matters. I dug deeper into how this type of arbitrage works at scale and found a study that made everything click. A team analyzed every trade on Polymarket over 12 months. They found 17,218 market conditions. 41% of them had an exploitable pricing error. And the total profit extracted by arbitrageurs was $40 million. The top single wallet made $2 million using one algorithm. The Frank-Wolfe method. I'll explain without math because the concept is simple even if the calculations aren't. Imagine you walk into a store that sells lottery tickets for 7 different drawings. Each ticket is priced separately. The store doesn't coordinate prices between drawings. You notice that if you buy a certain combination of tickets across all 7 drawings the total cost is $94 but you're guaranteed to win exactly $100 no matter which drawing hits. You don't need to predict which drawing will win. You just need to notice that the store mispriced the tickets. Here's Frank-Wolfe in one sentence. It scans thousands of related markets simultaneously and finds combinations where the total price is less than the guaranteed payout. Then it calculates the exact amounts to buy on each side to maximize the spread. The reason a human can't do this manually is scale. There are hundreds of active markets on Polymarket. Many are connected by logic. If event A happens then event B must also happen. If candidate X wins state Y then the national result shifts. The number of possible combinations grows exponentially. While you're checking 10 markets by hand the algorithm has scanned 17,000. What anoin123 does is a manual version of this. He picks one cluster of related markets like the Iran date series and runs the logic in his head. Buy NO across seven dates. Total cost less than a dollar. Wait. Collect. The automated version does the same thing but across all markets on the platform simultaneously. My personal takeaway after three weeks of studying this. I spent months trying to be smarter than the crowd. Reading polls. Watching news. Forming opinions. And the whole time there was a category of traders who had zero opinions about anything. They just waited for the crowd to misprice related markets and collected the difference. The uncomfortable realization is that prediction markets are not actually about predictions for those who make the most money. They're about math. And the math breaks every day because people trade on emotions and the platform prices markets independently of each other. I don't have the infrastructure to run Frank-Wolfe at scale. But I don't need to. Wallets like anoin123 do this in plain sight. Every trade on the blockchain. Every entry price. Every exit. Every timestamp. I stopped trying to predict events. I started watching wallets that make money regardless of what happens. The difference in my results is so stark it's uncomfortable to think about. If you want to understand the full math behind this the study is publicly available. Search for Arbitrage in Prediction Markets on arXiv. But the short version is this. Every time the crowd panics about a war or an election and pushes one market out of line with its related markets someone on the other side quietly buys dollars for 94 cents. The question is not whether they'll strike Iran. The question is whether you noticed that seven markets about the same event are priced as if they have nothing to do with each other. That gap is where the money lives.

Blaze

31,373 gรถrรผntรผleme โ€ข 7 ay รถnce

AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.
1:31

AI companies just BROKE the global supply chain for every piece of technology you own. And the fallout is way worse than anyone predicted... Sony is delaying the next PlayStation to 2028 or 2029. Nintendo is hiking the Switch 2 price mid-cycle. Apple warned investors that iPhone margins are getting crushed. Cisco just posted its worst share loss in 4 years. Oppo is cutting phone shipments by 20%. Lenovo, Dell, HP, Acer, and ASUS are all raising laptop prices 15-20%. Samsung is now reviewing memory contracts QUARTERLY instead of annually because prices change too fast to plan. And Elon Musk just told investors Tesla has to build its own chip factory from scratch because no supplier on the planet can keep up. His exact words: "We've got two choices: hit the chip wall or make a fab." All of this happened in the last 3 weeks. Same cause. Every single time. AI data centers are buying every memory chip on Earth. And there's nothing left for everyone else. Here's how we got here: 3 years ago, ChatGPT launched and the AI arms race began. Since then, Samsung, SK Hynix, and Micron, the only 3 companies that make memory chips, quietly made a decision that's now reshaping the ENTIRE global economy. They stopped prioritizing consumer memory. Every factory. Every production line. Every wafer. All redirected toward one customer: AI data centers Why? Money. AI memory chips sell for 3-5X the margin of regular RAM. When Google calls offering to buy your entire output at premium pricing, you don't say no. So the 3 companies that control 90% of the world's memory supply chose their highest-paying customers and left everyone else fighting over scraps. The numbers from this week are insane: OpenAI's Stargate project ALONE will consume 40% of the entire world's DRAM output. HBM demand is surging 70% year over year in 2026. HBM now takes 23% of total DRAM wafer production, up from 19% last year. Meanwhile, there's a 4% gap between global DRAM supply and demand. And that doesn't even account for depleted inventories across multiple industries. DRAM prices have surged over 170% since early 2025. DDR5 contract prices are still jumping double digits month over month. And the memory makers? They're printing money. Micron's revenue is expected to more than DOUBLE this fiscal year. SK Hynix sales doubled in 2024 and are on pace to double AGAIN. Samsung just reported quarterly profit nearly tripling. 3 companies. $650 billion in AI spending chasing their products. And they get to name their price. But the collateral damage is everywhere: Every industry that uses memory, which is every industry, is getting squeezed. Smartphone manufacturers are getting destroyed. For a mid-range phone, memory now represents up to 30% of the total build cost. Triple what it was in early 2025. Chinese phone makers like Xiaomi, Oppo, and Transsion are cutting shipment forecasts and raising prices because they literally cannot afford the memory to build their phones. Lenovo's CFO called the cost surge "unprecedented" and admitted they stockpiled 50% more inventory than normal just to survive the next few months. The PC market could shrink by up to 9% this year according to IDC. Not because people don't want computers. But because they can't afford the memory that goes inside them. And the gaming industry? Sony is seriously considering pushing the next PlayStation to 2028 or 2029. Their carefully planned console cycle is getting blown up because they can't secure memory at prices that make a new console viable. Nintendo is looking at raising the Switch 2 price. In the middle of a launch cycle. Something console makers almost never do. Nvidia is cutting RTX GPU production because they can't get enough GDDR7 memory. Even the car industry is getting hit... Analysts are warning about a repeat of the pandemic-era chip shortage that shut down auto factories worldwide. All because AI companies decided their chatbots needed the memory more than your car does. And this doesn't get better for YEARS. Building a new memory fab takes 3-5 years minimum. Micron's new factory in Idaho won't meaningfully increase supply until 2027 at the earliest. By then, AI demand will have grown even more. Memory makers are already selling their 2027 AND 2028 capacity to AI customers today. There is no supply relief coming. That's why Elon is planning to build Tesla's own "TeraFab," a massive semiconductor plant that makes logic chips, memory, AND packaging all under one roof. He said existing suppliers including TSMC, Samsung, and Micron simply cannot supply Tesla at the levels the company needs. Think about that. One of the richest men in the world, running one of the largest companies on Earth, can't buy enough memory chips. So he's building his own factory. If ELON can't get supply, what chance does everyone else have? The AI revolution has a tax. And YOU'RE paying it. Every dollar Big Tech spends on AI infrastructure drives up the cost of the memory inside your phone, your laptop, your car, your TV, and your gaming console. $650 billion in AI spending this year. 3 companies controlling 90% of the memory supply. And every wafer they allocate to an Nvidia GPU is a wafer denied to the device in your pocket. The AI boom isn't free. You're subsidizing it every time you buy a piece of technology. And the bill just went up like crazy.

Ricardo

568,117 gรถrรผntรผleme โ€ข 7 ay รถnce

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media โ€“ in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own handโ€ฆ by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud โ€“ so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms โ€“ and of course it lives on our oceansโ€™ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to todayโ€™s modern industrial robots that are produced means of production, cloud capital does not produce anything โ€“ it comprises machines manufactured so as to modify human behaviour. Thatโ€™s what Amazonโ€™s Alexa or Googleโ€™s Assistant or Appleโ€™s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly โ€“ those algorithms are deployed, usually by digital devices tied to the workersโ€™ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capitalโ€™s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital โ€“ letโ€™s call them cloudalists โ€“ have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists โ€“ from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? โ€œHang onโ€, I hear you say. โ€œIs Jeff Bezos really different to Henry Ford? Arenโ€™t they all a species of monopoly capitalists? Monopolists?โ€ No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesnโ€™t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you donโ€™t โ€“ the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type โ€œelectric bicyclesโ€ or โ€œbinocularsโ€ while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeffโ€™s algorithm wants each one of us to see. Everyone navigating around โ€“ except Jeff Bezos of course โ€“ everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeffโ€™s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent โ€“ which is, of course, todayโ€™s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs โ€“ digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons โ€“ it functioned as a capitalism-free-zone โ€“ that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countriesโ€™ central banks, mostly! How did that happen? Well, by accident, or โ€“ to be more precise โ€“ byโ€ฆ crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds โ€“ along with yachts, art, bitcoin, NFTs any โ€˜assetโ€™ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! Thatโ€™s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour โ€” in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isnโ€™t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in โ€“ as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalismโ€™s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me โ€“ to convince me that, yes, cloud capital may be important but โ€œthis is still capitalism mateโ€. Letโ€™s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it wonโ€™t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marxโ€™s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the driversโ€™ routes and in-cabin habits. Elon Muskโ€™s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isnโ€™t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But itโ€™s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:
17:20

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media โ€“ in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own handโ€ฆ by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud โ€“ so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms โ€“ and of course it lives on our oceansโ€™ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to todayโ€™s modern industrial robots that are produced means of production, cloud capital does not produce anything โ€“ it comprises machines manufactured so as to modify human behaviour. Thatโ€™s what Amazonโ€™s Alexa or Googleโ€™s Assistant or Appleโ€™s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly โ€“ those algorithms are deployed, usually by digital devices tied to the workersโ€™ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capitalโ€™s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital โ€“ letโ€™s call them cloudalists โ€“ have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists โ€“ from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? โ€œHang onโ€, I hear you say. โ€œIs Jeff Bezos really different to Henry Ford? Arenโ€™t they all a species of monopoly capitalists? Monopolists?โ€ No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesnโ€™t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you donโ€™t โ€“ the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type โ€œelectric bicyclesโ€ or โ€œbinocularsโ€ while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeffโ€™s algorithm wants each one of us to see. Everyone navigating around โ€“ except Jeff Bezos of course โ€“ everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeffโ€™s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent โ€“ which is, of course, todayโ€™s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs โ€“ digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons โ€“ it functioned as a capitalism-free-zone โ€“ that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countriesโ€™ central banks, mostly! How did that happen? Well, by accident, or โ€“ to be more precise โ€“ byโ€ฆ crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds โ€“ along with yachts, art, bitcoin, NFTs any โ€˜assetโ€™ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! Thatโ€™s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour โ€” in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isnโ€™t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in โ€“ as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalismโ€™s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me โ€“ to convince me that, yes, cloud capital may be important but โ€œthis is still capitalism mateโ€. Letโ€™s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it wonโ€™t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marxโ€™s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the driversโ€™ routes and in-cabin habits. Elon Muskโ€™s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isnโ€™t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But itโ€™s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:

Yanis Varoufakis

1,817,382 gรถrรผntรผleme โ€ข 2 yฤฑl รถnce

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the worldโ€™s first Agentic Browser. What followed was a movement โ€” a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the worldโ€™s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering โ€œEveryoneโ€™s Personal Supercomputerโ€. While others have built AI-native browsers, no one though has built something agentic from the ground up โ€” with AI as the foundation, not a feature. How do you fundamentally improve the livesโ€™ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: โ€œbut why ?โ€ The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didnโ€™t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with itโ€™s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepadโ€ฆ they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, โ€œare you getting it ?โ€ And beneath this UI lies a new intelligence routing layer โ€” leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacyโ€ฆ Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesnโ€™t just remember โ€” it knows what matters. VectorRank helps your AI weigh your lifeโ€™s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you donโ€™t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thingโ€ฆ AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didnโ€™t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyoneโ€™s hands. This isnโ€™t a browser, or an IDE or limited to any device or cloud. Itโ€™s an entire AI operating system โ€” with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen ๐Ÿ‡บ๐Ÿ‡ธ Vinod Khosla Andrej Karpathy Sam Altman
2:52

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the worldโ€™s first Agentic Browser. What followed was a movement โ€” a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the worldโ€™s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering โ€œEveryoneโ€™s Personal Supercomputerโ€. While others have built AI-native browsers, no one though has built something agentic from the ground up โ€” with AI as the foundation, not a feature. How do you fundamentally improve the livesโ€™ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: โ€œbut why ?โ€ The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didnโ€™t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with itโ€™s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepadโ€ฆ they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, โ€œare you getting it ?โ€ And beneath this UI lies a new intelligence routing layer โ€” leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacyโ€ฆ Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesnโ€™t just remember โ€” it knows what matters. VectorRank helps your AI weigh your lifeโ€™s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you donโ€™t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thingโ€ฆ AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didnโ€™t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyoneโ€™s hands. This isnโ€™t a browser, or an IDE or limited to any device or cloud. Itโ€™s an entire AI operating system โ€” with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen ๐Ÿ‡บ๐Ÿ‡ธ Vinod Khosla Andrej Karpathy Sam Altman

Varun

169,177 gรถrรผntรผleme โ€ข 1 yฤฑl รถnce

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 โ€”> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 โ€”> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 โ€”> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 โ€”> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.
1:07

how to produce long form documentaries with claude this is how creators are producing long-form youtube documentaries in the sleep niche for about a low cost. you'll spend most of your effort building the workflow once, then every script after that runs through the same pipeline for cents. the format that works in this niche is different from normal youtube. your viewers are actively trying to fall asleep. that's the entire point. so people leave for two reasons: they got bored, or it worked and they're out. the ones who fall asleep come back later and keep listening. that repeat listening is a huge part of why the niche prints. which means the script is 90% of the whole thing. average view duration on my channels sits close to 25 min. that number does not come from cinematic visuals or fancy editing. it comes from narrative structure. if the script gets repetitive, drifts off topic, or loses momentum halfway, people stop listening. better footage cannot rescue a weak story here. the problem is the format does not scale on its own. one video needs a 15k-20k word script, hours of narration, hundreds of visual changes, music, and final assembly. writing that manually takes forever. editing every scene takes even longer. here's the workflow i set up: claude api (NOT the chat app. HIGHLY RECOMMENDED to not skip this. in the chat interface you end up typing "continue.. write chapter 4.. don't repeat yourself.. you forgot what happened in chapter 2" and by the halfway point it's contradicting earlier sections and drifting from the outline. you spend more time babysitting than writing. the api sends every request automatically and you pay per actual usage instead of another monthly subscription) google sheets connected to the claude api. this is the whole engine. you don't need to be a dev. the sheet does two things: first it generates the full documentary structure/outline. then it writes ONE chapter at a time instead of trying to produce the entire 20k words in a single response (which is where models fall apart). before each chapter, it passes claude three things: the outline, the instructions for that specific section, and a running summary of everything already written. that running summary is the trick. it's why chapter 8 never contradicts chapter 2. capcut ai video maker for the first edit. it generates voiceover, subtitles, and an initial visual sequence from auto-matched stock footage. the stock matching is not perfect, but it gets you a 90% first draft way faster than manually searching for hundreds of clips. note: capcut caps at 3000 words, so you split the script into sections, generate each one, export, and combine into the final video. HERE'S HOW THE PRODUCTION ACTUALLY RUNS: step 1 โ€”> topic + title + thumbnail. do NOT skip this. ai cannot tell you which topic has demand or whether a title creates curiosity. this is where most of the value still is. figure this out before you touch any automation. step 2 โ€”> run the sheet. it builds the outline first, then writes chapter by chapter, feeding itself the running summary each time so it stays consistent. cost for a full script usually lands around $0.30-0.40 depending on the model, input length, and number of revisions. step 3 โ€”> paste script into capcut in sub-3000 word chunks. generate voiceover + subtitles + auto-matched visuals for each. export each section. step 4 โ€”> combine sections into the final 2-3 hour video. then you handle the parts ai can't: pacing check, misleading visuals, final editorial judgment. the reason this matters is repeatability. every script moves through the exact same production structure, but you can still change the topic, tone, evidence, pacing, and narrative direction each time. so it stops being random one-off videos and starts being a system. the math: capcut is ~$20/mo and allows many exports. claude api is a little above thirty cents per script. at 30-40 documentaries a month that works out to roughly $1 in direct software cost per finished video. that figure does NOT include your time, research, thumbnails, subscriptions, failed ideas, or the cost of building the workflow itself. it is not the full cost of the business, it's the direct software cost. one more thing worth knowing: mixing real historical/stock footage alongside ai assets is the best defense i've found against the "reused/inauthentic content" flags that destroy fully automated channels. that's from experience, not a rule youtube publishes. this is not passive income and it's not a one-click youtube machine. it's a production system that makes experimentation cheaper. ai removes the repetitive work. it does not remove the need for taste.

Sulfur

25,540 gรถrรผntรผleme โ€ข 1 ay รถnce

$AMD $5 Trillion MC Is Inevitable Long Term๐Ÿ‘‘ This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of ๐Ÿ’ฉ or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same โ†’ make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!
3:09

$AMD $5 Trillion MC Is Inevitable Long Term๐Ÿ‘‘ This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of ๐Ÿ’ฉ or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same โ†’ make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

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Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 โ†’ $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. โ†“ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. โ†“ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. โ†“ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" โ€” No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. โ†“ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. โ†“ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. โ†“ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. โ†“ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. โ†“ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. โ†“ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. โ†“ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. โ†“ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. โ†“ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. โ†“ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.
18:53

Made $530,000 with Ai Bot that started with $313. Didn't know how to code. Now this bots run 24/7 printing money while sleeping. I've made the exact step-by-step guide to build this Claude Code Polymarket trading bot. Prompts. Code. Risk settings. Paper trading checklist. Everything from zero to running bot. It's free. For 24 hours. After that I'm charging $499 for it. To grab it right now: 1. Comment "Claude Bot" 2. Like and Retweet this post 3. Follow me Himanshu Kumar ( I can't send DMs to non-followers ) I'm DMing everyone who Complete the 3 steps. I spent hundreds of thousands hiring developers because he was too scared to learn. Then learned Claude Code. Built algorithmic trading systems. $313 โ†’ $530,000. You have the same tools available right now. And you're using them to ask ChatGPT for Instagram captions. This attached video is a goldmine. Full live walkthrough. Claude Code building actual Polymarket trading bots. From zero. Every line of code. Every decision explained. Now let me break down why everything you're doing in trading is wrong and exactly how to fix it. Save this post. You'll hate yourself if you lose it. โ†“ Let's start with why you keep losing money. You already know the answer. You just won't admit it. You overtrade. Every. Single. Day. You see a candle move. You feel something. You enter. No plan. No edge. No reason. Just feelings. Then it goes against you. You feel something else. Panic. Anger. Denial. You move your stop loss. Or you didn't set one at all. "It'll come back." It doesn't come back. So you take another trade. A revenge trade. Bigger size this time. Because you need to "make it back." That one fails too. Now you're emotional. Now you're tilted. Now you're using leverage you have no business touching. 40x. 50x. 100x. On a trade you entered because a candle looked "bullish" and some guy on Twitter said "send it." You get liquidated. Close the laptop. Punch something. Tell yourself you'll be "more disciplined" tomorrow. Tomorrow comes. Same cycle. Same result. Same liquidation. You've been doing this for months. Maybe years. And you still think the problem is your strategy. The problem isn't your strategy. The problem is you. Save this post right now. What I'm about to show you is the only way to remove yourself from the equation. Follow Himanshu Kumar so you don't miss any of this. โ†“ Here's what's actually killing your account. It's not the market. The market doesn't care about you. It's not your indicators. RSI works fine. MACD works fine. They all "work." It's not your timeframe. It's not your broker. It's not the "manipulation." It's four things: 1. Emotions. You hold losers because hope feels better than loss. You cut winners because fear feels stronger than greed. You size up when angry. You skip trades when scared. Your emotional state determines your position size. That's insane. And you know it's insane. But you keep doing it. 2. Overtrading. You take 15 trades a day. Maybe 5 of them had actual setups. The other 10 were boredom. Boredom trades are the most expensive hobby in human history. 3. Leverage. You use 20x-50x on trades where you're not even sure about the direction. That's not trading. That's a casino with a nicer interface. 4. Fees. You're smashing market orders. Paying spread. Paying commission. On 15 trades a day. Your broker makes more money from your account than you do. Think about that. Your broker is profitable on your account. You're not. You're the product. Not the trader. These four things are why 90% of traders lose. Not bad luck. Not the market. You. Save this post and follow Himanshu Kumar because the solution is coming next. โ†“ The solution is painfully obvious. Remove yourself from the equation. Not partially. Not "I'll be more disciplined." Not "I'll journal my trades." Not "I'll meditate before trading." Completely remove yourself. Build a bot. Let the bot trade. You go live your life. The bot doesn't feel emotions. The bot doesn't overtrade. The bot doesn't use reckless leverage. The bot doesn't smash market orders and bleed fees. The bot follows the rules. Every single time. Without exception. Without "just this once." Without "I have a feeling about this one." Rules in. Execution out. No human in the middle to mess everything up. That's algorithmic trading. And before your ego jumps in with "but I'm different, I have discipline" โ€” No you don't. Your account balance proves you don't. If you had discipline, your account would be green. It's not. So you don't. Accept it. Automate it. Move on. This is the hardest truth in trading. Your discipline will always fail. A bot's won't. Save this post. Follow Himanshu Kumar for the exact bot setup that removes your emotions permanently. โ†“ "But I don't know how to code." Neither did he. The guy in this video didn't know how to code for most of his life. Got held back in 7th grade. People counted him out early. Spent years building apps and SaaS businesses without writing a single line of code. Hired developers on Upwork instead. Spent hundreds of thousands of dollars paying other people to build what he could have built himself. Because he was scared to learn. That fear cost him years. And hundreds of thousands of dollars. Sound familiar? You're doing the same thing right now. Not with developers. But with your time. You're spending thousands of hours trading manually because you're scared to learn the thing that would make trading automatic. The fear of learning to code is costing you more than any bad trade ever did. Because every month you trade manually is a month of emotional decisions, overleveraged entries, and unnecessary losses that a bot would never make. And here's the thing that should really frustrate you: AI does the hard parts now. You don't need a computer science degree. You don't need to work at a hedge fund. You don't need to be "good at math." Claude Code writes the code for you. You just need to think clearly about trading ideas. That's it. If you can describe a strategy in English, Claude can build it in Python. "I don't know how to code" stopped being a valid excuse in 2024. It's 2026. You're 2 years late on that excuse. Find a new one. Or stop making excuses entirely. Save this post. Follow Himanshu Kumar because I'm showing you how people with zero coding experience are building profitable bots. โ†“ The process that actually makes money. Three letters. R. B. I. Research. Backtest. Implement. That's it. That's the entire process. Every single day. Research: Find an idea. A pattern. A market inefficiency. Don't trade it yet. Don't even think about trading it yet. Just research it. Backtest: Test the idea against historical data. Does it work? Not "does it look good on one chart." Does it work across thousands of trades? Across different market conditions? Across in-sample AND out-of-sample data? If no, kill it. Find another idea. If yes, move to step 3. Implement: Build the bot. Deploy it. Paper trade first. Then live with small size. Scale only on evidence. Research. Backtest. Implement. Every day. No exceptions. You know what your current process is? Feel. Enter. Pray. F. E. P. Feel bullish. Enter a trade. Pray it works. That's not a process. That's gambling with a TradingView subscription. RBI is the only process that works. Save this post. Tattoo it on your forearm. Follow Himanshu Kumar for daily RBI breakdowns. โ†“ What Claude Code actually does that your manual process can't. You can maybe test 3-5 strategy ideas per week. Manually adjusting parameters. Manually checking results. Manually writing code (badly). Claude Code tests 50-100 ideas per week. With parallel agents running simultaneously. Multiple strategies being built, tested, and validated at the same time. While you sleep. The guy in this video spends 4-8 hours a day building systems with Claude Code. Not trading. Building. Research. Backtest. Implement. Then iterate. Improve. Optimize. Every day the systems get better. Every day the edge compounds. Every day the bots get smarter. While you? You spend 4-8 hours a day staring at charts making the same mistakes you made last month. Same indicators. Same patterns. Same entries. Same losses. He's iterating forward. You're running in circles. Same 8 hours per day. Completely different outcomes. Because he's building systems. And you're feeding a casino. Stop feeding the casino. Start building the machine. Save this post and follow Himanshu Kumar for the Claude Code workflow that iterates strategies while you sleep. โ†“ Jim Simons. That's the benchmark. You probably don't know who Jim Simons is. And that tells me everything about how seriously you take trading. Jim Simons. Mathematician. Founded Renaissance Technologies. Built a net worth of $31 billion. 100% from algorithmic trading. Not one single manual trade. Not one "gut feeling" entry. Not one RSI divergence. Not one "smart money concept." Algorithms. Bots. Systems. Data. $31 billion. His fund averaged 66% annual returns for over 30 years. While you're excited about making $200 on a trade that you'll give back tomorrow. The best trader in human history never placed a manual trade in his life. And you think your edge is staring at a 5-minute chart with bloodshot eyes at 2 AM? Your edge is building the system. Not being inside it. Jim Simons is the benchmark. Everything else is noise. Save this post. Follow Himanshu Kumar because I'm building toward the same goal and showing every step publicly. โ†“ What you need to understand about patience. This is not get-rich-overnight. The guy in this video says it directly: "This channel is not for people looking to get rich overnight. It's not plug and play. There are no shortcuts. If you're impatient, this probably isn't for you." And that's exactly why most people will fail at this. Because you want results now. Today. This trade. You don't want to spend a week building a bot. You don't want to paper trade for 2 weeks. You don't want to test 50 ideas to find 1 that works. You want to copy someone's bot, run it live with your rent money, and be rich by Friday. That's why you'll be broke by Friday. The guy making $2.3M spent months iterating. Testing. Failing. Rebuilding. Testing again. He was patient when you would have quit. He was calm when you would have panicked. He was consistent when you would have given up. Patience isn't just a virtue in trading. It's the only virtue. Without it, everything else fails. Impatience is the most expensive personality trait in trading. Save this post. Follow Himanshu Kumar and learn to build systems with the patience that actually pays. โ†“ The live streams where the real learning happens. The YouTube video is the trailer. The live streams are the movie. Real-time bot building. Real-time questions answered. Real code shown. Real mistakes made and fixed. Not polished highlight reels where everything works perfectly. Actual development. Where things break. Where strategies fail. Where code doesn't compile. Where the fix takes 2 hours. Because that's what real development looks like. And seeing the messy parts is more valuable than any polished tutorial. Because when your bot breaks at 3 AM, you need to know how to fix it. Not just how to celebrate when it works. The streams mix beginner and advanced. Start with how to automate trading. How to use AI for code generation. Then dive into the daily work. Claude Code. Parallel agents. Constant iteration. Live debugging. 4-8 hours of real algorithmic trading development. Live. Uncut. No filter. Most "trading education" shows you the wins. This shows you the work. Save this post. Follow Himanshu Kumar for the stream schedules and breakdowns. โ†“ The belief that changes everything. Code is the greatest equalizer. Not money. Not connections. Not a degree. Not where you grew up. Not what school you went to. Code. Once you can build systems, you can build anything. For the rest of your life. A trading bot today. A SaaS product tomorrow. An automation business next month. A completely different life next year. The skill isn't "algorithmic trading." The skill is building systems. And that skill transfers to everything. The guy who can build a trading bot can also build a lead gen tool. Can also build a content pipeline. Can also build a SaaS product. Can also build literally anything that runs on logic and code. One skill. Infinite applications. And AI makes learning it 100x easier than it was 5 years ago. You don't need to be smart. You don't need talent. You need Claude Code and the willingness to sit down and build something instead of consuming content about building something. Building is the skill. Everything else is entertainment disguised as education. Save this post. Follow Himanshu Kumar because I'm showing you how to build, not just how to watch. โ†“ If any of this applies to you, pay attention. If you've lost money from overtrading. If you've been liquidated. If you know trading is the vehicle but manual execution keeps crashing you. If you've tried "being more disciplined" and it never lasted more than a week. If you keep saying "next month I'll start automating." If you've spent more money on courses than you've made from trading. There is a better way. It's not a magic indicator. It's not a signal group. It's not a $997 mentorship from a guy who makes money teaching, not trading. It's building your own system. A system that trades without emotion. A system that follows rules without exception. A system that runs while you sleep. A system that compounds while you live your life. That's the answer. It's always been the answer. You've just been too scared to accept that the solution requires building something instead of buying something. โ†“ What the next 30 days look like if you actually commit. Week 1: Watch the video. Learn Claude Code basics. Build your first simple strategy. Run your first backtest. Week 2: Iterate. Let Claude improve the strategy. Run Monte Carlo validation. Paper trade. Week 3: Go live with $50-100. Tiny positions. Watch every trade. Compare to paper results. Week 4: Scale based on evidence. Not based on excitement. Not based on one good day. Based on data. 30 days from now you either have a running bot that trades without your emotions destroying every position. Or you're exactly where you are right now. Reading another post. Making another promise. Breaking it by Tuesday. Same 30 days either way. Different actions. Different results. Different life. โ†“ Full video tutorial attached. Live bot building with Claude Code. From zero to running Polymarket trading bot. Every line of code. Every decision explained. The video is free. Claude Code is available now. The market is open 24/7. The only thing standing between you and a profitable trading bot is the same thing that's been standing there for months. You. Get out of your own way. Follow Himanshu Kumar for daily AI trading bot breakdowns, live build sessions, and the full RBI process. Save this post. Watch the video. Build the bot. Or keep trading manually and keep losing. The choice has never been easier. And you've never been more stubborn about making the wrong one.

Himanshu Kumar

38,153 gรถrรผntรผleme โ€ข 5 ay รถnce

This Silicon Valley insider just exposed Sam Altman and Dario Amodei of running a con on the entire world. Ed Zitron founded a tech PR agency in 2013 and has spent 13 years being paid to make tech companies look good to reporters. A trade list of the top 50 PR people in tech has named him four separate times. In June he obtained OpenAI's audited financial statements and published them. He just sat down with Steven Bartlett and said: "I think generative AI is at its heart a con." And he also explained whyโ€ฆ OpenAI booked $13.07 billion of revenue in 2025 and spent $34 billion getting it. The operating loss came to $20.92 billion. Research and development alone cost $19.18 billion. That's more than the company's entire revenue. And the same filings show where a lot of it went: OpenAI paid Microsoft $17.2 billion last year. Microsoft paid OpenAI $303 million back. But this is where it gets really interestingโ€ฆ You have never once paid what any of this actually costs. SemiAnalysis bought every subscription tier OpenAI and Anthropic sell, then ran coding tasks until the weekly limits died. The gap they found is huge: A $200 ChatGPT Pro plan absorbed $14,000 worth of tokens at list API prices. Claude Max absorbed $8,000. OpenAI starts losing money on a Plus subscriber the moment that person uses 11.4% of what they're allowed. So every impression you have of these tools was formed while somebody else covered the bill. Uber found out what the honest price looks like. The company burned through its ENTIRE annual AI budget by April. President and COO Andrew Macdonald said the costs were getting hard to justify. Engineers there are now capped at $1,500 a month per coding tool. That's one of the most sophisticated technology companies on Earth putting its own staff on a leash. Zitron's argument is that the demand everyone points to was never real. It's demand at a price nobody has been asked to pay yet. But the money loop is whatโ€™s really concerning here: Nvidia holds equity in CoreWeave, sells CoreWeave the chips, and signed a backstop with an initial value of $6.3 billion. CoreWeave's own filing shows us the terms: When its data center capacity isn't fully used by its own customers, Nvidia is OBLIGATED to buy the unsold capacity. That obligation runs through April 2032. So Nvidia sells the GPUs, funds the buyer, and guarantees the demand. Then CoreWeave walks into a bank holding a signed customer contract and borrows against it. Zitron's read on that: If you want to build a profitable business, a bank tells you to get lost. If you want to buy GPUs, it's open season. And Anthropic complicates his case. The company is running at a $47 billion revenue run rate and projected an operating profit for the second quarter of this year. Amazon lost money for over a decade before AWS turned. Hundreds of millions of people use these products daily and that adoption isn't fake. If you think about it like that, this is every infrastructure build that got called insane right before it worked. Zitron says the difference is the size of the hole. Amazon burned $29.7 billion across 12 years. OpenAI burned $21 billion in 12 MONTHS and has pledged roughly $600 billion toward infrastructure through 2030. Asked what would change his mind, he said a hardware breakthrough that cuts the cost by a thousand. Nobody has one. So this ends in a crash that takes ordinary retirement accounts down with it. The companies that own those revealing numbers won't publish them at all. Only one of those two is hiding something. And most retirement money in America is already indexed to the companies doing the hiding. This wonโ€™t end well.
2:41

This Silicon Valley insider just exposed Sam Altman and Dario Amodei of running a con on the entire world. Ed Zitron founded a tech PR agency in 2013 and has spent 13 years being paid to make tech companies look good to reporters. A trade list of the top 50 PR people in tech has named him four separate times. In June he obtained OpenAI's audited financial statements and published them. He just sat down with Steven Bartlett and said: "I think generative AI is at its heart a con." And he also explained whyโ€ฆ OpenAI booked $13.07 billion of revenue in 2025 and spent $34 billion getting it. The operating loss came to $20.92 billion. Research and development alone cost $19.18 billion. That's more than the company's entire revenue. And the same filings show where a lot of it went: OpenAI paid Microsoft $17.2 billion last year. Microsoft paid OpenAI $303 million back. But this is where it gets really interestingโ€ฆ You have never once paid what any of this actually costs. SemiAnalysis bought every subscription tier OpenAI and Anthropic sell, then ran coding tasks until the weekly limits died. The gap they found is huge: A $200 ChatGPT Pro plan absorbed $14,000 worth of tokens at list API prices. Claude Max absorbed $8,000. OpenAI starts losing money on a Plus subscriber the moment that person uses 11.4% of what they're allowed. So every impression you have of these tools was formed while somebody else covered the bill. Uber found out what the honest price looks like. The company burned through its ENTIRE annual AI budget by April. President and COO Andrew Macdonald said the costs were getting hard to justify. Engineers there are now capped at $1,500 a month per coding tool. That's one of the most sophisticated technology companies on Earth putting its own staff on a leash. Zitron's argument is that the demand everyone points to was never real. It's demand at a price nobody has been asked to pay yet. But the money loop is whatโ€™s really concerning here: Nvidia holds equity in CoreWeave, sells CoreWeave the chips, and signed a backstop with an initial value of $6.3 billion. CoreWeave's own filing shows us the terms: When its data center capacity isn't fully used by its own customers, Nvidia is OBLIGATED to buy the unsold capacity. That obligation runs through April 2032. So Nvidia sells the GPUs, funds the buyer, and guarantees the demand. Then CoreWeave walks into a bank holding a signed customer contract and borrows against it. Zitron's read on that: If you want to build a profitable business, a bank tells you to get lost. If you want to buy GPUs, it's open season. And Anthropic complicates his case. The company is running at a $47 billion revenue run rate and projected an operating profit for the second quarter of this year. Amazon lost money for over a decade before AWS turned. Hundreds of millions of people use these products daily and that adoption isn't fake. If you think about it like that, this is every infrastructure build that got called insane right before it worked. Zitron says the difference is the size of the hole. Amazon burned $29.7 billion across 12 years. OpenAI burned $21 billion in 12 MONTHS and has pledged roughly $600 billion toward infrastructure through 2030. Asked what would change his mind, he said a hardware breakthrough that cuts the cost by a thousand. Nobody has one. So this ends in a crash that takes ordinary retirement accounts down with it. The companies that own those revealing numbers won't publish them at all. Only one of those two is hiding something. And most retirement money in America is already indexed to the companies doing the hiding. This wonโ€™t end well.

Ricardo

55,913 gรถrรผntรผleme โ€ข 23 gรผn รถnce

Elon Musk just said on camera that America CANNOT beat China with humans alone. His exact words: "We definitely can't win on the human front." This is the richest man on the planet. Advisor to the president. And he's saying the US is cooked without robots. Here's why he's probably right: China is about to hit 3x the total US electricity output. Elon says electricity is a direct proxy for industrial capacity. Three times the electricity means roughly three times the manufacturing power. They have 4x the population. And Elon said something that'll piss a lot of people off: "The average work ethic in China is higher than in the US." America's birth rate has been below replacement since 1971. More people retiring every year. Fewer entering the workforce. No amount of policy, tariffs, or reshoring fixes that math. His solution: Optimus. He literally called it "the infinite money glitch." Because you can use robots to build more robots. Here's what makes this different from every other robotics play: 3 things are hard about humanoid robots. 1. Real-world AI 2. The hand 3. Scale manufacturing And the hand is harder than EVERYTHING else combined. Tesla had to custom design every single actuator, motor, gear, sensor, and control system from physics first principles. There is no supply chain. Nothing comes from a catalog. Not a single component. But they've solved it. Optimus has full human-hand dexterity with all degrees of freedom. No other company has demonstrated this. Not even in demos. Then you layer on what Elon described as a "recursive multiplicative exponential": Exponential growth in digital intelligence. Multiplied by exponential growth in chip capability. Multiplied by exponential growth in electromechanical dexterity. And then the robots start building robots. He's targeting 1 million Optimus units per year at Gen 3. Ten million at Gen 4. The first use case? Any operation that runs 24/7. Factories, warehouses, refineries, every continuous operation on the planet. Robots don't sleep, don't overheat, don't quit. And here's the part that should terrify every other country: America can't build enough ore refineries because Americans don't want refining jobs. China does 2x more ore refining than the rest of the world COMBINED. They dominate rare earths. The US literally mines rare earth ore, puts it on a train, ships it to CHINA for refining, then ships the finished product back. Optimus wants to fix that. Not by convincing Americans to take refining jobs but by making humans optional in the process entirely. And Elon also said something else that went completely under the radar: "Pure AI, pure robotics corporations will FAR outperform any corporations that have humans in the loop." He compared it to spreadsheets replacing human computers. Entire skyscrapers used to be filled with humans doing calculations. A laptop replaced all of them. Now imagine replacing some cells in your spreadsheet with humans again. It would be WORSE. That's his prediction for the future of corporations. Mixed human-AI companies lose to pure AI-robotics companies. Not by a little. By orders of magnitude. The race isn't AI models anymore. It's not chatbots or benchmarks or who scores higher on some test. The race is physical. Whoever builds the robot army first wins the entire global economy. China has the workers. The factories. The electricity. The refining. The supply chains. America has one card left to play... And it's a 5'11" humanoid robot that Elon calls the infinite money glitch. This is either the move that saves American manufacturing. Or the most disastrous science project in history.
2:19

Elon Musk just said on camera that America CANNOT beat China with humans alone. His exact words: "We definitely can't win on the human front." This is the richest man on the planet. Advisor to the president. And he's saying the US is cooked without robots. Here's why he's probably right: China is about to hit 3x the total US electricity output. Elon says electricity is a direct proxy for industrial capacity. Three times the electricity means roughly three times the manufacturing power. They have 4x the population. And Elon said something that'll piss a lot of people off: "The average work ethic in China is higher than in the US." America's birth rate has been below replacement since 1971. More people retiring every year. Fewer entering the workforce. No amount of policy, tariffs, or reshoring fixes that math. His solution: Optimus. He literally called it "the infinite money glitch." Because you can use robots to build more robots. Here's what makes this different from every other robotics play: 3 things are hard about humanoid robots. 1. Real-world AI 2. The hand 3. Scale manufacturing And the hand is harder than EVERYTHING else combined. Tesla had to custom design every single actuator, motor, gear, sensor, and control system from physics first principles. There is no supply chain. Nothing comes from a catalog. Not a single component. But they've solved it. Optimus has full human-hand dexterity with all degrees of freedom. No other company has demonstrated this. Not even in demos. Then you layer on what Elon described as a "recursive multiplicative exponential": Exponential growth in digital intelligence. Multiplied by exponential growth in chip capability. Multiplied by exponential growth in electromechanical dexterity. And then the robots start building robots. He's targeting 1 million Optimus units per year at Gen 3. Ten million at Gen 4. The first use case? Any operation that runs 24/7. Factories, warehouses, refineries, every continuous operation on the planet. Robots don't sleep, don't overheat, don't quit. And here's the part that should terrify every other country: America can't build enough ore refineries because Americans don't want refining jobs. China does 2x more ore refining than the rest of the world COMBINED. They dominate rare earths. The US literally mines rare earth ore, puts it on a train, ships it to CHINA for refining, then ships the finished product back. Optimus wants to fix that. Not by convincing Americans to take refining jobs but by making humans optional in the process entirely. And Elon also said something else that went completely under the radar: "Pure AI, pure robotics corporations will FAR outperform any corporations that have humans in the loop." He compared it to spreadsheets replacing human computers. Entire skyscrapers used to be filled with humans doing calculations. A laptop replaced all of them. Now imagine replacing some cells in your spreadsheet with humans again. It would be WORSE. That's his prediction for the future of corporations. Mixed human-AI companies lose to pure AI-robotics companies. Not by a little. By orders of magnitude. The race isn't AI models anymore. It's not chatbots or benchmarks or who scores higher on some test. The race is physical. Whoever builds the robot army first wins the entire global economy. China has the workers. The factories. The electricity. The refining. The supply chains. America has one card left to play... And it's a 5'11" humanoid robot that Elon calls the infinite money glitch. This is either the move that saves American manufacturing. Or the most disastrous science project in history.

Ricardo

49,857 gรถrรผntรผleme โ€ข 7 ay รถnce

Deepseek V4 Flash 0731 (Q2) - 12 tokens/sec - Single RTX 4090 - 650+ tokens/sec prefill - 250k context - no kv cache quantization! DeepSeek just dropped the official V4 Flash 0731 two days ago with a massive agent capabilities upgrade. The official benchmarks are literally crushing their own V4-Pro-Preview on agentic tasks like Terminal Bench 2.1 and DeepSWE. Unsloth AI said they couldn't wait to bring it to local devices, and they delivered. If you thought my 118B Poolside Laguna S 2.1 MoE run last week on a single GPU was wild, hold onto your hardware. I just successfully ran Unslothโ€™s brand new 91GB DeepSeek-V4-Flash-0731 (UD-IQ2_M) GGUF entirely locally. And I pushed it to a mind-bending 250,000 context window. The VRAM ceiling is an illusion if you know how to optimize llama.cpp. Here are the benchmarks and the cheat codes to run a local frontier class model yourself. For the hardware and setup, I used a single NVIDIA RTX 4090 (24GB VRAM) hooked up via a PCIe 4 bus, running Ubuntu 22.04 LTS and CUDA 13.0. You don't need a massive enterprise server for this, if you have more than 80 GB of standard DDR4 RAM and a 24GB card like an RTX 3090 or 4090, you can run this exact stack yourself. All benchmarks were run using a massive 28k token prompt to truly stress test the prefill limits. no kv cache quantization THE BENCHMARKS (Scaling Context): # 80k Context (Baseline: -b 2048 -ub 2048): Prefill: 465.43 t/s | Decode: 13.00 t/s | VRAM: 22.87 GB # 80k Context (Optimized: -b 4096 -ub 4096): Prefill: 643.15 t/s | Decode: 12.20 t/s | VRAM: 23.00 GB (Notice how doubling the batch flags spiked my prefill throughput by nearly 200 t/s with almost zero VRAM penalty) # 180k Context (-b 4096 -ub 4096): Prefill: 629.18 t/s | Decode: 11.92 t/s | VRAM: 23.40 GB # 250k Context MAXIMUM (-b 4096 -ub 4096): Prefill: 619.02 t/s | Decode: 11.54 t/s | VRAM: 23.40 GB # THE SECRET SAUCE (Why this works): Unslothโ€™s UD-IQ2_M quant is ~91GB across 3 files. Since I only have 24GB of VRAM, the PCIe 4 bus and system RAM have to do the heavy lifting. The magic bullet is the --no-mmap flag. By completely bypassing OS disk paging, I forced llama.cpp to load the massive model weights directly into the system RAM upfront. Combined with Flash Attention (-fa on) and exactly 12 CPU threads (--threads 12), I maintained an incredibly stable 11.5+ tokens/sec decode speed even at a quarter million token context. # THE EXACT COMMAND: ./build/bin/llama-server -m /workspace/models/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf -c 250000 -fa on --port 8080 --threads 12 -b 4096 -ub 4096 --no-mmap -v Local conversational and agentic coding AI is fully here. You donโ€™t need an API or an H100 cluster. Qwen 3.8 27b drops next week making the 24GB VRAM tier even more worthwhile. What does your current local AI rig look like, and what's the craziest model you've managed to squeeze into it? Official huggingface GGUF links from Unsloth and performance graphs are dropped in the replies below!
0:21

Deepseek V4 Flash 0731 (Q2) - 12 tokens/sec - Single RTX 4090 - 650+ tokens/sec prefill - 250k context - no kv cache quantization! DeepSeek just dropped the official V4 Flash 0731 two days ago with a massive agent capabilities upgrade. The official benchmarks are literally crushing their own V4-Pro-Preview on agentic tasks like Terminal Bench 2.1 and DeepSWE. Unsloth AI said they couldn't wait to bring it to local devices, and they delivered. If you thought my 118B Poolside Laguna S 2.1 MoE run last week on a single GPU was wild, hold onto your hardware. I just successfully ran Unslothโ€™s brand new 91GB DeepSeek-V4-Flash-0731 (UD-IQ2_M) GGUF entirely locally. And I pushed it to a mind-bending 250,000 context window. The VRAM ceiling is an illusion if you know how to optimize llama.cpp. Here are the benchmarks and the cheat codes to run a local frontier class model yourself. For the hardware and setup, I used a single NVIDIA RTX 4090 (24GB VRAM) hooked up via a PCIe 4 bus, running Ubuntu 22.04 LTS and CUDA 13.0. You don't need a massive enterprise server for this, if you have more than 80 GB of standard DDR4 RAM and a 24GB card like an RTX 3090 or 4090, you can run this exact stack yourself. All benchmarks were run using a massive 28k token prompt to truly stress test the prefill limits. no kv cache quantization THE BENCHMARKS (Scaling Context): # 80k Context (Baseline: -b 2048 -ub 2048): Prefill: 465.43 t/s | Decode: 13.00 t/s | VRAM: 22.87 GB # 80k Context (Optimized: -b 4096 -ub 4096): Prefill: 643.15 t/s | Decode: 12.20 t/s | VRAM: 23.00 GB (Notice how doubling the batch flags spiked my prefill throughput by nearly 200 t/s with almost zero VRAM penalty) # 180k Context (-b 4096 -ub 4096): Prefill: 629.18 t/s | Decode: 11.92 t/s | VRAM: 23.40 GB # 250k Context MAXIMUM (-b 4096 -ub 4096): Prefill: 619.02 t/s | Decode: 11.54 t/s | VRAM: 23.40 GB # THE SECRET SAUCE (Why this works): Unslothโ€™s UD-IQ2_M quant is ~91GB across 3 files. Since I only have 24GB of VRAM, the PCIe 4 bus and system RAM have to do the heavy lifting. The magic bullet is the --no-mmap flag. By completely bypassing OS disk paging, I forced llama.cpp to load the massive model weights directly into the system RAM upfront. Combined with Flash Attention (-fa on) and exactly 12 CPU threads (--threads 12), I maintained an incredibly stable 11.5+ tokens/sec decode speed even at a quarter million token context. # THE EXACT COMMAND: ./build/bin/llama-server -m /workspace/models/DeepSeek-V4-Flash-0731-UD-IQ2_M-00001-of-00003.gguf -c 250000 -fa on --port 8080 --threads 12 -b 4096 -ub 4096 --no-mmap -v Local conversational and agentic coding AI is fully here. You donโ€™t need an API or an H100 cluster. Qwen 3.8 27b drops next week making the 24GB VRAM tier even more worthwhile. What does your current local AI rig look like, and what's the craziest model you've managed to squeeze into it? Official huggingface GGUF links from Unsloth and performance graphs are dropped in the replies below!

Alok

46,100 gรถrรผntรผleme โ€ข 1 ay รถnce