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Somebody quit his data center job and leased an empty warehouse. filled it with rows of server machines. charges clients between $4-6 K p/month for private isolated deployment. Each client receives dedicated machines to host model. He runs all on vLLM.

Rohan Paul

155,549 subscribers

129,027 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

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Brett makes $1.3M/year as a solopreneur and runs his business entirely from home. But the craziest part? He doesn't take a single client meeting or Zoom call... To manage 20+ clients completely by himself without burning out, he relies on a dead-simple framework: > clients drop design requests straight into a Trello queue > he knocks them out one-by-one with zero async clutter or Slack messaging His total overhead to run the business? Exactly $176 a month. He didn't even quit his 9-to-5 day job until he was bringing in $80,000 a month from his side hustle.

Brett makes $1.3M/year as a solopreneur and runs his business entirely from home. But the craziest part? He doesn't take a single client meeting or Zoom call... To manage 20+ clients completely by himself without burning out, he relies on a dead-simple framework: > clients drop design requests straight into a Trello queue > he knocks them out one-by-one with zero async clutter or Slack messaging His total overhead to run the business? Exactly $176 a month. He didn't even quit his 9-to-5 day job until he was bringing in $80,000 a month from his side hustle.

Starter Story

21,144 просмотров • 2 месяцев назад

He built an "AI concierge" offer and tested it by telling the first 6 people he met. 5 said yes. $8K MRR in 10 days. The whole offer: two 45-minute Zoom calls a month. They share their screen. He helps them build with AI. That's it. He raised the price with every yes: $1,200 → $1,500 → $1,800 → $2,000 At $1,500/month, that's a $1,000/hour rate. The genius part: clients also get "unlimited" access between calls. In 2.5 months, only 3 of his 5 clients messaged him, once each. Massive perceived value. Almost zero work. He capped it at 6 clients.

He built an "AI concierge" offer and tested it by telling the first 6 people he met. 5 said yes. $8K MRR in 10 days. The whole offer: two 45-minute Zoom calls a month. They share their screen. He helps them build with AI. That's it. He raised the price with every yes: $1,200 → $1,500 → $1,800 → $2,000 At $1,500/month, that's a $1,000/hour rate. The genius part: clients also get "unlimited" access between calls. In 2.5 months, only 3 of his 5 clients messaged him, once each. Massive perceived value. Almost zero work. He capped it at 6 clients.

The Startup Ideas Podcast (SIP) 🧃

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I just interviewed a guy who did $100,000 in his first 100 days placing overseas talent with blue collar business owners. He started with $200. No tech background. No recruiting experience. His entire tech stack to launch the company was QuickBooks, Wi-Fi, a computer, and Lovable. That's it. What does he do? He runs a staffing agency that connects plumbers, electricians, and landscapers with full-time admins and marketers from Latin America. The talent costs the business owner around $1,200 to $1,600 a month full-time. He charges his clients $4,500 to $5,200 one time to make the placement. Then he offers a $200 a month membership for ongoing support. 75% of his clients pay for the membership. In this episode Alex: - Breaks down exactly how he finds great overseas talent on LinkedIn and Facebook - Shows the specific interview questions he asks to spot reliability before he ever hires - Walks through how to close your first client with a three-sentence text - Explains why 90% of his placements are brand new hires, not replacements for somebody who got fired - Gives the exact payment tools and onboarding process he uses with every client This one was really good!

Chris Koerner

118,082 просмотров • 2 месяцев назад

A 21-year-old clears $35K a month from his bedroom. One laptop. Claude does 90% of the work he charges for. His parents still think he’s “looking for a job.” He stopped looking 8 months ago. Part 1 - Sell automations, not hours. Businesses drown in manual work - invoicing, lead follow-ups, booking reminders. He builds the system that kills the busywork. They pay $2,500 upfront plus $300/mo. Claude writes every workflow. Part 2 - Claude finds the buyers. Point it at a niche - real estate, clinics, contractors. It pulls 200 businesses still running on sticky notes and spreadsheets. The lead list builds itself by morning. Part 3 - One blueprint, every client. Same automation skeleton each time. Claude reads their process, rewires it, swaps the branding. 20 minutes per build instead of a week. Part 4 - The real ramp. Month 1: 3 clients, $7,500. Month 3: 9 clients, $19,000. Month 5: 15 clients, $30,000. Month 7: 20+ clients plus retainers pushing past $35K The $300 monthly fee is the engine. 70 clients on retainer is $21,000 that lands whether he works or not. What keeps everyone else stuck at 21: They wait to feel ready. He shipped client 1 before he felt ready. Claude covered the gaps. His friends are sending out résumés. He’s deciding which clients to fire. The job he was “looking for” pays a quarter of this. If this was useful - follow.

A 21-year-old clears $35K a month from his bedroom. One laptop. Claude does 90% of the work he charges for. His parents still think he’s “looking for a job.” He stopped looking 8 months ago. Part 1 - Sell automations, not hours. Businesses drown in manual work - invoicing, lead follow-ups, booking reminders. He builds the system that kills the busywork. They pay $2,500 upfront plus $300/mo. Claude writes every workflow. Part 2 - Claude finds the buyers. Point it at a niche - real estate, clinics, contractors. It pulls 200 businesses still running on sticky notes and spreadsheets. The lead list builds itself by morning. Part 3 - One blueprint, every client. Same automation skeleton each time. Claude reads their process, rewires it, swaps the branding. 20 minutes per build instead of a week. Part 4 - The real ramp. Month 1: 3 clients, $7,500. Month 3: 9 clients, $19,000. Month 5: 15 clients, $30,000. Month 7: 20+ clients plus retainers pushing past $35K The $300 monthly fee is the engine. 70 clients on retainer is $21,000 that lands whether he works or not. What keeps everyone else stuck at 21: They wait to feel ready. He shipped client 1 before he felt ready. Claude covered the gaps. His friends are sending out résumés. He’s deciding which clients to fire. The job he was “looking for” pays a quarter of this. If this was useful - follow.

RGK

35,209 просмотров • 2 месяцев назад

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure

CHINESE AI TEACHER STACKED 4 NVIDIA DGX SPARK CHIPS INTO A CLUSTER - AND NOW RUNS A BUSINESS ON IT FROM HIS DESK 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else he's running a Qwen 32 35B model with 4 concurrent requests at the same time and the speed barely drops compared to a single request what used to require a $10,000+/month cloud setup now sits on his desk and costs him $40-60/month in electricity 4 chips at $2,999 each - one time investment - and now he has a cluster that handles client inference requests around the clock while he does something else the people still paying per token are financing someone else's infrastructure

Sprytix

51,132 просмотров • 3 месяцев назад

$🚨 I DON'T KNOW WHAT PEOPLE ARE WAITING FOR! This guy just deployed a full GPU compute farm in a data center Not renting cloud instances, not paying per-token, just racks of consumer GPUs on warehouse shelving doing AI inference 24/7 The setup is wild: open-frame rigs with rows of white fans, PCIe riser cables, server-grade PSUs, and a PDU for power distribution. All mounted on red industrial shelving with raised-floor cooling underneath. It looks like a crypto mining op, because it probably was one Here's what's actually happening: > Ex-mining rigs are being redeployed for AI inference at scale > Each rig runs 6-8 GPUs on PCIe risers connected to a single motherboard > Fans pull cold air from the raised floor through the open frames > A PDU on each rack handles power distribution across all rigs > The whole thing runs in a colocation facility with cheap power rates The economics make sense: cloud GPU pricing runs $2-4/hr per card. Own the hardware, colocate it for $0.08-0.12/kWh, and you're printing compute at a fraction of the cost The crypto-to-AI pipeline is real. Same hardware, same facilities, same power contracts, completely different revenue model Bookmark this!$

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cristal

16,305 просмотров • 2 месяцев назад

THIS DEVELOPER BOUGHT 4 MAC MINIS FOR OPENCLAW - AND GOT 4 MILLION VIEWS LAST MONTH WITHOUT TOUCHING THE EDITOR 00:39 he says it straight - OpenClaw does the scripting, voiceover, editing, researches what will go viral - he just looks at the result 4 Mac Minis, each running its own agent, each handling a separate task - one on video production, others on agency automation Jump Desktop lets him control all four from his MacBook Pro wherever he is - dummy HDMI adapters so the machines never turn off instead of VAs on Upwork who sleep, eat and sometimes don't get it right - four machines running 24/7 that do everything correctly the first time Claude Sonnet for complex tasks, Haiku or Gemini Flash for routine - and API costs stay under control 4 million views, zero video editors, four Mac Minis under the desk did all the work for him

THIS DEVELOPER BOUGHT 4 MAC MINIS FOR OPENCLAW - AND GOT 4 MILLION VIEWS LAST MONTH WITHOUT TOUCHING THE EDITOR 00:39 he says it straight - OpenClaw does the scripting, voiceover, editing, researches what will go viral - he just looks at the result 4 Mac Minis, each running its own agent, each handling a separate task - one on video production, others on agency automation Jump Desktop lets him control all four from his MacBook Pro wherever he is - dummy HDMI adapters so the machines never turn off instead of VAs on Upwork who sleep, eat and sometimes don't get it right - four machines running 24/7 that do everything correctly the first time Claude Sonnet for complex tasks, Haiku or Gemini Flash for routine - and API costs stay under control 4 million views, zero video editors, four Mac Minis under the desk did all the work for him

Noisy

29,062 просмотров • 2 месяцев назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,234 просмотров • 1 год назад

APPLE BUILT A $10,000 COMPUTER THAT RUNS WHAT USED TO REQUIRE AN ENTIRE SERVER ROOM 06:40 he shows the number that breaks everything - one Mac Studio is more powerful than four Dell or AMD machines combined and uses half the energy DeepSeek R1 doesn't run on even two competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room

APPLE BUILT A $10,000 COMPUTER THAT RUNS WHAT USED TO REQUIRE AN ENTIRE SERVER ROOM 06:40 he shows the number that breaks everything - one Mac Studio is more powerful than four Dell or AMD machines combined and uses half the energy DeepSeek R1 doesn't run on even two competitors - you need four - one Mac Studio does it alone runs quieter than a fan in the room and uses less energy than a kettle in the kitchen four Mac Studios on a desk connected through a regular cable run a 1 trillion parameter model - what used to require an entire data center anyone can put this at home and Jeff Geerling proved it - run the same models that big companies use without cloud, without subscriptions, without data leaving the room

Noisy

26,305 просмотров • 2 месяцев назад

You can’t make this up - META built their massive data center in Georgia - They ignored a residents complaint of brown water for 7 years - After it went viral, META sent out a representative. But it was all for show The resident says after META left they came to the determination that their data center had nothing to do with her water situation…. That started right when they built their data center…. Timeline of events - Meta broke ground on a $750 million+ data center - Shortly after construction began (including land clearing and blasting), their private well water started having severe problems: low pressure, heavy sediment and mud and discoloration - They’ve had to replace multiple appliances including 2 washing machines, dishwasher, hot water heater due to sediment buildup clogging pipes and equipment They now rely on bottled water for drinking and cooking. They have to use store water for basic needs like flushing toilets It’s META’s fault

You can’t make this up - META built their massive data center in Georgia - They ignored a residents complaint of brown water for 7 years - After it went viral, META sent out a representative. But it was all for show The resident says after META left they came to the determination that their data center had nothing to do with her water situation…. That started right when they built their data center…. Timeline of events - Meta broke ground on a $750 million+ data center - Shortly after construction began (including land clearing and blasting), their private well water started having severe problems: low pressure, heavy sediment and mud and discoloration - They’ve had to replace multiple appliances including 2 washing machines, dishwasher, hot water heater due to sediment buildup clogging pipes and equipment They now rely on bottled water for drinking and cooking. They have to use store water for basic needs like flushing toilets It’s META’s fault

Wall Street Apes

57,286 просмотров • 3 месяцев назад

OpenAI’s “The Deployment Company” is stealing clients’ IP and selling it to their competitors. This used to be a conspiracy theory. It’s not anymore. Watch this clip of OpenAI’s Head of Forward Deployed Engineering (speaking at South Park Commons). He says it directly: FDE service revenue doesn’t matter to OpenAI. Only Recurring Revenue from product does. To turn FDE engagement into product, OpenAI needs three things: 1. Subject matter expertise 2. Context to train on 3. Engineering And clients are paying OpenAI for them to get all three. OpenAI’s FDE clients don’t matter to them. They’re just a data source. Indie FDE agencies, on the other hand, obsess over clients because that is their business model. There isn’t a backend offer. Indie FDE agencies make money from happy clients. The Deployment Company makes money when shoplift clients’ data. Incentives matter. Hire Indie FDEs.

OpenAI’s “The Deployment Company” is stealing clients’ IP and selling it to their competitors. This used to be a conspiracy theory. It’s not anymore. Watch this clip of OpenAI’s Head of Forward Deployed Engineering (speaking at South Park Commons). He says it directly: FDE service revenue doesn’t matter to OpenAI. Only Recurring Revenue from product does. To turn FDE engagement into product, OpenAI needs three things: 1. Subject matter expertise 2. Context to train on 3. Engineering And clients are paying OpenAI for them to get all three. OpenAI’s FDE clients don’t matter to them. They’re just a data source. Indie FDE agencies, on the other hand, obsess over clients because that is their business model. There isn’t a backend offer. Indie FDE agencies make money from happy clients. The Deployment Company makes money when shoplift clients’ data. Incentives matter. Hire Indie FDEs.

Mike Fishbein

57,459 просмотров • 3 месяцев назад

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

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

Noisy

79,665 просмотров • 2 месяцев назад

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 didn’t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clients’ financial records would never leave a machine the firm could physically point to that promise closed the deal here’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 they’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 firm’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: → managing partner came down for an in-person demo → asked the only question that mattered: where does the data live → he pointed at the rig and said “right here, nowhere else” → they signed a monthly deal before leaving the basement the economics underneath: → 8 used 3090s: ~$5,600, paid off in the first two months → electricity: a few hundred a month under load → what the firm pays: enough to clear his mortgage every month → 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

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 didn’t care that it looked like a crypto leftover they cared about one promise no cloud provider could make them - that their clients’ financial records would never leave a machine the firm could physically point to that promise closed the deal here’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 they’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 firm’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: → managing partner came down for an in-person demo → asked the only question that mattered: where does the data live → he pointed at the rig and said “right here, nowhere else” → they signed a monthly deal before leaving the basement the economics underneath: → 8 used 3090s: ~$5,600, paid off in the first two months → electricity: a few hundred a month under load → what the firm pays: enough to clear his mortgage every month → 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 просмотров • 2 месяцев назад

27-year-old guy in Dublin made $34,800 last month with one AI UGC girl who doesn’t exist. No real model. No film crew. No face of his own on camera. He created her entirely with AI and now delivers 50–60 ad videos to brands every week. One digital girl replaces an entire studio. First month: $2,100. He kept going. After five months clients started messaging him first. Last month: $34,800. Agencies are still charging $8,000–10,000 for a single shooting day. He runs everything from a laptop.

27-year-old guy in Dublin made $34,800 last month with one AI UGC girl who doesn’t exist. No real model. No film crew. No face of his own on camera. He created her entirely with AI and now delivers 50–60 ad videos to brands every week. One digital girl replaces an entire studio. First month: $2,100. He kept going. After five months clients started messaging him first. Last month: $34,800. Agencies are still charging $8,000–10,000 for a single shooting day. He runs everything from a laptop.

Novaryn

22,337 просмотров • 29 дней назад

25-year-old guy in Vienna made $31,900 last month with one AI UGC girl who doesn’t exist. No real model. No film crew. No face of his own on camera. He created her entirely with AI and now delivers 45–55 ad videos to brands every week. One digital girl replaces an entire studio. First month: $1,720. He kept going. After five months clients started messaging him first. Last month: $31,900. Agencies are still charging $7,500–9,500 for a single shooting day. He runs everything from a laptop.

25-year-old guy in Vienna made $31,900 last month with one AI UGC girl who doesn’t exist. No real model. No film crew. No face of his own on camera. He created her entirely with AI and now delivers 45–55 ad videos to brands every week. One digital girl replaces an entire studio. First month: $1,720. He kept going. After five months clients started messaging him first. Last month: $31,900. Agencies are still charging $7,500–9,500 for a single shooting day. He runs everything from a laptop.

Novaryn

73,373 просмотров • 1 месяц назад

Here it is: The single most shocking testimonies of the November 3, 2020 election: The Commonwealth of Pennsylvania. An Obama insider wishing for Christmas Trump in handcuffs was in charge of Dominion voting machines during the 2020 election. Jim Savage held up in his hand a fairly large vDrive key and said; You can overthrow the United States government with this. Leah Hoopes: “On Nov. 5th, I observed Savage enter the counting center… and that’s when Jim Savage was plugging vDrives into the computer.” GregoryStenstrom: “He walked in three days after the election, and he took a bunch of USB sticks and plugged them into the voting machines. I watched him. I testified to it. It’s just clear. It flipped the vote by 50,000 votes.” Whistleblower: “Jim Savage held up in his hand a fairly large vDrive key, and he said this is extremely important. This controls all of the votes. This controls how the machine runs. You can change an election with this.” 📝 The 2020 election was not an election. It was a treasonous conspiracy.

Here it is: The single most shocking testimonies of the November 3, 2020 election: The Commonwealth of Pennsylvania. An Obama insider wishing for Christmas Trump in handcuffs was in charge of Dominion voting machines during the 2020 election. Jim Savage held up in his hand a fairly large vDrive key and said; You can overthrow the United States government with this. Leah Hoopes: “On Nov. 5th, I observed Savage enter the counting center… and that’s when Jim Savage was plugging vDrives into the computer.” GregoryStenstrom: “He walked in three days after the election, and he took a bunch of USB sticks and plugged them into the voting machines. I watched him. I testified to it. It’s just clear. It flipped the vote by 50,000 votes.” Whistleblower: “Jim Savage held up in his hand a fairly large vDrive key, and he said this is extremely important. This controls all of the votes. This controls how the machine runs. You can change an election with this.” 📝 The 2020 election was not an election. It was a treasonous conspiracy.

🇺🇸RealRobert🇺🇸

174,886 просмотров • 8 месяцев назад

This guy installed a personal Nvidia AI data center on his desk and now runs 7 agents 24/7 for $0/month DGX Spark on a desk = $0 per token, forever. The 7 agents now running on it: → Morning brief agent - reads every source, drops 400 words at 6am → Coding agent - runs 24/7 against the private repo, code never leaves the machine → Document Q&A on NDA files he'd never paste into a public model → Hourly competitive intel sweep across 30+ sources → Weekly 70B fine-tune on his own dataset → Always-on local voice assistant → Sunday archive re-processor that re-runs the entire knowledge base under a new prompt Each line above was a $200-500/month API charge. All of it runs on $3,248 of owned hardware now. The numbers: → Tier 1 ($249) - every "small enough for 8GB" task → Tier 2 ($2,999) - 70B at full BF16, 405B at Q3, fine-tuning → $3,248 one-time, full stack → 6 weeks to payback → $0/month forever after The unlock isn't the savings. It's that he stopped rationing. Cloud pricing teaches you to count tokens. Owning the box teaches you to ship the agent you've been "considering" for 6 months. Full breakdown 👇

This guy installed a personal Nvidia AI data center on his desk and now runs 7 agents 24/7 for $0/month DGX Spark on a desk = $0 per token, forever. The 7 agents now running on it: → Morning brief agent - reads every source, drops 400 words at 6am → Coding agent - runs 24/7 against the private repo, code never leaves the machine → Document Q&A on NDA files he'd never paste into a public model → Hourly competitive intel sweep across 30+ sources → Weekly 70B fine-tune on his own dataset → Always-on local voice assistant → Sunday archive re-processor that re-runs the entire knowledge base under a new prompt Each line above was a $200-500/month API charge. All of it runs on $3,248 of owned hardware now. The numbers: → Tier 1 ($249) - every "small enough for 8GB" task → Tier 2 ($2,999) - 70B at full BF16, 405B at Q3, fine-tuning → $3,248 one-time, full stack → 6 weeks to payback → $0/month forever after The unlock isn't the savings. It's that he stopped rationing. Cloud pricing teaches you to count tokens. Owning the box teaches you to ship the agent you've been "considering" for 6 months. Full breakdown 👇

ZEUS⚡️

37,458 просмотров • 3 месяцев назад

Phil Waldron, ex-military data expert, found Frankfurt Server & Dominion-Smartmatic Network tied to the 2020 election with election data sent to foreign servers through internet connected machines. U.S. election data packets from multiple states were routed to a SCYTL server in Frankfurt tied to Dominion/Smartmatic. Traffic surged massively on Nov 3-4, then the server went dark on Nov 9. "We have the Frankfurt traffic and data packets," Waldron said, backed by a German analyst tracking the flow. Logs also show massive traffic coming and going from U.S. 2020 election precincts to foreign countries including China, Iran, and Serbia. After the election Dominion sent reps all across the country to wipe machines of all logs and data to cover up foreign ties and election interference. Tina Peters is one of the only people who preserved data before they erased it and why she was thrown in prison. Leaked Panama docs reveal SGO, Smartmatic, and Dominion share the same tiny Barbados HQ. Dominion's code traces back to Smartmatic, licensed to both Dominion and ES&S, same vulnerable DNA. Plus, a mysterious $400M transaction to Dominion just before the election.

Phil Waldron, ex-military data expert, found Frankfurt Server & Dominion-Smartmatic Network tied to the 2020 election with election data sent to foreign servers through internet connected machines. U.S. election data packets from multiple states were routed to a SCYTL server in Frankfurt tied to Dominion/Smartmatic. Traffic surged massively on Nov 3-4, then the server went dark on Nov 9. "We have the Frankfurt traffic and data packets," Waldron said, backed by a German analyst tracking the flow. Logs also show massive traffic coming and going from U.S. 2020 election precincts to foreign countries including China, Iran, and Serbia. After the election Dominion sent reps all across the country to wipe machines of all logs and data to cover up foreign ties and election interference. Tina Peters is one of the only people who preserved data before they erased it and why she was thrown in prison. Leaked Panama docs reveal SGO, Smartmatic, and Dominion share the same tiny Barbados HQ. Dominion's code traces back to Smartmatic, licensed to both Dominion and ES&S, same vulnerable DNA. Plus, a mysterious $400M transaction to Dominion just before the election.

The SCIF

24,108 просмотров • 5 месяцев назад