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THIS GUY BOUGHT A $2,400 NVIDIA BOX AND SAVED $18,700/YEAR ON CLOUD GPUS WITHOUT RENTING SERVERS AGAIN the entire setup runs on one rule - stop paying every time you want to test something most people run 20 small AI experiments in the cloud and think it’s cheap because...

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Gipp 🦅

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12,484 views • 27 days ago •via X (Twitter)

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A Guy paying $3,200/month to run AI agents in the cloud. Then he stacked 4 Mac Minis on his desk. Cost: • $2,396 one time • $12/month electricity Result: • Cloud bill: $3,200 → $0 • Faster response times • Private data stays local • $38,000+ saved in year one The craziest part? Once AI runs on your desk, every experiment becomes free. The cloud isn't always cheaper.
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A Guy paying $3,200/month to run AI agents in the cloud. Then he stacked 4 Mac Minis on his desk. Cost: • $2,396 one time • $12/month electricity Result: • Cloud bill: $3,200 → $0 • Faster response times • Private data stays local • $38,000+ saved in year one The craziest part? Once AI runs on your desk, every experiment becomes free. The cloud isn't always cheaper.

Radha Tripathi

40,773 views • 27 days ago

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.
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This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

Gipp 🦅

590,100 views • 1 month ago

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
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JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

54,309 views • 28 days ago

THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.
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THIS GUY SAW A $430 AI BILL AND BUILT HIS OWN AI LAB UNDER HIS DESK INSTEAD RTX 5090 + RTX 4090, 56GB VRAM, 128GB RAM, Proxmox and local Qwen / DeepSeek / Llama models running without API keys while everyone else is still paying every time they test a prompt. The best part of the setup: api_key: “not-needed”. His agents can scan GitHub, Reddit and RSS feeds, read notes, test ideas overnight and break without turning into another invoice. If something fails, he fixes the config, not the credit card limit. Most people rent AI by the token. He is turning a desk setup into a private machine that works even when the dashboard is closed.

Gipp 🦅

69,445 views • 1 month ago

A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.
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A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.

Gipp 🦅

21,269 views • 2 months ago

California resident shows it cost $608 to register her 5 year old car in California Paying the government to register your car every year is theft Imagine if you bought a laptop one year and then had to pay the government every year to use it. It’s the same thing. It’s a scam
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California resident shows it cost $608 to register her 5 year old car in California Paying the government to register your car every year is theft Imagine if you bought a laptop one year and then had to pay the government every year to use it. It’s the same thing. It’s a scam

Wall Street Apes

2,665,351 views • 1 year ago

NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.
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NVIDIA just made AI detect objects 10x faster by deleting one step. It's called LocateAnything, and it removes the biggest bottleneck no one else was fixing in vision-language models. Normally a model builds each bounding box one coordinate token at a time. 100 objects means thousands of tokens before an answer. NVIDIA scrapped that: their Parallel Box Decoding predicts the whole box in a single forward pass, as one atomic unit. → 12.7 boxes/sec on one H100 → 10x faster than Qwen3-VL → +3.8% F1 on LVIS, accuracy up, not down → 3B params, runs on one consumer GPU Treating the box as one unit keeps its coordinates tied together, which is why accuracy climbed instead of falling. One model handles detection, GUI grounding, OCR, and document understanding, ready for computer-use agents, robotics, and document pipelines. 100% open source, weights, code, demo, and paper all live.

Alvaro Cintas

191,943 views • 2 days ago

the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below
0:15

the thing you rent for $200 a month just became something you can own for $1,700 once but the money is not even the real story for the first time a 200 billion parameter model is not in a datacenter, it is sitting on a desk the cloud spent years convincing you a model this size needed their servers, their meter, their monthly bill people are stacking four subscriptions into a $440 a month bill to rent what one box this size now owns outright it needed a box the size of a book the moment the model moves from their datacenter to your desk, the whole game changes it stops being about who has the best AI it becomes about who ships it on every desk the cloud told you this needed a datacenter it needed a desk i did the full math on what this kills in the article below

John Doe

24,757 views • 14 days ago

A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.
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A CHINESE GUY PUT 4 MINISFORUM MS-S1 MAX MINI PCs IN HIS BEDROOM AND TURNED THEM INTO A 24/7 AI AGENT CLUSTER. TOTAL POWER BILL: ABOUT $44/MO. each box is a tiny local AI workstation built around the Ryzen AI Max+ 395. around $3,000 per unit gets him 128GB of unified memory, 2TB storage, dual 10GbE, and up to roughly 96GB usable as VRAM on Linux. one MS-S1 Max can already run serious open models without touching the cloud. Qwen3-Coder 30B for fast coding, Llama 3.3 70B for heavier reasoning, and larger research models overnight when speed matters less than free inference. four boxes in one room changes the whole game. he is not opening a chatbot, paying for every loop, or shutting agents down before sleep. this is private infrastructure that keeps working even when he is offline. the agents can sort inboxes, review code, summarize documents, monitor feeds, prep meetings, and read papers overnight. on cloud APIs, that kind of always-on stack can easily burn $800 to $1,200 a month if used aggressively. his setup is roughly a $12,000 hardware spend, but the monthly cost is basically electricity. a rack, a switch, a NAS, a small monitor, and four tiny MS-S1 Max boxes turning a bedroom corner into a private inference factory. this is what AI looks like when it stops being rented and starts becoming something you own.

Gipp 🦅

24,836 views • 9 days ago

i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running
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i spent $26,600 on cloud GPU rentals over 14 months before i found a NVIDIA DGX Spark at $2,999 (founder's edition) or $3,999 (shipping price) it paid for itself in 6 weeks i run 200B parameter models locally now and my old cloud provider keeps sending me loyalty discount emails the math on that $26,600 is embarrassing to type out loud $1,900/month for 14 months, H100 instances on a specialist cloud provider, because anything bigger than a 70B model simply would not fit anywhere else i paid the invoices like they were a utility bill and told myself it was just the cost of doing serious AI work it took me over a year to find out it wasn't 14 months, broken down: → months 1-4: $1,400-1,600/month - felt like manageable infrastructure overhead → months 5-9: crept to $1,900-2,100 as i started running DeepSeek-class experiments, costs tracking directly with model size → months 10-12: one agent loop ran for 36 hours against a 130B model while i slept, that month hit $2,400 → month 13: ran the cumulative total for the first time, saw $23,800, felt physically sick → month 14: another $2,800 month while i waited for the hardware to ship the box is the NVIDIA DGX Spark - roughly the footprint of a large mac mini, powered by a GB10 Grace Blackwell chip with 128GB of unified LPDDR5X memory that unified memory is the whole thing an RTX 4090 has 24GB of VRAM, which means a 70B model in full BF16 precision physically does not fit, you're quantizing down or you're renting cloud, those are your options this box loads a 200B parameter model quantized and serves it through vLLM over localhost, same API interface the cloud endpoint used the migration took one line of code - i changed the base URL from the provider's endpoint to 127.0.0.1:8000 and everything just worked electricity to run continuous 200B inference locally comes out to about $12/month the payback arithmetic is almost too clean: $2,999 hardware cost against $1,900/month saved, the box paid for itself before i'd owned it two months what i didn't account for was how completely the cost model changes your behavior when there's no hourly meter running, you greenlight experiments you'd never approve on cloud - agent loops that churn for hours, running 10,000 documents through a reasoning pass at 3am, speculative fine-tuning jobs you'd normally skip because the cost felt unjustifiable i ran more experiments in the first 30 days after the box arrived than in the four months before it the loyalty discount email landed about 8 weeks after i cancelled the cloud subscription 15% off my next three months, valued customer, we'd love to have you back i didn't reply the box was already running

Argona

22,099 views • 28 days ago

One thing we can hold on to in a rapidly changing world is that Christ is the same yesterday, today, and tomorrow. He is unchanging and He will never stop loving us.
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One thing we can hold on to in a rapidly changing world is that Christ is the same yesterday, today, and tomorrow. He is unchanging and He will never stop loving us.

Renatta

10,503 views • 6 months ago

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk
0:17

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

beamnxw ./

30,361 views • 28 days ago

A guy was paying $200/mo for Claude Max. His subscription burned through in 3 hours of work. He bought a base Mac Mini for $599. Installed 5 local models on it. One command. One flag. His office neighbors thought he was mining crypto. He just taught the machine to sort messages, compress context, and keep the system alive while he sleeps. At 4am Claude hit its rate limit. The local model picked up. In the morning he read the logs - everything worked. He didn't even wake up. A team doing the same thing - that's 3 engineers and $15,000/mo on API costs. He paid $599 once. 35 billion parameters on 16 gigs of memory. Everyone said impossible. One flag in one command proved them all wrong. And people like him - there's only a handful so far.
0:16

A guy was paying $200/mo for Claude Max. His subscription burned through in 3 hours of work. He bought a base Mac Mini for $599. Installed 5 local models on it. One command. One flag. His office neighbors thought he was mining crypto. He just taught the machine to sort messages, compress context, and keep the system alive while he sleeps. At 4am Claude hit its rate limit. The local model picked up. In the morning he read the logs - everything worked. He didn't even wake up. A team doing the same thing - that's 3 engineers and $15,000/mo on API costs. He paid $599 once. 35 billion parameters on 16 gigs of memory. Everyone said impossible. One flag in one command proved them all wrong. And people like him - there's only a handful so far.

Medvid

7,636,558 views • 2 months ago

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one
0:10

A CHINESE GUY STOPPED PAYING WEB DESIGNERS $1,800 PER LANDING PAGE AND BUILT THE SAME KIND OF SITE WITH CLAUDE CODE IN ONE AFTERNOON FOR LESS THAN $70 claude was not just writing code. it was doing the job of a designer, copywriter and frontend dev in one window. in 4 hours, one rough idea turned into the layout, color system, font choices, page sections and the full html/css/js build the real trick was the references. he gave claude 5 screenshots, made it ask 7 questions first, then pushed it toward one clear visual direction instead of accepting another generic ai-looking template the first version already looked solid. the second pass made it feel expensive: better typography, a darker palette, mobile cleanup, cursor effects, 6 micro-interactions and custom hero visuals his old workflow was burning $1,200 on design, $500 on frontend work and another $150 on small fixes every time he needed a new page. now the whole test costs less than $70 and the site still looks like something a $5,000 agency would ship the edge is not “ai builds websites.” the edge is that one person can now brief, critique, polish and launch in one afternoon without waiting 10 days for a designer to send version one

Gipp 🦅

293,049 views • 25 days ago

And, the way you always keep the most important person for last, the fact that the apogee of his acceptance speech was about Connor.. he had me sobbing, cause of how completely unguarded and raw Hudson is here. The way the emotion is slowly building up, as he is going down that list, his voice cracking a bit, his hands shaking. And he can feel it slowly, tears forming in his eyes, tears he is holding in the best he can. As he is holding that award, so firmly, that last name on his list, the one that felt like an awakening. He is the one he'd want to hold right there (or rather be held 🥺). The one who is more than a scene partner, more than a best friend, his "emotional support person in life", his "soulmate", his anchor even. Cracking a joke or two to try to release a bit of pressure, but when comes the point of saying his name "to the honorary Canadian Connor Storrie, I share this award with you." and not find his face, his eyes, his smile in front of him, the absence is screaming in his chest and the tears are closer than ever. Cause to be here, and for that to happen not side by side, like everything they've experienced for the past year or so, this doesn't feel quite right. "A single person is missing for you, and the whole world is empty" So yes he got through that speech without crying (barely), but not without beautifully opening his heart up to us before, and showing just how much he loves him 💓
0:18

And, the way you always keep the most important person for last, the fact that the apogee of his acceptance speech was about Connor.. he had me sobbing, cause of how completely unguarded and raw Hudson is here. The way the emotion is slowly building up, as he is going down that list, his voice cracking a bit, his hands shaking. And he can feel it slowly, tears forming in his eyes, tears he is holding in the best he can. As he is holding that award, so firmly, that last name on his list, the one that felt like an awakening. He is the one he'd want to hold right there (or rather be held 🥺). The one who is more than a scene partner, more than a best friend, his "emotional support person in life", his "soulmate", his anchor even. Cracking a joke or two to try to release a bit of pressure, but when comes the point of saying his name "to the honorary Canadian Connor Storrie, I share this award with you." and not find his face, his eyes, his smile in front of him, the absence is screaming in his chest and the tears are closer than ever. Cause to be here, and for that to happen not side by side, like everything they've experienced for the past year or so, this doesn't feel quite right. "A single person is missing for you, and the whole world is empty" So yes he got through that speech without crying (barely), but not without beautifully opening his heart up to us before, and showing just how much he loves him 💓

Sab✨💚

18,552 views • 28 days ago

I see moments like this one and all I can think about is how much jongin how to suddenly give up with his quick enlistment. And now look at him, he got to tour as a soloist and got to finally promote with his group, all in the same year 🥹
0:14

I see moments like this one and all I can think about is how much jongin how to suddenly give up with his quick enlistment. And now look at him, he got to tour as a soloist and got to finally promote with his group, all in the same year 🥹

ami

57,020 views • 6 months ago

HIGHLIGHT OF YEAR: This Guy was removed from a WhatsApp group, he went and got a court order to be added back. They added him back and made him administrator then one by one left the group and created a new one. He is now heading back to court to get the members back on his WhatsApp group like the beginning.
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HIGHLIGHT OF YEAR: This Guy was removed from a WhatsApp group, he went and got a court order to be added back. They added him back and made him administrator then one by one left the group and created a new one. He is now heading back to court to get the members back on his WhatsApp group like the beginning.

Mzilikazi wa Afrika

1,558,812 views • 2 years ago

A regular American developer bought $1,400 worth and stacked seven Mac Minis on top of each other and connected them with metal cables. Neighbors thought he was building a mining server. His wife thought he'd lost his mind. He just didn't want to pay $15,000 a month for a dev team. On the screen - a diagram. Seven Mac Minis connected via Ethernet working as one machine. EXO framework distributes tasks between them automatically. 11.44 TFLOPS each. Together - more than most cloud servers that companies pay thousands for every month. He paid $1,400 for the hardware once. 38 agents from GitHub, 156 skills. A system that learns from session to session and in two weeks writes code just like he does - but seven times faster because it runs on seven machines in parallel. A task that took a junior dev 10-12 hours - the tower closes in 20 minutes. One founder with this setup ships a product like a team of eight people. For $20 a month instead of $120,000 a year. This 7 Mac Mini setup helped him win the Anthropic hackathon and make $26,000 without a team.
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A regular American developer bought $1,400 worth and stacked seven Mac Minis on top of each other and connected them with metal cables. Neighbors thought he was building a mining server. His wife thought he'd lost his mind. He just didn't want to pay $15,000 a month for a dev team. On the screen - a diagram. Seven Mac Minis connected via Ethernet working as one machine. EXO framework distributes tasks between them automatically. 11.44 TFLOPS each. Together - more than most cloud servers that companies pay thousands for every month. He paid $1,400 for the hardware once. 38 agents from GitHub, 156 skills. A system that learns from session to session and in two weeks writes code just like he does - but seven times faster because it runs on seven machines in parallel. A task that took a junior dev 10-12 hours - the tower closes in 20 minutes. One founder with this setup ships a product like a team of eight people. For $20 a month instead of $120,000 a year. This 7 Mac Mini setup helped him win the Anthropic hackathon and make $26,000 without a team.

Noisy

2,029,394 views • 2 months ago

UP: Boyfriend Ram Singh Parihar killed girlfriend Preeti in Jhansi. The body was hidden in the room for a week. Then he ordered a blue iron box and some wood. On the night of January 16, the body was burnt and the bones and ashes were placed in that box. The next night, Ram Singh booked a taxi. He put the box in the taxi and started walking behind the bike himself. Ram Singh went missing. The taxi driver got suspicious. He opened the box and found bones. Police have arrested the accused.
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UP: Boyfriend Ram Singh Parihar killed girlfriend Preeti in Jhansi. The body was hidden in the room for a week. Then he ordered a blue iron box and some wood. On the night of January 16, the body was burnt and the bones and ashes were placed in that box. The next night, Ram Singh booked a taxi. He put the box in the taxi and started walking behind the bike himself. Ram Singh went missing. The taxi driver got suspicious. He opened the box and found bones. Police have arrested the accused.

Krishna Chaudhary

440,672 views • 5 months ago

Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.
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Most people think AI data centers are giant buildings in the desert. One guy installed four mini Nvidia AI data centers right behind his work desk and now they pay him every month. Each unit is about the size of a small fridge. Inside: Nvidia GPUs running AI workloads 24/7. He hooked them up next to his AC system and that was basically it. Now the company pays him a flat monthly fee for the electricity and Wi-Fi they use. According to him, it brings in around $10,000/month straight into his account. The crazy part: the units also cool part of the house, cutting his AC bill by roughly $600. That’s more than $120,000 a year from four AI boxes sitting inside his home office. His mortgage is basically being paid by AI hardware behind his chair. Quietly, regular homes are starting to become AI infrastructure. Save this post. You’re watching the next gold rush move into people’s homes.

Shelpid.WI3M

974,778 views • 1 month ago