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Acasis 8-in-1 40Gbps dock turns a Mac Mini into a 2,910 MB/s workstation with one 4TB SSD slot. Hands open the silver case. A Samsung 990 Pro 4TB drops into the single M.2 bay. Lid screws down. The Mac Mini slides in from the side. Two USB4 ports. Three...

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HodlReaper

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16,198 görüntüleme • 15 gün önce •via X (Twitter)

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Acasis 8-in-1 40Gbps dock turns a Mac Mini into a 2,765 MB/s workstation with one 2TB SSD slot. Hands open the silver case. A Samsung 990 Pro 2TB drops into the single M.2 bay. Lid screws down. The Mac Mini slides in from the side. Two USB4 ports. Three 10Gbps USB-A. HDMI. DisplayPort. SD 4.0. TF 4.0. Power Delivery. Blackmagic Disk Speed Test hits 2,765 MB/s write and 2,812 MB/s read. Every ProRes and H.265 checkbox turns green. Card slots appear as separate drives. 2.45 GB copies in about 4 seconds. Infrared thermometer reads 27.3°C under load. The dock sits on the desk with three monitors running and the original Mac Mini case still closed.
0:25

Acasis 8-in-1 40Gbps dock turns a Mac Mini into a 2,765 MB/s workstation with one 2TB SSD slot. Hands open the silver case. A Samsung 990 Pro 2TB drops into the single M.2 bay. Lid screws down. The Mac Mini slides in from the side. Two USB4 ports. Three 10Gbps USB-A. HDMI. DisplayPort. SD 4.0. TF 4.0. Power Delivery. Blackmagic Disk Speed Test hits 2,765 MB/s write and 2,812 MB/s read. Every ProRes and H.265 checkbox turns green. Card slots appear as separate drives. 2.45 GB copies in about 4 seconds. Infrared thermometer reads 27.3°C under load. The dock sits on the desk with three monitors running and the original Mac Mini case still closed.

HodlReaper

13,074 görüntüleme • 14 gün önce

APPLE CHARGES $200 TO GO FROM 256GB TO 512GB ON THE MAC MINI. $400 FOR 1TB. $600 FOR 2TB. AND YOU HAVE TO DECIDE BEFORE YOU BUY. Pick wrong and you're stuck forever. Apple soldered the storage shut. That's their entire business model. Sell the box cheap, charge a premium for every upgrade you can't do yourself. One dock just broke the whole system. The Acasis 8-in-1 plugs into the Mac Mini through a single Thunderbolt cable. Slide any NVMe SSD into the top. 2,819 MB/s write, 2,857 MB/s read. Faster than Apple's own base storage. A 2TB NVMe SSD costs about $100 on Amazon. Apple charges $600 for the same upgrade. That's a 6x markup for storage you could add yourself in 10 seconds. But it doesn't stop at storage. SD card reader, DisplayPort, USB ports, power delivery. Every port Apple left out, this dock puts back in. Surface temp under load: 26.7°C. Same aluminum finish. Sits right next to the Mac Mini like Apple designed it. That's when it clicks. The $599 Mac Mini was never the cheap option. It was the entry fee. The real cost was every upgrade Apple locked behind the checkout page. One $150 dock and a $100 SSD just replaced $600 worth of Apple tax. Still think you need the upgraded model?
0:42

APPLE CHARGES $200 TO GO FROM 256GB TO 512GB ON THE MAC MINI. $400 FOR 1TB. $600 FOR 2TB. AND YOU HAVE TO DECIDE BEFORE YOU BUY. Pick wrong and you're stuck forever. Apple soldered the storage shut. That's their entire business model. Sell the box cheap, charge a premium for every upgrade you can't do yourself. One dock just broke the whole system. The Acasis 8-in-1 plugs into the Mac Mini through a single Thunderbolt cable. Slide any NVMe SSD into the top. 2,819 MB/s write, 2,857 MB/s read. Faster than Apple's own base storage. A 2TB NVMe SSD costs about $100 on Amazon. Apple charges $600 for the same upgrade. That's a 6x markup for storage you could add yourself in 10 seconds. But it doesn't stop at storage. SD card reader, DisplayPort, USB ports, power delivery. Every port Apple left out, this dock puts back in. Surface temp under load: 26.7°C. Same aluminum finish. Sits right next to the Mac Mini like Apple designed it. That's when it clicks. The $599 Mac Mini was never the cheap option. It was the entry fee. The real cost was every upgrade Apple locked behind the checkout page. One $150 dock and a $100 SSD just replaced $600 worth of Apple tax. Still think you need the upgraded model?

Framez

49,948 görüntüleme • 15 gün önce

STOP UPGRADING YOUR MAC MINI, YOU ARE DOING IT WRONG Apple has been robbing us blind on storage upgrades for a decade, forcing people to throw away perfectly good computers. But if you have an M1 or M2 Mac Mini, this one insane vertical dock completely breaks the matrix. Forget ugly dongles and external enclosures dangling off your desk. This heavy-duty aluminum stand by Acasis transforms your base-model Mac Mini into a miniature Mac Pro footprint. Why this is the ultimate workspace hack: Hidden 40Gbps Enclosure: Slides out from the side panel to support up to an 8TB NVMe SSD at full native speed. Front-Facing Pro IO: Packs SD/microSD 4.0, dual 10Gbps USB-A, and a 10Gbps USB-C right where you can reach them. Dual Display Loop: Adds an integrated HDMI output for seamless dual 4K/60Hz monitor setups.
14:48

STOP UPGRADING YOUR MAC MINI, YOU ARE DOING IT WRONG Apple has been robbing us blind on storage upgrades for a decade, forcing people to throw away perfectly good computers. But if you have an M1 or M2 Mac Mini, this one insane vertical dock completely breaks the matrix. Forget ugly dongles and external enclosures dangling off your desk. This heavy-duty aluminum stand by Acasis transforms your base-model Mac Mini into a miniature Mac Pro footprint. Why this is the ultimate workspace hack: Hidden 40Gbps Enclosure: Slides out from the side panel to support up to an 8TB NVMe SSD at full native speed. Front-Facing Pro IO: Packs SD/microSD 4.0, dual 10Gbps USB-A, and a 10Gbps USB-C right where you can reach them. Dual Display Loop: Adds an integrated HDMI output for seamless dual 4K/60Hz monitor setups.

Shadow Nick

15,047 görüntüleme • 1 ay önce

THE $599 MAC MINI GETS A $49 DOCK AND TURNS FROM A DESK COMPUTER INTO A TINY 24/7 AI WORKFLOW SERVER 00:19 he clips the dock onto the back of the mac mini, adds extra ports, ethernet, storage and turns one small silver box into a workflow hub that can stay online for 700+ hours a month a base mac mini can sit online all day, pulling light power while scripts watch folders, move files, process notes and keep boring workflows running in the background paired with claude, it becomes a small home automation box: 2-hour lectures get transcribed, 30-page articles get summarized, reports get drafted, and research gets cleaned before you even open the laptop the difference is the loop. trigger, process, verify, save, repeat. the same workflow that costs $40-100/month across cloud tools can run locally from one machine on your desk that is why this tiny upgrade matters. the dock is cheap, but it gives the mac mini enough expansion to act like a quiet server instead of another computer waiting for you to use it bookmark this before your desk setup realizes it became infrastructure.
0:29

THE $599 MAC MINI GETS A $49 DOCK AND TURNS FROM A DESK COMPUTER INTO A TINY 24/7 AI WORKFLOW SERVER 00:19 he clips the dock onto the back of the mac mini, adds extra ports, ethernet, storage and turns one small silver box into a workflow hub that can stay online for 700+ hours a month a base mac mini can sit online all day, pulling light power while scripts watch folders, move files, process notes and keep boring workflows running in the background paired with claude, it becomes a small home automation box: 2-hour lectures get transcribed, 30-page articles get summarized, reports get drafted, and research gets cleaned before you even open the laptop the difference is the loop. trigger, process, verify, save, repeat. the same workflow that costs $40-100/month across cloud tools can run locally from one machine on your desk that is why this tiny upgrade matters. the dock is cheap, but it gives the mac mini enough expansion to act like a quiet server instead of another computer waiting for you to use it bookmark this before your desk setup realizes it became infrastructure.

Gipp 🦅

73,740 görüntüleme • 2 ay önce

APPLE IS LYING TO YOU ABOUT THE MAC MINI, AND THIS SUB-$100 FIX PERMANENTLY UNLOCKS ITS TRUE POWER The Mac Mini is an absolute beast until you run out of ports in exactly 5 seconds and your system begins throttling to death under heavy video editing or gaming. Most users waste hundreds on messy dongles and external hubs, completely destroying their desk aesthetics. The ultimate power move is dropping a vertical, active-cooling dock right into your workstation ecosystem. Why this layout changes everything: The Thermal Shield: Integrated aluminum pads and a silent high-speed fan that completely eliminate system lag, heat build-up, and sudden freezing under load. Extreme Expansion: Instantly unlocks triple-monitor support via multiple HDMI ports, high-speed USB arrays, and a hidden SSD slot. Modular Integration: Bridges directly into the Mac's native ports using a flush, custom jump connector, keeping your desktop entirely cord-free. Stop letting Apple gatekeep your machine's peak performance.
3:22

APPLE IS LYING TO YOU ABOUT THE MAC MINI, AND THIS SUB-$100 FIX PERMANENTLY UNLOCKS ITS TRUE POWER The Mac Mini is an absolute beast until you run out of ports in exactly 5 seconds and your system begins throttling to death under heavy video editing or gaming. Most users waste hundreds on messy dongles and external hubs, completely destroying their desk aesthetics. The ultimate power move is dropping a vertical, active-cooling dock right into your workstation ecosystem. Why this layout changes everything: The Thermal Shield: Integrated aluminum pads and a silent high-speed fan that completely eliminate system lag, heat build-up, and sudden freezing under load. Extreme Expansion: Instantly unlocks triple-monitor support via multiple HDMI ports, high-speed USB arrays, and a hidden SSD slot. Modular Integration: Bridges directly into the Mac's native ports using a flush, custom jump connector, keeping your desktop entirely cord-free. Stop letting Apple gatekeep your machine's peak performance.

Ridark

86,314 görüntüleme • 1 ay önce

A Mac Mini M4 ran 3 hours 50 minutes on a pocket power bank. It idles at 1.5 watts. Light work pulls 3 to 5 watts. The same machine that once needed a wall outlet now sits on a desk as a full home server, drawing less power than a night light. An iPad becomes the wireless display. Touch works. Apple Pencil works. Then the Vision Pro takes over for a bigger screen. The tiny bank never dropped below critical. No wall plug. No UPS. Just the Mac Mini doing real work for almost 4 hours. That efficiency is why the M4 Mini is the new default for always-on home servers. Quiet. Tiny. Cheap to keep alive. The power bank still had charge left.
0:32

A Mac Mini M4 ran 3 hours 50 minutes on a pocket power bank. It idles at 1.5 watts. Light work pulls 3 to 5 watts. The same machine that once needed a wall outlet now sits on a desk as a full home server, drawing less power than a night light. An iPad becomes the wireless display. Touch works. Apple Pencil works. Then the Vision Pro takes over for a bigger screen. The tiny bank never dropped below critical. No wall plug. No UPS. Just the Mac Mini doing real work for almost 4 hours. That efficiency is why the M4 Mini is the new default for always-on home servers. Quiet. Tiny. Cheap to keep alive. The power bank still had charge left.

HodlReaper

12,887 görüntüleme • 15 gün önce

A $599 MAC MINI, A 340 YUAN SCREEN, AND $3 TO $5 OF POWER CAN TURN INTO A PRIVATE AI SERVER THAT CUTS THE DUMBEST PART OF A $150 API BILL 00:03 he shows the whole setup on the desk: tiny mac mini, cheap external display, keyboard, cables, and one silent box small enough to hide under the monitor while running local models all day. developers burn paid tokens on the wrong stuff. code formatting, quick summaries, draft emails, cron checks, log cleanup and tiny reviews can eat most of the bill, even when none of it needs a frontier model. ollama on a mac mini changes the math. qwen, llama, gemma or phi can handle the boring repeat work locally, while claude stays for architecture, production code and the problems that actually need expensive reasoning. one box can serve the whole workflow. open webui turns the mac mini into a private chat interface for your phone, laptop and tablet, with local files staying in your room instead of getting shipped to another cloud tab. monthly savings come from the invisible calls. 1,440 cron checks, 200 small summaries, 80 log reviews and dozens of throwaway prompts can move off paid tokens before they quietly become another $150 invoice. bookmark this before your next API bill looks like rent.
0:27

A $599 MAC MINI, A 340 YUAN SCREEN, AND $3 TO $5 OF POWER CAN TURN INTO A PRIVATE AI SERVER THAT CUTS THE DUMBEST PART OF A $150 API BILL 00:03 he shows the whole setup on the desk: tiny mac mini, cheap external display, keyboard, cables, and one silent box small enough to hide under the monitor while running local models all day. developers burn paid tokens on the wrong stuff. code formatting, quick summaries, draft emails, cron checks, log cleanup and tiny reviews can eat most of the bill, even when none of it needs a frontier model. ollama on a mac mini changes the math. qwen, llama, gemma or phi can handle the boring repeat work locally, while claude stays for architecture, production code and the problems that actually need expensive reasoning. one box can serve the whole workflow. open webui turns the mac mini into a private chat interface for your phone, laptop and tablet, with local files staying in your room instead of getting shipped to another cloud tab. monthly savings come from the invisible calls. 1,440 cron checks, 200 small summaries, 80 log reviews and dozens of throwaway prompts can move off paid tokens before they quietly become another $150 invoice. bookmark this before your next API bill looks like rent.

Gipp 🦅

21,848 görüntüleme • 1 ay önce

"Mac Minis for example are a very good fit" - Andrej Karpathy Andrej Karpathy shouted out my work on EXO Labs in his keynote at Y Combinator AI SUS! Here's the breakdown: Right now most AI workloads run in the cloud where requests from different users are continuously batched together. These workloads are FLOPS-bound and favors hardware with the best unit economics of $ per FLOP, i.e. enterprise GPUs. The personal computing revolution will shift these workloads to personal devices with lower batch sizes (mostly batch_size=1). batch_size=1 inference is memory-bound, because all of the model parameters need to be loaded into the GPU every time a token is generated. Apple Silicon with its Unified Memory architecture has a lot of memory and memory bandwidth per $ compared to other hardware: - M4 Pro Mac Mini, 24GB @ 273GB/s, $58.33/GB, $5.13/GB/s - H100, 80GB @ 3350GB/s, $625/GB, $14.93/GB/s The unit economics of Apple Silicon are becoming more compelling with every release of the Mac. The future of AI inference looks more like open weights models (OpenAI open weights model soonTM) run at low batch_size on personal devices.
0:20

"Mac Minis for example are a very good fit" - Andrej Karpathy Andrej Karpathy shouted out my work on EXO Labs in his keynote at Y Combinator AI SUS! Here's the breakdown: Right now most AI workloads run in the cloud where requests from different users are continuously batched together. These workloads are FLOPS-bound and favors hardware with the best unit economics of $ per FLOP, i.e. enterprise GPUs. The personal computing revolution will shift these workloads to personal devices with lower batch sizes (mostly batch_size=1). batch_size=1 inference is memory-bound, because all of the model parameters need to be loaded into the GPU every time a token is generated. Apple Silicon with its Unified Memory architecture has a lot of memory and memory bandwidth per $ compared to other hardware: - M4 Pro Mac Mini, 24GB @ 273GB/s, $58.33/GB, $5.13/GB/s - H100, 80GB @ 3350GB/s, $625/GB, $14.93/GB/s The unit economics of Apple Silicon are becoming more compelling with every release of the Mac. The future of AI inference looks more like open weights models (OpenAI open weights model soonTM) run at low batch_size on personal devices.

Alex Cheema

103,604 görüntüleme • 1 yıl önce

A 24 BAY DELL POWEREDGE T550 TOWER SERVER FITS IN A HOME OFFICE WITHOUT A RACK, 384TB OF RAW SAS STORAGE HOSTS EVERY OPEN SOURCE LLM EVER RELEASED LOCALLY, THE HOME AI LIBRARY TIER UNDER YOUR MAP 00:25 the reseller spins the chassis around, "it's a 24 bay Dell T550 tower server man, look it's got a boss card for dual NVMe boot SSDs" a refurbished T550 ships at $900-1,100 with single Xeon Silver, 64GB DDR4 ECC and 24 hot swap SAS bays in a tower form factor that fits next to a desk without a server rack 24x 16TB SAS drives at $80 each on eBay equals 384TB raw storage for $1,920, the same array on enterprise Mac Studio Pro tier would cost $35,000 in thunderbolt JBOD enclosures Llama 3.3 70B is 42GB, Qwen3-235B is 110GB, DeepSeek-V3 is 100GB, Mistral Large is 80GB, the entire ollama public model library fits inside 4TB, you can mirror every open weights release for the next decade your map covers compute boxes that swap one model at a time, the T550 is the model warehouse tier that feeds them, a homelab operator wires the tower over 10GbE to a mac mini and the mac never waits 30 seconds to pull a swap the window is open, follow and bookmark before it closes
0:53

A 24 BAY DELL POWEREDGE T550 TOWER SERVER FITS IN A HOME OFFICE WITHOUT A RACK, 384TB OF RAW SAS STORAGE HOSTS EVERY OPEN SOURCE LLM EVER RELEASED LOCALLY, THE HOME AI LIBRARY TIER UNDER YOUR MAP 00:25 the reseller spins the chassis around, "it's a 24 bay Dell T550 tower server man, look it's got a boss card for dual NVMe boot SSDs" a refurbished T550 ships at $900-1,100 with single Xeon Silver, 64GB DDR4 ECC and 24 hot swap SAS bays in a tower form factor that fits next to a desk without a server rack 24x 16TB SAS drives at $80 each on eBay equals 384TB raw storage for $1,920, the same array on enterprise Mac Studio Pro tier would cost $35,000 in thunderbolt JBOD enclosures Llama 3.3 70B is 42GB, Qwen3-235B is 110GB, DeepSeek-V3 is 100GB, Mistral Large is 80GB, the entire ollama public model library fits inside 4TB, you can mirror every open weights release for the next decade your map covers compute boxes that swap one model at a time, the T550 is the model warehouse tier that feeds them, a homelab operator wires the tower over 10GbE to a mac mini and the mac never waits 30 seconds to pull a swap the window is open, follow and bookmark before it closes

Antid

18,526 görüntüleme • 1 ay önce

THIS GUY SPENT $829 TO TURN A MAC MINI INTO A BATTERY-POWERED AI WORKER THAT CAN RUN FOR 12 HOURS ALMOST ANYWHERE 02:10 he plugs the $599 mac mini into a portable battery and pure sine wave inverter. the power kit adds roughly $230 to the build, weighs about 2 kilograms and keeps the computer running for around 10 to 12 hours depending on the workload. suddenly, the mac is no longer tied to a desk. it can process documents, download files, run scheduled scripts and organize research from a backpack, a car or any place where a normal computer would need an outlet. claude handles the reasoning, obsidian becomes the permanent memory and local whisper converts hours of audio into text without charging by the minute. python connects the stack and starts each task automatically, even when nobody is watching the screen. one 2-hour lecture can become 5 key ideas, 3 timestamped quotes and 5 review questions. 120 unread links can become connected notes, while a 45-minute meeting is turned into a clean summary before the laptop is opened. the full $829 setup can replace a $25 storage plan, $39 automation tool, $20 transcription service and a $30 vps. instead of paying up to $114 every month, the machine runs locally with only a few dollars in claude api usage. bookmark this before ai computers stop needing desks.
2:41

THIS GUY SPENT $829 TO TURN A MAC MINI INTO A BATTERY-POWERED AI WORKER THAT CAN RUN FOR 12 HOURS ALMOST ANYWHERE 02:10 he plugs the $599 mac mini into a portable battery and pure sine wave inverter. the power kit adds roughly $230 to the build, weighs about 2 kilograms and keeps the computer running for around 10 to 12 hours depending on the workload. suddenly, the mac is no longer tied to a desk. it can process documents, download files, run scheduled scripts and organize research from a backpack, a car or any place where a normal computer would need an outlet. claude handles the reasoning, obsidian becomes the permanent memory and local whisper converts hours of audio into text without charging by the minute. python connects the stack and starts each task automatically, even when nobody is watching the screen. one 2-hour lecture can become 5 key ideas, 3 timestamped quotes and 5 review questions. 120 unread links can become connected notes, while a 45-minute meeting is turned into a clean summary before the laptop is opened. the full $829 setup can replace a $25 storage plan, $39 automation tool, $20 transcription service and a $30 vps. instead of paying up to $114 every month, the machine runs locally with only a few dollars in claude api usage. bookmark this before ai computers stop needing desks.

Gipp 🦅

14,055 görüntüleme • 1 ay önce

APPLE SOLD THIS 39.9-POUND TOWER FOR $2,499 IN 2009 - ONE LATCH TURNED THE ENTIRE MACHINE INTO A WORKBENCH. this is the Early 2009 Mac Pro. the video shows the part spec sheets rarely capture. pull one latch and the aluminum side panel comes away. the memory, graphics card, drive bays and expansion slots are immediately visible. no hidden screws before you can inspect the machine. no loose SATA cables hanging from every drive. the base model shipped with: 2.66GHz quad-core Xeon 3GB of 1066MHz DDR3 ECC memory 640GB SATA hard drive GeForce GT 120 with 512MB of memory four PCIe 2.0 slots. the quad-core model supported up to 16GB of RAM. storage was handled by four cable-free carriers that connected directly inside the chassis. the case also left space for two optical drives. the rear panel carried hardware that now feels like an archive: dual Gigabit Ethernet optical audio USB 2.0 FireWire 800. the GT 120 offered Mini DisplayPort and dual-link DVI. install four of those cards and Apple rated the tower for up to eight 30-inch displays. the enclosure measured 20.1 inches tall and weighed almost 40 pounds before serious upgrades. this was not a compact desktop. it was a machine designed to be opened, understood and rebuilt. honest line: the Xeon, GT 120, SATA drive and USB 2.0 ports are ancient by modern standards. beautiful serviceability does not make old hardware fast. those parts aged. the enclosure did not. 17 years later, the most impressive component is still the case. bookmark & watch today ↓
0:53

APPLE SOLD THIS 39.9-POUND TOWER FOR $2,499 IN 2009 - ONE LATCH TURNED THE ENTIRE MACHINE INTO A WORKBENCH. this is the Early 2009 Mac Pro. the video shows the part spec sheets rarely capture. pull one latch and the aluminum side panel comes away. the memory, graphics card, drive bays and expansion slots are immediately visible. no hidden screws before you can inspect the machine. no loose SATA cables hanging from every drive. the base model shipped with: 2.66GHz quad-core Xeon 3GB of 1066MHz DDR3 ECC memory 640GB SATA hard drive GeForce GT 120 with 512MB of memory four PCIe 2.0 slots. the quad-core model supported up to 16GB of RAM. storage was handled by four cable-free carriers that connected directly inside the chassis. the case also left space for two optical drives. the rear panel carried hardware that now feels like an archive: dual Gigabit Ethernet optical audio USB 2.0 FireWire 800. the GT 120 offered Mini DisplayPort and dual-link DVI. install four of those cards and Apple rated the tower for up to eight 30-inch displays. the enclosure measured 20.1 inches tall and weighed almost 40 pounds before serious upgrades. this was not a compact desktop. it was a machine designed to be opened, understood and rebuilt. honest line: the Xeon, GT 120, SATA drive and USB 2.0 ports are ancient by modern standards. beautiful serviceability does not make old hardware fast. those parts aged. the enclosure did not. 17 years later, the most impressive component is still the case. bookmark & watch today ↓

Grimmer

221,756 görüntüleme • 9 gün önce

DELL ASUS ACER AND GIGABYTE ALL SHIPPED $2,999 DGX SPARK CLONES IN THE SAME QUARTER, SAMSUNG GEN 5 SSD CHOICE CAUSES 2X PERFORMANCE GAPS, THE LOCAL AI HARDWARE MAP JUST GREW BY FOUR FRONTIER TIER OPTIONS 02:13 the reviewer flips through the spec sheets, "we saw major differences in the SSD, some are using Gen 4 SSDs which will slow these platforms down, others have Gen 5 Samsung drives, Samsung is the leader by almost a factor of two" NVIDIA's GB10 Grace Blackwell reference board got picked up by Dell, Asus, Acer and Gigabyte in the same quarter, four $2,999 mini boxes with 128GB unified memory and 1 PFLOP of AI throughput from competing OEMs the Asus version runs coolest across GPU, CPU and SSD thanks to an unpainted bottom plate and a copper heatsink element, the Acer leads on price-to-perf, the Dell honeycomb intake actually restricts airflow slightly, the gigabyte sits middle of pack Samsung Gen 5 versus Gen 4 inside the same chassis causes a 2x read-write gap for the exact same $2,999 sticker, the buyer who reads the SSD spec sheet gets a 70B model loading in 4 seconds, the one who doesn't waits 8 your serie's frontier tier was the $1,700 EVO-X2, four DGX Spark OEM clones at $2,999 extend the map upward, the local AI hardware boom is now staffed by every major PC vendor not just AMD and apple, the category fully arrived bookmark this and read the article below
2:50

DELL ASUS ACER AND GIGABYTE ALL SHIPPED $2,999 DGX SPARK CLONES IN THE SAME QUARTER, SAMSUNG GEN 5 SSD CHOICE CAUSES 2X PERFORMANCE GAPS, THE LOCAL AI HARDWARE MAP JUST GREW BY FOUR FRONTIER TIER OPTIONS 02:13 the reviewer flips through the spec sheets, "we saw major differences in the SSD, some are using Gen 4 SSDs which will slow these platforms down, others have Gen 5 Samsung drives, Samsung is the leader by almost a factor of two" NVIDIA's GB10 Grace Blackwell reference board got picked up by Dell, Asus, Acer and Gigabyte in the same quarter, four $2,999 mini boxes with 128GB unified memory and 1 PFLOP of AI throughput from competing OEMs the Asus version runs coolest across GPU, CPU and SSD thanks to an unpainted bottom plate and a copper heatsink element, the Acer leads on price-to-perf, the Dell honeycomb intake actually restricts airflow slightly, the gigabyte sits middle of pack Samsung Gen 5 versus Gen 4 inside the same chassis causes a 2x read-write gap for the exact same $2,999 sticker, the buyer who reads the SSD spec sheet gets a 70B model loading in 4 seconds, the one who doesn't waits 8 your serie's frontier tier was the $1,700 EVO-X2, four DGX Spark OEM clones at $2,999 extend the map upward, the local AI hardware boom is now staffed by every major PC vendor not just AMD and apple, the category fully arrived bookmark this and read the article below

starmex

17,573 görüntüleme • 1 ay önce

1 AI character clip becomes 50 ads without a single actor or reshoot. 100% generated. The face isn’t real. The pipeline is. Here’s the build, start to finish. Part 1 - Make the character. Generate the face on a node canvas. Lock the identity so it holds across every shot. Part 2 - Drop in the product. One node. The product sits in her hands, on the desk, wherever you point it. Part 3 - Animate. Seedance 2.0 turns the still into a moving clip. Lighting, motion, room - all baked in. Part 4 - The swap nodes. This is the part nobody shows. Build nodes that replace one thing at a time. Character. Outfit. Product. Nothing else moves. Swap the product → new ad. Swap the outfit → new ad. Swap the face → new ad. Same 6 seconds. Same light. Same motion. 50 variants before lunch. Old way: hire talent, film, reshoot for every SKU, edit each cut. New way: 1 canvas, swap 1 node, export. The cost of variant 2 used to be a full shoot. Now it’s a click. Film once. Swap forever.
0:45

1 AI character clip becomes 50 ads without a single actor or reshoot. 100% generated. The face isn’t real. The pipeline is. Here’s the build, start to finish. Part 1 - Make the character. Generate the face on a node canvas. Lock the identity so it holds across every shot. Part 2 - Drop in the product. One node. The product sits in her hands, on the desk, wherever you point it. Part 3 - Animate. Seedance 2.0 turns the still into a moving clip. Lighting, motion, room - all baked in. Part 4 - The swap nodes. This is the part nobody shows. Build nodes that replace one thing at a time. Character. Outfit. Product. Nothing else moves. Swap the product → new ad. Swap the outfit → new ad. Swap the face → new ad. Same 6 seconds. Same light. Same motion. 50 variants before lunch. Old way: hire talent, film, reshoot for every SKU, edit each cut. New way: 1 canvas, swap 1 node, export. The cost of variant 2 used to be a full shoot. Now it’s a click. Film once. Swap forever.

HodlReaper

21,188 görüntüleme • 2 ay önce

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?
1:05

One guy keeps a farm of Mac minis on his desk and says each $600 box brings him $2,000 a month while he sleeps. AND THE HARDWARE ACTUALLY WORKS. But the number is not even the interesting part. The broken part is HOW: his AI no longer sits in a chat window. It sees the screen, moves the mouse itself, types and clicks the interface like a human at a computer. That is it. While most people still run AI in a chat and ask it for text, he sat Claude down right at the computer and put it to work with its hands. He automated not a single task but the workplace itself. How it actually works: on every Mac mini Claude runs with computer use turned on and the official Claude API docs spell it out: screenshot capture, mouse control, keyboard input, desktop automation. The agent opens the browser and the apps itself and runs the boring routine on a schedule: pulls leads, fills the CRM, checks orders, runs QA on the site. One box, one quiet worker that does not sleep and does not ask for a salary. His math is simple: a Mac mini is $600 once, Claude Max is $200 a month, and a live white-collar worker on the same routine costs a business $4,000 and up. So he rents out each node to a client as an AI worker for about $2,000 and 6 Mac minis come out to around $12,000 a month with costs a bit over $1,000 on subscriptions. But the $12,000 is his projection not a revenue dashboard: the video has no client, no task log, no working automation at all. The real asset here is not the stack of hardware but the one repeatable process the agent actually closes. Because a Mac mini on its own earns nothing. The money shows up exactly where the boring browser routine used to be done by hand for a salary and now you can hand it to an agent for the price of a subscription. Computer use is still in beta, almost nobody builds a service on it and the demand for cheap GUI routine is huge. The window is open for literally the next few months. Most people will watch this, laugh at the "$600 AI worker" and close it. And the ones who actually put an agent on one boring task and grind it into a repeat will ride this wave while it is still empty. Would you sit an AI right at your own computer on the boring routine or are you still clicking through it by hand?

Sorven

12,259 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

A Stanford 26-year-old spent 2 years turning 11,400 Obsidian notes into a neural net that finishes his sentences. It started as a folder called “misc” with 43 PDF in it. Most vaults die around 300 notes, because nothing connects and nothing comes back roughly 90% of what you save gets read once, on the day you save it. He fixed it by treating retrieval as the product and storage as the leftovers. Phase 1: Atomize (months 1-3) One idea per note, 200 words max, title written as a claim instead of a topic. “Attention is a filter, not a spotlight” beats “Notes on attention.” He rewrote 1,200 old notes this way and the vault shrank from 2,800 files to 1,600 usable ones. Phase 2: Embed locally (months 4-8) Ollama running nomic-embed-text on a Mac Mini M4, every note chunked at 512 tokens with 64-token overlap, all of it dumped into a local ChromaDB. 11,400 notes became 38,000 vectors and 1.2 GB on disk. Query time 0.4 seconds, cost $0 a month, nothing leaving the machine. Phase 3: Kill the hallucinations (months 9-14) Plain cosine search returned garbage about 30% of the time, so he bolted on a re-ranker and one hard rule: every retrieved chunk carries its source note title into the answer, and no citation means no answer. Wrong recalls fell from 1 in 4 to 1 in 30. Phase 4: Make it speak first (months 15-24) A nightly job reads the day’s writing, pulls the 5 oldest notes with the highest similarity, and drops 3 questions into tomorrow’s daily note. He stopped searching the vault somewhere in month 17 now it opens the conversation before he does. Before: 2,800 notes, 0 reused, 6 weeks per paper draft. After: 11,400 notes, 60-70 surfaced every week unasked, 9 days per draft. The whole stack is 4 pieces Obsidian, Ollama, ChromaDB, and a 40-line Python script that runs at 2 AM. Total software spend across 24 months: $0. Most people build a second brain to store things they will never open again. He stopped writing to remember. He writes so the machine can remind him.
0:26

A Stanford 26-year-old spent 2 years turning 11,400 Obsidian notes into a neural net that finishes his sentences. It started as a folder called “misc” with 43 PDF in it. Most vaults die around 300 notes, because nothing connects and nothing comes back roughly 90% of what you save gets read once, on the day you save it. He fixed it by treating retrieval as the product and storage as the leftovers. Phase 1: Atomize (months 1-3) One idea per note, 200 words max, title written as a claim instead of a topic. “Attention is a filter, not a spotlight” beats “Notes on attention.” He rewrote 1,200 old notes this way and the vault shrank from 2,800 files to 1,600 usable ones. Phase 2: Embed locally (months 4-8) Ollama running nomic-embed-text on a Mac Mini M4, every note chunked at 512 tokens with 64-token overlap, all of it dumped into a local ChromaDB. 11,400 notes became 38,000 vectors and 1.2 GB on disk. Query time 0.4 seconds, cost $0 a month, nothing leaving the machine. Phase 3: Kill the hallucinations (months 9-14) Plain cosine search returned garbage about 30% of the time, so he bolted on a re-ranker and one hard rule: every retrieved chunk carries its source note title into the answer, and no citation means no answer. Wrong recalls fell from 1 in 4 to 1 in 30. Phase 4: Make it speak first (months 15-24) A nightly job reads the day’s writing, pulls the 5 oldest notes with the highest similarity, and drops 3 questions into tomorrow’s daily note. He stopped searching the vault somewhere in month 17 now it opens the conversation before he does. Before: 2,800 notes, 0 reused, 6 weeks per paper draft. After: 11,400 notes, 60-70 surfaced every week unasked, 9 days per draft. The whole stack is 4 pieces Obsidian, Ollama, ChromaDB, and a 40-line Python script that runs at 2 AM. Total software spend across 24 months: $0. Most people build a second brain to store things they will never open again. He stopped writing to remember. He writes so the machine can remind him.

West Lord

170,060 görüntüleme • 25 gün önce

THIS DEVELOPER BOUGHT A $799 MAC MINI AND NOW RUNS 5 FACELESS YOUTUBE CHANNELS FOR $55/MONTH WITH CLAUDE AGENTS the trick is not buying a stronger computer. the trick is giving claude its own 24/7 machine, so it can take over the screen, click through tools, move files, write scripts and keep working while his main laptop stays untouched each channel runs from its own skill. documentary, luxury, gaming, deep sea and infrastructure all have different tones, seo formats and script rules. claude can turn one topic into a 1,600-2,000 word script, 8-10 visual prompts and 3 title options without a new brief every night the whole system costs $799 once and around $55 a month to operate. claude, elevenlabs, midjourney and electricity replace the manual loop most faceless creators get stuck in after month two. the mac mini itself stays online for about $3/month setup takes around 45 minutes. fresh macos install, 32gb ram, claude desktop, computer use permissions, google drive routing, connectors and one dedicated output folder. after that the machine becomes the place where the work happens most people try to scale youtube by adding more channels and more manual work. this flips the model. one box handles research, scripts, prompts, descriptions and scheduling while the human does one review session per week month 6 is where this starts getting dangerous. the channels that survive the first 90 days are not just posting videos anymore. they are running a tiny content factory from a box under the desk
1:07

THIS DEVELOPER BOUGHT A $799 MAC MINI AND NOW RUNS 5 FACELESS YOUTUBE CHANNELS FOR $55/MONTH WITH CLAUDE AGENTS the trick is not buying a stronger computer. the trick is giving claude its own 24/7 machine, so it can take over the screen, click through tools, move files, write scripts and keep working while his main laptop stays untouched each channel runs from its own skill. documentary, luxury, gaming, deep sea and infrastructure all have different tones, seo formats and script rules. claude can turn one topic into a 1,600-2,000 word script, 8-10 visual prompts and 3 title options without a new brief every night the whole system costs $799 once and around $55 a month to operate. claude, elevenlabs, midjourney and electricity replace the manual loop most faceless creators get stuck in after month two. the mac mini itself stays online for about $3/month setup takes around 45 minutes. fresh macos install, 32gb ram, claude desktop, computer use permissions, google drive routing, connectors and one dedicated output folder. after that the machine becomes the place where the work happens most people try to scale youtube by adding more channels and more manual work. this flips the model. one box handles research, scripts, prompts, descriptions and scheduling while the human does one review session per week month 6 is where this starts getting dangerous. the channels that survive the first 90 days are not just posting videos anymore. they are running a tiny content factory from a box under the desk

Gipp 🦅

19,829 görüntüleme • 2 ay önce

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.
0:47

everyone in iOS development should watch this. seriously, it might change the whole industry. i pointed claude code at a live ios device running on revyl, typed "test everything," and walked away. here's what's actually happening: ① you don't write the tests. no scripts, no selectors, no test plan. i never told it which screens to open or what to check. it read the app, decided what mattered, and tested it. the entire instruction was "test everything." ② it built its own test team. it looked at the app, clocked that it's basically four mini apps (rides, delivery, services, account), and split itself into 4 agents, one per surface. scoping coverage like that is usually a person's whole afternoon. it did it in seconds, unprompted. ③ all four ran at the same time, each on its own live device. this is where revyl comes in. every agent gets its own live ios session in the cloud, so four running apps get tested in parallel instead of taking turns on one simulator. serial testing turns coverage into a time tax. running all of it at once removes the tax. ④ it tests like a person, not like a script. each agent drives the app the way a user would, taps through the flows, and visually checks each screen against what it expected to see. nothing is pinned to a brittle element id, so renaming a button doesn't take down half your suite. that one detail is the most annoying thing about how we test today, and it just quietly goes away. ⑤ no xcuitest, no sims melting your laptop. i didn't write a single xcuitest script, and there were no simulators booting on my machine. the agents run on cloud devices, so coverage stops being capped by what your laptop can handle. the part that got me isn't that an agent tested an app. it's that i never told it how. i handed it a device and an intent, and it figured out the scoping, the parallelizing, and the driving on its own. if you still write and maintain mobile ui tests by hand, i'm not sure that lasts the year.

Landseer Enga

23,963 görüntüleme • 2 ay önce

Trading without AI in 2026? You're done. Purely on the math, before anyone even out-trades you. You are not up against other people anymore. 75% of US stock volume runs through machines. Jane Street put $6 billion and 4,032 GPUs behind agents that write trading strategies, test them and kill them all night. Man Group's AI turns out hundreds of new signals a week where its human desk used to manage about 20 a quarter. You're on the other side of that with a chart and a feeling. 89% of retail traders lost money last year. The gap was never brains. Testing one idea properly costs a desk about $87,500 in salary time or massive computing power for AI. You don't have any of those things. That part is over. You have your own AI trading agent now, built by a team backed by Entrée Capital and hedge fund managers, and trying it costs nothing: No code, no desk, no excuses left. What it does with one English sentence: > compiles your idea into entry, exit, sizing and risk rules > backtests 5 years of data in about 12 seconds, 1-minute bars in 47 > scores it 0 to 100 on Sharpe, Sortino, profit factor and max drawdown > kills anything with a half-life under 10 days no matter how pretty the curve looks > runs walk-forward and Monte Carlo on whatever survives, then years of data it has never seen > puts it live on your exchange in about 90 seconds and keeps running while you sleep Same machine the funds run, minus the hiring, the salaries and the three-week wait. 4,800+ traders are already inside. Save this and put one idea through it tonight after read full breakdown below. You'll know in 12 seconds what three weeks of hoping never told you.
0:54

Trading without AI in 2026? You're done. Purely on the math, before anyone even out-trades you. You are not up against other people anymore. 75% of US stock volume runs through machines. Jane Street put $6 billion and 4,032 GPUs behind agents that write trading strategies, test them and kill them all night. Man Group's AI turns out hundreds of new signals a week where its human desk used to manage about 20 a quarter. You're on the other side of that with a chart and a feeling. 89% of retail traders lost money last year. The gap was never brains. Testing one idea properly costs a desk about $87,500 in salary time or massive computing power for AI. You don't have any of those things. That part is over. You have your own AI trading agent now, built by a team backed by Entrée Capital and hedge fund managers, and trying it costs nothing: No code, no desk, no excuses left. What it does with one English sentence: > compiles your idea into entry, exit, sizing and risk rules > backtests 5 years of data in about 12 seconds, 1-minute bars in 47 > scores it 0 to 100 on Sharpe, Sortino, profit factor and max drawdown > kills anything with a half-life under 10 days no matter how pretty the curve looks > runs walk-forward and Monte Carlo on whatever survives, then years of data it has never seen > puts it live on your exchange in about 90 seconds and keeps running while you sleep Same machine the funds run, minus the hiring, the salaries and the three-week wait. 4,800+ traders are already inside. Save this and put one idea through it tonight after read full breakdown below. You'll know in 12 seconds what three weeks of hoping never told you.

cvxv666

38,982 görüntüleme • 19 gün önce

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.
1:16

This Chinese developer linked two $2,999 NVIDIA DGX Sparks into one box and runs the full Qwen3-235B at home, after dropping his $1,999-a-month cloud bill to zero. He wired 2 small boxes into a single computer, split a giant 235-billion-parameter model in half between them, and serves it across his own network at about 10 tokens a second, with no internet, no cloud, right there on the desk. No data center, no thousand-dollar graphics cards, no monthly cloud bill. Just him, 2 gold boxes the size of a sandwich, one cable between them, and 1 power strip. And here is the whole payoff. He used to pay the cloud $1,999 a month for the same model, and the meter ticked on every request. Now he paid $5,998 once for 2 boxes, they covered their cost in 3 months, and after that he sends as many requests as he wants for free, only electricity. The two Sparks talk over one fast cable, each holds 128GB of memory, and together they carry the whole model, about 73GB loaded per box, with the chip inside pinned near the limit at 96%. Both boxes work as one and keep trading data over the cable, with no cloud in the loop and no single word leaking out. The ready model sits on one local address, and any app on his network calls it as easily as ChatGPT. And here is how he described, in plain words, what this pair of boxes does: "this is a pair of boxes that holds the huge Qwen3-235B model and serves it to one network. the model is split in half, and each box owns its half. parts: // Box 1 (holds the first half of the model and starts the answer fast, the first word appears in under a second) // Box 2 (holds the second half and writes out the rest, about 10 tokens a second) // Cable (connects the 2 boxes and moves data between them on every step, with no lag) // Address (one local address where any app sends its request, like to a cloud model) // Test (a script that runs big prompts through and measures speed and delays) // Monitor (checks temperature, power draw, and load on both boxes every 2 seconds). the model never goes to the cloud. he only steps in when a box runs hotter than 80 degrees or the cable between them starts dropping data." So the system knows exactly what it is, what it is for, and where its limits are. It knows it has to hold the whole huge model across 2 boxes on its own. It knows it has to answer every request locally, with no meter, no limits, and no internet. It knows the human is only needed when a box overheats or the link between them stalls. → The setup runs around the clock on 2 boxes, each pulling under 60 watts → However many requests he sends, the monthly bill is $0, only electricity → The first box starts the answer in under a second → The second writes text at about 10 tokens a second → One request at a time: 838 tokens in 85 seconds, first word in 0.8s → Two requests at once: 697 tokens in 108 seconds, first word in 0.7s → Both boxes sit at 96% load and warm up to 76-78 degrees And only when a chip in a box runs hotter than 80 degrees or the cable between the 2 Sparks drops data does the system call the owner. And when he himself is out on a run or in a coffee shop, he still reaches his own model at home from his phone: sends a big prompt to the local Qwen3-235B, gets the full answer back in under a minute and a half, with no token meter ticking and no limit to hit. Here is what the test shows on his screen during one of the night runs: "one request at a time: 838 tokens in 84.9 seconds, first word in 0.8s, then 0.1s per token." "two requests at once: 697 tokens in 107.6 seconds, first word in 0.7s, then 0.15s per token." "Box 1: chip at 96% load, 76 degrees, 56 watts, 73GB used in memory." "Box 2: chip at 96% load, 78 degrees, 56 watts, the Qwen3-235B model fully loaded." And while everyone around is paying for AI by the month and bumping into limits, his top-tier model just sits on the desk and works as much as he wants: his own little power plant instead of a forever meter. He has no server rack of his own and no cloud account behind it. Just 2 DGX Spark boxes on a desk, one model split in half between them, one local address, and a folder of prompts next to it. Out of everything I have seen this year, this is the cleanest way to stop paying for AI: $5,998 of hardware on the desk once, $0 a month to the cloud, unlimited forever, and between them 2 gold boxes, 1 cable, and the full Qwen3-235B answering at home with no internet.

Blaze

93,871 görüntüleme • 2 ay önce