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3 problems. 1 box. ❌ Losing files forever. ✅ RAID + Backup. One drive fails? No problem. ❌ Chaos across devices. ✅ Thunderbolt 4 + 10GbE. Fast sync. Remote access. ❌ Paying forever for slow, creepy cloud storage. ✅ No subscription. No speed limits. Your home, your data. #ZimaCube2....

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ZimaSpace

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24,333 Aufrufe • vor 2 Monaten •via X (Twitter)

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Gipp 🦅

457,929 Aufrufe • vor 2 Monaten

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.
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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 Aufrufe • vor 2 Monaten

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs
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⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,874 Aufrufe • vor 2 Jahren

$100 to One Person who LIKES ❤️ + RETWEETS ♻️ Must be following Flex Props 💎 ✅ Recap 💰 Yesterday’s PROFIT: +$750 to $800+ (for $100 bettors) 💰 ✅ 📈 MONSTER GREEN DAY — one of our biggest single-day runs ⸻ 🚨 ABSOLUTE HEATER DAY 🚨 We went CRAZY yesterday… wins across NHL, NBA, Soccer & Ladders 🔥 Up a TON of units in ONE DAY and stayed disciplined 💯 ⸻ ✅ CASHED PLAYS ✅ 🏒 Bruins vs Wild Over 1.5 2nd Period Goals | -180 | 2U 💣 FREE BOMB ✅ ⚽️ Red Bull Salzburg vs RZ Pellets WAC Over 2.5 Goals | -165 FanDuel | 2U ✅ 🏒 Canucks vs Devils Goal in First 10 Minutes of 3rd Period | +132 FanDuel ✅ 🏒 Löwen Frankfurt vs Straubing Tigers Over 0.5 1st Period Goals | 2U ✅ Collab NBA 🏀 Parlay | +151 FanDuel • Jalen Smith 5+ Points • Deandre Ayton Over 3.5 1st Q Points ⸻ 🪜 LADDER PLAY RUN 🪜 ✅ Play 1️⃣ $77.78 ➡️ $157.12 ✅ Play 2️⃣ $157.12 ➡️ $246.90 ✅ Play 3️⃣ $246.90 ➡️ $367.07 🔥 Ladder Profit: +$289.29 ⸻ ✅ MORE CASH ✅ Moses Moody Over 7.5 Points -141 | 2U ✅ 76ers vs Hawks Over 224.5 -110 | 2U ⸻ ❌ LOSSES (CONTROLLED) 🤮 Jalen Smith Over 7.5 Points | 2U 🤮 Trey Murphy Over 3.5 Assists | 1U 🤮 Rui Hachimura Over 1.5 Threes | 1U ⸻ 💰 $100 BETTOR SUMMARY If you played $100 per play (no unit scaling): ➡️ Up approximately $750–$800+ on the day ➡️ Even with losses, ROI was 🔥 thanks to ladders & plus-money hits 💎 VIP 85% OFF | 3 MONTHS $15 👇 DubClub: 📊 All Data Researched on HOF. Use my code “FLEXPROPS” and get a free week + 50% Off after trial.
0:58

$100 to One Person who LIKES ❤️ + RETWEETS ♻️ Must be following Flex Props 💎 ✅ Recap 💰 Yesterday’s PROFIT: +$750 to $800+ (for $100 bettors) 💰 ✅ 📈 MONSTER GREEN DAY — one of our biggest single-day runs ⸻ 🚨 ABSOLUTE HEATER DAY 🚨 We went CRAZY yesterday… wins across NHL, NBA, Soccer & Ladders 🔥 Up a TON of units in ONE DAY and stayed disciplined 💯 ⸻ ✅ CASHED PLAYS ✅ 🏒 Bruins vs Wild Over 1.5 2nd Period Goals | -180 | 2U 💣 FREE BOMB ✅ ⚽️ Red Bull Salzburg vs RZ Pellets WAC Over 2.5 Goals | -165 FanDuel | 2U ✅ 🏒 Canucks vs Devils Goal in First 10 Minutes of 3rd Period | +132 FanDuel ✅ 🏒 Löwen Frankfurt vs Straubing Tigers Over 0.5 1st Period Goals | 2U ✅ Collab NBA 🏀 Parlay | +151 FanDuel • Jalen Smith 5+ Points • Deandre Ayton Over 3.5 1st Q Points ⸻ 🪜 LADDER PLAY RUN 🪜 ✅ Play 1️⃣ $77.78 ➡️ $157.12 ✅ Play 2️⃣ $157.12 ➡️ $246.90 ✅ Play 3️⃣ $246.90 ➡️ $367.07 🔥 Ladder Profit: +$289.29 ⸻ ✅ MORE CASH ✅ Moses Moody Over 7.5 Points -141 | 2U ✅ 76ers vs Hawks Over 224.5 -110 | 2U ⸻ ❌ LOSSES (CONTROLLED) 🤮 Jalen Smith Over 7.5 Points | 2U 🤮 Trey Murphy Over 3.5 Assists | 1U 🤮 Rui Hachimura Over 1.5 Threes | 1U ⸻ 💰 $100 BETTOR SUMMARY If you played $100 per play (no unit scaling): ➡️ Up approximately $750–$800+ on the day ➡️ Even with losses, ROI was 🔥 thanks to ladders & plus-money hits 💎 VIP 85% OFF | 3 MONTHS $15 👇 DubClub: 📊 All Data Researched on HOF. Use my code “FLEXPROPS” and get a free week + 50% Off after trial.

Flex Props 💎

11,843 Aufrufe • vor 7 Monaten

Today I'm lanching my secret openclaw agent I use 1000+ times daily you can use it for free tbh you don't need a 512GB Mac Studio to solve a Mission Control problem. I've been working on ShipClaw for the last 1.5 weeks after my software job. I've been in AI for the last 3 4 years and I know how to solve the Mission Control problem. You must solve 3 problems: → Every user has their own tenant → Every user has their own pairing ID so other members know which platform OpenClaw is used on → Mission Control AI Agent Orchestration That's how I solve these problems using Railway, Cloudflare, and infrastructure VPS in the cloud. Now users have their own identity, own auth, and own OpenClaw server that runs 24/7 with no cost. Why should you pay for a 512GB Mac Studio and give your computer access to OpenClaw? ❌ Stop doing that. Instead, invest in something that pays you in the long game. enjoy ❤️
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Today I'm lanching my secret openclaw agent I use 1000+ times daily you can use it for free tbh you don't need a 512GB Mac Studio to solve a Mission Control problem. I've been working on ShipClaw for the last 1.5 weeks after my software job. I've been in AI for the last 3 4 years and I know how to solve the Mission Control problem. You must solve 3 problems: → Every user has their own tenant → Every user has their own pairing ID so other members know which platform OpenClaw is used on → Mission Control AI Agent Orchestration That's how I solve these problems using Railway, Cloudflare, and infrastructure VPS in the cloud. Now users have their own identity, own auth, and own OpenClaw server that runs 24/7 with no cost. Why should you pay for a 512GB Mac Studio and give your computer access to OpenClaw? ❌ Stop doing that. Instead, invest in something that pays you in the long game. enjoy ❤️

Umair Ali

33,453 Aufrufe • vor 4 Monaten

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.
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A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 Aufrufe • vor 1 Jahr

Weekend plans: I can sleep at 3AM. My tiny server probably can’t. 😎 #ZimaBoard2 #ZimaOS #HomeServer #Homelab
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Weekend plans: I can sleep at 3AM. My tiny server probably can’t. 😎 #ZimaBoard2 #ZimaOS #HomeServer #Homelab

ZimaSpace

36,456 Aufrufe • vor 3 Monaten