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15 concurrent terminal workloads on a local DGX Spark, all served by nvidia/nemotron-3-super 120B A12B NVFP4 through vLLM. 15/15 completed, 0 errors, 30.2s wall time: no fake dashboard, just local inference under concurrent load. fineprint: live local concurrency demo

Joey

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WeiboAI dropped VibeThinker-3B, so I had to try it locally. this is a 3B model, not a giant frontier system. in the video I load it on my DGX Spark, give it 3 small algorithm problems, stream the actual model output live, then run the generated python through pytest. no benchmark screenshot no canned answer just a tiny local reasoner writing code and real tests deciding if it worked it went 3/3.

WeiboAI dropped VibeThinker-3B, so I had to try it locally. this is a 3B model, not a giant frontier system. in the video I load it on my DGX Spark, give it 3 small algorithm problems, stream the actual model output live, then run the generated python through pytest. no benchmark screenshot no canned answer just a tiny local reasoner writing code and real tests deciding if it worked it went 3/3.

Joey

16,865 Aufrufe • vor 3 Monaten

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses three NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,861 Aufrufe • vor 8 Monaten

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

AI researcher breaks down why your $5,000 Mac Studio runs local AI slower than benchmarks promise: "Performance isn't about the hardware - it's about three software layers stacked on top of it." in 15 minutes he tests MLX, Ollama, llama.cpp, and the new vllm-mlx head to head no GPU needed, just the Mac you probably already own - why Ollama, the most popular tool, is actually one of the slower options on Mac - the real bottleneck nobody benchmarks: 'prefill' time before the first token even appears - the exact quantization setting (4-bit) that's the best speed-to-quality tradeoff - why unified memory makes Macs uniquely good for big models compared to Nvidia GPUs complete guide to choosing the right hardware for local AI is below 👇

rewind

207,513 Aufrufe • vor 3 Monaten

Voice agents still don’t understand who’s speaking to them. That’s a huge gap compared with humans, hidden by all the “phone-call” demos. But that changes today! NVIDIA is open-sourcing Nemotron 3 Diarization: a model that can reliably track speakers in live conversations, under a commercial-friendly license! In my tests, the quality is really good with one-second speech chunks. So we can use it for voice agents! I tested it with Reachy Mini and speech-to-speech running on a DGX Spark. It’s super fun to see the robot notice a new voice, ask for a name, and remember it. The model has day-zero integration with Transformers! Kudos to the NVIDIA team for shipping useful tools for the whole community!

Voice agents still don’t understand who’s speaking to them. That’s a huge gap compared with humans, hidden by all the “phone-call” demos. But that changes today! NVIDIA is open-sourcing Nemotron 3 Diarization: a model that can reliably track speakers in live conversations, under a commercial-friendly license! In my tests, the quality is really good with one-second speech chunks. So we can use it for voice agents! I tested it with Reachy Mini and speech-to-speech running on a DGX Spark. It’s super fun to see the robot notice a new voice, ask for a name, and remember it. The model has day-zero integration with Transformers! Kudos to the NVIDIA team for shipping useful tools for the whole community!

Andi Marafioti

73,578 Aufrufe • vor 1 Tag

just built a local AI waifu on my two macbooks by using the oss parallax inference library!!! not super smooth yet but parallax is still the easiest way to run llms locally on top of mlx - and no bigco can steal my waifu. still need folks to help her see/hear/draw since mlx has no legit wrapper yet. i’m running a campaign giving away mac minis + dgx spark. please please share your localhost ai model setup by using parallax (you don't need a gpu to try self-hosting you can just start with an extra macbook you borrowed from your friend like i did) or any fun app built on top of yourself host model to link below. come build sovereign intelligence with me 🫶

just built a local AI waifu on my two macbooks by using the oss parallax inference library!!! not super smooth yet but parallax is still the easiest way to run llms locally on top of mlx - and no bigco can steal my waifu. still need folks to help her see/hear/draw since mlx has no legit wrapper yet. i’m running a campaign giving away mac minis + dgx spark. please please share your localhost ai model setup by using parallax (you don't need a gpu to try self-hosting you can just start with an extra macbook you borrowed from your friend like i did) or any fun app built on top of yourself host model to link below. come build sovereign intelligence with me 🫶

Asuka Zheng🎀@ICML

178,998 Aufrufe • vor 10 Monaten

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

NVIDIA JUST SHOWED HOW A $4,699 BOX CAN KILL A $200 MONTHLY AI BILL AND RUN 200B MODELS FROM YOUR DESK. 00:02 NVIDIA delivers the DGX Spark to developers, researchers, and AI teams, turning what used to require rented datacenter GPUs into a box small enough to hold in one hand. inside is 128GB of unified memory, 4TB of storage, and up to 1 PFLOP of AI compute, enough to run 70B models comfortably and push into the 200B class locally. that means no per-token API bill, no files leaving your machine, and no waiting for someone else’s server every time an agent reads documents, writes code, or runs an automation. two DGX Sparks can even be linked together for models around 400B parameters, while a single unit costs roughly $4,000 to $4,700 instead of renting multi-GPU cloud infrastructure every month. the full local AI ladder starts at $0 with the computer you already own, but NVIDIA just showed what the final rung looks like: a personal AI supercomputer sitting directly on your desk.

Gipp 🦅

11,334 Aufrufe • vor 2 Monaten

Most speculative decoding still drafts tokens one at a time. That's not parallel generation — it just hides the serial loop behind a smaller model. UC San Diego's z-lab just drew a clear line between the two. They released DFlash — a lightweight block diffusion model that drafts a whole block of tokens in a single forward pass, then lets the target model verify the block in parallel. Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell. No token-by-token drafting anywhere in the speculative path. Here's what's actually interesting: → The drafter is conditioned on the target model's own hidden features, injected into the Key/Value cache of every draft layer — so acceptance length scales with draft depth instead of diluting away → A 5-layer drafter replaces the 7B diffusion drafters that capped earlier methods near 3–4× → MATH-500 speedup: 6.08× vs. 1.81× for EAGLE-3 (4.86× average vs. 1.76×, Qwen3-8B, greedy) → Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell — at the same interactivity target → Lossless: the target still verifies every token, so output quality is preserved Full analysis: Paper: NVIDIA's metrics: Project: Model weights: Repo: NVIDIA AI NVIDIA AIDev

Most speculative decoding still drafts tokens one at a time. That's not parallel generation — it just hides the serial loop behind a smaller model. UC San Diego's z-lab just drew a clear line between the two. They released DFlash — a lightweight block diffusion model that drafts a whole block of tokens in a single forward pass, then lets the target model verify the block in parallel. Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell. No token-by-token drafting anywhere in the speculative path. Here's what's actually interesting: → The drafter is conditioned on the target model's own hidden features, injected into the Key/Value cache of every draft layer — so acceptance length scales with draft depth instead of diluting away → A 5-layer drafter replaces the 7B diffusion drafters that capped earlier methods near 3–4× → MATH-500 speedup: 6.08× vs. 1.81× for EAGLE-3 (4.86× average vs. 1.76×, Qwen3-8B, greedy) → Up to 15× higher throughput for gpt-oss-120b on NVIDIA Blackwell — at the same interactivity target → Lossless: the target still verifies every token, so output quality is preserved Full analysis: Paper: NVIDIA's metrics: Project: Model weights: Repo: NVIDIA AI NVIDIA AIDev

Marktechpost AI

23,328 Aufrufe • vor 3 Monaten

She defeated three ninjas... with ramen noodles. Who needs swords when your ramen is the ultimate weapon? Made with Seedance 2.0 on LovartAI Prompt: PART 1 (0:00–0:15) Cut 1 (0:00–0:05) — Sneaking Through the Market 1920s Osaka market, Betamax film look. Yuki stealthily tiptoes through crowded stalls, sleeve over her mouth, eyes scanning. She spots a ramen shop, grins, and dashes through the noren curtain. Cut 2 (0:05–0:10) — Ramen at Last Inside the steamy ramen shop, Yuki excitedly orders a giant tonkotsu ramen with extra pork and egg. She sits down as the steaming bowl arrives, smiling and raising her chopsticks like a sword. Cut 3 (0:10–0:15) — Ninja Ambush Three burly ninja-like thugs notice her, pull on masks, and suddenly attack. Tables scatter as they leap through the air. Yuki flips clear while protecting her ramen, frozen mid-slurp with an annoyed glare. PART 2 (0:15–0:30) Cut 4 (0:15–0:20) — Noodle Defense A ninja throws a shuriken. Yuki instantly catches it by stretching a single ramen noodle between her chopsticks, then effortlessly dodges the ninjas' attacks with playful martial-arts agility. Cut 5 (0:20–0:25) — Men-no-Jutsu Yuki plants her feet and chants, "Men-no-jutsu!" Glowing ramen noodles rise from the bowl and whip through the shop, wrapping around all three ninjas and tying them together. Cut 6 (0:25–0:30) — Bowl Finish The tangled ninjas stumble and crash into a heap. Yuki leaps high into the air and smashes the empty ramen bowl onto their heads with a triple bonk. Freeze frame on her fierce pout as her gold-and-red hairpins sparkle under the lantern light. #Lovart, #LovartPartner

She defeated three ninjas... with ramen noodles. Who needs swords when your ramen is the ultimate weapon? Made with Seedance 2.0 on LovartAI Prompt: PART 1 (0:00–0:15) Cut 1 (0:00–0:05) — Sneaking Through the Market 1920s Osaka market, Betamax film look. Yuki stealthily tiptoes through crowded stalls, sleeve over her mouth, eyes scanning. She spots a ramen shop, grins, and dashes through the noren curtain. Cut 2 (0:05–0:10) — Ramen at Last Inside the steamy ramen shop, Yuki excitedly orders a giant tonkotsu ramen with extra pork and egg. She sits down as the steaming bowl arrives, smiling and raising her chopsticks like a sword. Cut 3 (0:10–0:15) — Ninja Ambush Three burly ninja-like thugs notice her, pull on masks, and suddenly attack. Tables scatter as they leap through the air. Yuki flips clear while protecting her ramen, frozen mid-slurp with an annoyed glare. PART 2 (0:15–0:30) Cut 4 (0:15–0:20) — Noodle Defense A ninja throws a shuriken. Yuki instantly catches it by stretching a single ramen noodle between her chopsticks, then effortlessly dodges the ninjas' attacks with playful martial-arts agility. Cut 5 (0:20–0:25) — Men-no-Jutsu Yuki plants her feet and chants, "Men-no-jutsu!" Glowing ramen noodles rise from the bowl and whip through the shop, wrapping around all three ninjas and tying them together. Cut 6 (0:25–0:30) — Bowl Finish The tangled ninjas stumble and crash into a heap. Yuki leaps high into the air and smashes the empty ramen bowl onto their heads with a triple bonk. Freeze frame on her fierce pout as her gold-and-red hairpins sparkle under the lantern light. #Lovart, #LovartPartner

Shami

85,724 Aufrufe • vor 2 Monaten

🐆 Rapid-MLX v0.12 is here. We’ve officially evolved from a simple chat app into a full-fledged, on-device AI studio for Apple Silicon! 🖥️✨ We didn't just push the MLX inference engine to its limits and expand support for a massive lineup of local open-source models—we are alpha-launching the highly anticipated Desktop Version. (A huge shoutout to the IoTeX community for grinding through the closed beta with us. Your feedback was incredible and helped shape this beast.) Here are the game-changing features you can run on your Mac right now, 100% free and 100% offline 👇 🚀 Blazing Fast Local LLMs Run anything from 4B up to Qwen3.5-122B completely offline. No guessing games—we recommend models matched perfectly to your Mac's actual RAM. Rich chat includes syntax highlighting, markdown tables, and honest tok/s metrics. 🎨 Local Image Generation A brand new Images tab to render directly on your machine. Pick a model (FLUX.2-klein, Z-Image-Turbo), prompt, and refine. Everything lands in a visual filmstrip. 👁️ Vision & Live Web Tools Attach an image and chat about it with local vision. Need real-time data? Our built-in web tools (weather, search, page-fetch) run mid-answer with strict, transparent privacy controls. 🤖 Plug-and-Play Coding Agents Wire up Claude Code, Codex, Cline, or Continue in seconds. One copy-paste from the Launch tab spins up a local OpenAI/Anthropic-compatible endpoint. 🔒 Private by Design Everything runs on-device. Signed, notarized, and entirely local. Your data stays yours. Turn your Mac into an AI powerhouse today. ⚡️

raullen

34,148 Aufrufe • vor 1 Monat

In South Africa, a resilient woman named Blessing, a mother and bakery shop owner, juggles entrepreneurship and family every day without ever letting go of her dream. 📦 In the past, she handled sourcing, customs, and shipping all by herself—exhausting and overwhelming! 🚀 Now, Buffalo Logistics takes care of it all, delivering supplies from China to South Africa in just 36 hours, so she can focus on her customers—and enjoy more time with family. 🌐 Buffalo is the fastest online trade logistics provider between China and Africa. Powered by Huawei Cloud technology, Buffalo is building a smarter, faster supply chain: ✅ Delivery time: Reduced from 20 days to 36 hours ✅ Package delivery success rate: Increased from 90% to 99% ✅ Local service response time: Cut from 15 hours to 15 minutes 📈 Delivery efficiency now reaches 85%, 10% higher than the industry average 🤖 Automation has reduced error rates to 0.01% 🛡️ 24/7 cloud service ensures platform stability and uninterrupted operations 🤝 Huawei Cloud & Buffalo Logistics are reshaping trust through technology and delivering hope with speed across Africa.

In South Africa, a resilient woman named Blessing, a mother and bakery shop owner, juggles entrepreneurship and family every day without ever letting go of her dream. 📦 In the past, she handled sourcing, customs, and shipping all by herself—exhausting and overwhelming! 🚀 Now, Buffalo Logistics takes care of it all, delivering supplies from China to South Africa in just 36 hours, so she can focus on her customers—and enjoy more time with family. 🌐 Buffalo is the fastest online trade logistics provider between China and Africa. Powered by Huawei Cloud technology, Buffalo is building a smarter, faster supply chain: ✅ Delivery time: Reduced from 20 days to 36 hours ✅ Package delivery success rate: Increased from 90% to 99% ✅ Local service response time: Cut from 15 hours to 15 minutes 📈 Delivery efficiency now reaches 85%, 10% higher than the industry average 🤖 Automation has reduced error rates to 0.01% 🛡️ 24/7 cloud service ensures platform stability and uninterrupted operations 🤝 Huawei Cloud & Buffalo Logistics are reshaping trust through technology and delivering hope with speed across Africa.

Huawei Sub-Saharan Africa

665,686 Aufrufe • vor 1 Jahr

WARP SPEED: EPISODE 5 - starring Varunram Ganesh, Founder of Lapis Varunram Ganesh The next generation of 20 year olds are going to default to LLMs for search. Varun is building Lapis into the most accurate AI search analytics platform. They are part of Y Combinator F25 - 1 day away from demo day with a countdown clock on his desk. The insight: people are already defaulting to ChatGPT over Google. As time goes on, more leveraged decisions get made through LLMs, not search engines. "So many people are searching on Google, they're going to be searching on ChatGPT. Why aren't people knowing where they are on ChatGPT?" Lapis tells you exactly what a human will search for your website to show up in AI. And unlike Google, it's not about ranking positions - LLMs reason differently. On Warp: "I don't even think about payroll. Which is just to say Warp is such a good product that I just don't think about it. No spam emails. No upsells. It's just so smooth." In this conversation: (0:00) - Shift from Google-style search to LLMs: “More leveraged decisions are now made through the LLM.” (0:15) - What Lapis does: the most accurate AI search analytics platform. (1:15) - YC intensity: six weeks from Demo Day, rapid feedback loops, constant customer conversations. (2:00) - “The hardest part of running a startup is the most boring stuff.” (2:41) - How AI search differs from Google search. (3:23) - Building a company through trust. (3:51) - “Every founder said, ‘Check out Gabriella Warp - a genuinely friendly solution. (4:02) - Big providers needed six weeks just to get set up. (4:20) - Texted Ayush and got set up in 15 minutes. (4:25) - Warp is so smooth you don’t think about payroll — no spam emails, no upsells, just seamless.

WARP SPEED: EPISODE 5 - starring Varunram Ganesh, Founder of Lapis Varunram Ganesh The next generation of 20 year olds are going to default to LLMs for search. Varun is building Lapis into the most accurate AI search analytics platform. They are part of Y Combinator F25 - 1 day away from demo day with a countdown clock on his desk. The insight: people are already defaulting to ChatGPT over Google. As time goes on, more leveraged decisions get made through LLMs, not search engines. "So many people are searching on Google, they're going to be searching on ChatGPT. Why aren't people knowing where they are on ChatGPT?" Lapis tells you exactly what a human will search for your website to show up in AI. And unlike Google, it's not about ranking positions - LLMs reason differently. On Warp: "I don't even think about payroll. Which is just to say Warp is such a good product that I just don't think about it. No spam emails. No upsells. It's just so smooth." In this conversation: (0:00) - Shift from Google-style search to LLMs: “More leveraged decisions are now made through the LLM.” (0:15) - What Lapis does: the most accurate AI search analytics platform. (1:15) - YC intensity: six weeks from Demo Day, rapid feedback loops, constant customer conversations. (2:00) - “The hardest part of running a startup is the most boring stuff.” (2:41) - How AI search differs from Google search. (3:23) - Building a company through trust. (3:51) - “Every founder said, ‘Check out Gabriella Warp - a genuinely friendly solution. (4:02) - Big providers needed six weeks just to get set up. (4:20) - Texted Ayush and got set up in 15 minutes. (4:25) - Warp is so smooth you don’t think about payroll — no spam emails, no upsells, just seamless.

Ayush S

57,591 Aufrufe • vor 9 Monaten

Stablecoin on-ramps are broken in four fundamental ways. New report by Bluechip The Geography Tax > US/EU on-ramping: 0-0.3% > Central Africa: 15-20% Same dollar. Same stablecoin. 50x price difference. Why: Licensing is fragmented across jurisdictions. Local liquidity is thin. Banks treat these markets as afterthoughts. Operators must maintain exotic currency inventory in uncertain regulatory environments. No direct issuer relationships exist, so users rely on informal networks and local telco agents who set their own spreads. The Broken Funnel: Global card on-ramp completion: 21% Africa: 6% Asia: 7% Why: Failed KYC from document verification issues. Card declines from banks blocking crypto purchases. Session timeouts from complex multi-step flows. Poor localization. Lack of local payment method support. The journey was designed for crypto-native users, not first-time buyers. Cards Are Structurally Incompatible Emerging market card on-ramping: 7-10% US/EU: 3-5% Bank transfers: 0-0.3% Why: Card networks charge 1-3% MDR on every transaction. In e-commerce, merchants hide this in product margins. Stablecoins are pegged 1:1. There's no margin. You can't inflate the price of a dollar. Ramps must also price in chargeback risk and fraud losses. The cost has nowhere to go except to the user. This gap will not close. Remittance Corridors Are Broken Tanzania to Kenya traditional: 59.7% Stablecoin: 5-6% South Africa to China traditional: 22.8% Stablecoin: 1.2% Why: Correspondent banking adds layers of intermediaries. Each one takes a cut. Limited competition in smaller corridors. Weak FX liquidity. Cash-heavy payout networks. Settlement takes 1-5 days because every leg requires reconciliation. Stablecoins bypass all of this. Settlement in under an hour. But without accessible on-ramps, the technology sits behind a wall most users can't climb.

Stablecoin on-ramps are broken in four fundamental ways. New report by Bluechip The Geography Tax > US/EU on-ramping: 0-0.3% > Central Africa: 15-20% Same dollar. Same stablecoin. 50x price difference. Why: Licensing is fragmented across jurisdictions. Local liquidity is thin. Banks treat these markets as afterthoughts. Operators must maintain exotic currency inventory in uncertain regulatory environments. No direct issuer relationships exist, so users rely on informal networks and local telco agents who set their own spreads. The Broken Funnel: Global card on-ramp completion: 21% Africa: 6% Asia: 7% Why: Failed KYC from document verification issues. Card declines from banks blocking crypto purchases. Session timeouts from complex multi-step flows. Poor localization. Lack of local payment method support. The journey was designed for crypto-native users, not first-time buyers. Cards Are Structurally Incompatible Emerging market card on-ramping: 7-10% US/EU: 3-5% Bank transfers: 0-0.3% Why: Card networks charge 1-3% MDR on every transaction. In e-commerce, merchants hide this in product margins. Stablecoins are pegged 1:1. There's no margin. You can't inflate the price of a dollar. Ramps must also price in chargeback risk and fraud losses. The cost has nowhere to go except to the user. This gap will not close. Remittance Corridors Are Broken Tanzania to Kenya traditional: 59.7% Stablecoin: 5-6% South Africa to China traditional: 22.8% Stablecoin: 1.2% Why: Correspondent banking adds layers of intermediaries. Each one takes a cut. Limited competition in smaller corridors. Weak FX liquidity. Cash-heavy payout networks. Settlement takes 1-5 days because every leg requires reconciliation. Stablecoins bypass all of this. Settlement in under an hour. But without accessible on-ramps, the technology sits behind a wall most users can't climb.

James | Snapcrackle

12,279 Aufrufe • vor 9 Monaten

NVIDIA MADE A $249 DESK CHIP THAT TURNS LOCAL LLMS INTO A TINY PRIVATE BACKEND jetson orin nano super is basically a pocket-size ai board: 67 TOPS, 1024-core ampere gpu, 8gb memory, 7-25w power draw. not a gaming pc, not a rented cloud box, just a small nvidia dev kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.

NVIDIA MADE A $249 DESK CHIP THAT TURNS LOCAL LLMS INTO A TINY PRIVATE BACKEND jetson orin nano super is basically a pocket-size ai board: 67 TOPS, 1024-core ampere gpu, 8gb memory, 7-25w power draw. not a gaming pc, not a rented cloud box, just a small nvidia dev kit running llama, mistral, qwen and deepseek from your own table that changes the cost structure. the same automations people casually run through openai for $100-200/month can move the boring work local: summaries, drafts, code checks, document q&a, scraping cleanup, private agents. the board is $249 once, then around $2/month in electricity the setup is almost stupid. install ollama, pull a 7b model, aim your app at localhost. your workflow still talks to an openai-style api, but now the request never leaves the room and the bill does not grow every time a script loops no, it is not replacing claude for the hardest 20%. but it absolutely attacks the expensive background layer people forgot they were paying for every day cloud ai made people rent intelligence by the token. nvidia is quietly showing what happens when that meter sits under your desk instead.

Gipp 🦅

39,609 Aufrufe • vor 4 Monaten

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

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

ZEUS⚡️

37,467 Aufrufe • vor 3 Monaten

Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.

Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.

Ihtesham Ali

2,535,750 Aufrufe • vor 2 Monaten

Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.

Palantir sells governments a war room that costs millions a year. So, a guy named Elie just rebuilt it, put it on GitHub, and gave it away. It's called World Monitor. Open it and you get a live 3D globe with 500+ news feeds pouring in across 15 categories, all summarized by AI as they land. > Military movements. > Economic shocks. > Natural disasters. > Cyber incidents. > Flight paths. > Shipping lanes. + 56 different map layers you can stack on top of each other. It scores 31 countries on a stress index and updates the number as things happen. It watches 29 stock exchanges, commodities, and crypto in one panel. It runs local AI through Ollama, so you can use the whole thing without a single API key. Native desktop app for Windows, macOS, and Linux. 25 languages. Works out of the box after one clone.

Kyronis

809,729 Aufrufe • vor 1 Monat

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

THIS DEVELOPER CONNECTED 8 NVIDIA DGX SPARKS INTO ONE CLUSTER - AND RAN AN 800GB MODEL THAT MADE HIM 10X MORE PRODUCTIVE 21:47 he says it straight - "this is a terabyte of VRAM - we ran Quen 3.5, 800GB on disk, a model that doesn't even fit on a single Mac Studio - 24 tokens per second - I'd say that's a win" 8 Sparks connected through a $1,300 switch via RDMA over Ethernet - each node adding 128GB of memory into one unified pool of 1TB started with one Spark at 3 tokens per second - every added node doubled the speed - and eight together deliver 24 tokens on a model that physically cannot run anywhere else Kimi K2 at 600GB loaded in 15 minutes, 115GB per node, 13 tokens per second - a model that simply cannot run on anything smaller Claude helped configure the entire cluster - SSH mesh across all 8 machines, network config, jumbo frames, QSFP port speeds - all from one terminal most people rent cloud compute for models this size at $2,000+/month - he built the cluster once and now every token costs 20x less

Noisy

186,592 Aufrufe • vor 3 Monaten

I built a powerful real-time edge terminal specifically for Polymarket - multi-timeframe dashboard covering all coins! (100% FREE & fully open-source) In one sentence: This Python bot gives you live alpha on Polymarket’s Up/Down crypto binaries by fusing real-time Binance order flow, current Polymarket probabilities, and multi-TF technical analysis. Spot mispricings fast - where the market odds haven’t yet caught up to momentum, aggressive delta, or strong signals. Perfect for lightning-fast 15-minute scalps (markets resolve every 15 min with constant repricing) or cleaner swings on 1h / 4h / daily horizons. Pure decision-support tool - no auto-trading, just sharp, actionable insight delivered straight to your terminal. > Coins covered: BTC, ETH, SOL, XRP > Timeframes: 15m, 1h, 4h, daily - All 16 market combinations are live and heavily traded on Polymarket (especially the 15m contracts - ultra-fast flips) Features at a glance: > Streams live trades + full order book from Binance > Pulls real-time Up/Down prices & depth via Polymarket WebSocket > Computes 11+ indicators on the fly > Rolls everything up into a clear BULLISH / BEARISH / NEUTRAL bias score + probability estimate per timeframe > Displays a clean, colorful, auto-refreshing terminal dashboard Order-book signals: > OBI (imbalance) > visible buy/sell walls > liquidity depth (0.1% / 0.5% / 1.0%) > net flow & volume > CVD (across 1m/3m/5m windows) > 1m delta > Volume Profile + POC Technical indicators: > RSI (14) > MACD (12/26/9) + signal line + histogram > VWAP > EMA 5 / EMA 20 cross >Heikin-Ashi candle streak count Important: this is NOT an auto-trading bot. It highlights where Polymarket odds are lagging real Binance order-flow and multi-timeframe TA - giving you an edge on 15m scalping, 1–4h momentum trades, and daily directional confirmation. Built with: Python (asyncio + Rich/Textual for a slick CLI look) One-line start: python Refreshes every few seconds Completely free & open-source → GitHub repo Want access? Like + RT + drop a reply or quote below - I’ll DM you the GitHub link Don’t miss the edge - especially on those 15-minute markets that flip every quarter hour. Seeing all timeframes at once is real alpha. Here’s to green candles and fat PnL!

st1ne

175,887 Aufrufe • vor 7 Monaten