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raullen

@Raullen • 62,242 subscribers

Building open intelligence for the physical world. Cofounder @iotex_io. ex-@Google, @Uber, @Zoox.

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now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. Pro now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. 🧠 By returning calibrated probabilities for up to 64 typed questions in a single forward pass, it turns complex judgment into a scalable primitive. ⚡ For decentralized networks, this unlocks a new layer of autonomy at the node level: ⚖️ Consensus of Judgment: IoTeX delegates can independently score device readings and attestations, neutralizing spoofed sensors before they ever touch the chain. 🛡️ MEV Defense: Proposers can locally score the mempool for wash trades and toxic flow, protecting the network entirely in-house. The endgame for IoTeX has always been empowering machines to perceive, verify, and understand the world around them. 🌍 Pushing verifiable intelligence directly to the edge is how we get there. Give it a try 👇

now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. Pro now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. 🧠 By returning calibrated probabilities for up to 64 typed questions in a single forward pass, it turns complex judgment into a scalable primitive. ⚡ For decentralized networks, this unlocks a new layer of autonomy at the node level: ⚖️ Consensus of Judgment: IoTeX delegates can independently score device readings and attestations, neutralizing spoofed sensors before they ever touch the chain. 🛡️ MEV Defense: Proposers can locally score the mempool for wash trades and toxic flow, protecting the network entirely in-house. The endgame for IoTeX has always been empowering machines to perceive, verify, and understand the world around them. 🌍 Pushing verifiable intelligence directly to the edge is how we get there. Give it a try 👇

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Blown away by how capable small decision models are getting. 🤯 I just had a local System One model (Laya) play Snake on my Mac via Rapid-MLX · local AI on your Mac. No cloud, no text gen—just pure real-time decision making. Avg latency: ~312ms. Zero errors. Local agent loops are insanely fast now. 🐍⚡️

Blown away by how capable small decision models are getting. 🤯 I just had a local System One model (Laya) play Snake on my Mac via Rapid-MLX · local AI on your Mac. No cloud, no text gen—just pure real-time decision making. Avg latency: ~312ms. Zero errors. Local agent loops are insanely fast now. 🐍⚡️

12,859 Aufrufe

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🐆 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. ⚡️
0:37
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🐆 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

34,196 Aufrufe • vor 2 Monaten

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx
0:40
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🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

raullen

22,776 Aufrufe • vor 4 Monaten

Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM
0:21
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Hermes Agent Nous Research + Gemma 4 26B Google DeepMind, fully working on local Mac 🤯: one prompt → full CLI app + tests + pytest passing tool calls firing back to back — local Claude Code vibes Gemma 4 MoE is fast AND smart, and tool calling just works — powered by Rapid-MLX which natively parses Gemma 4's tool format (no other local backend can do this yet) Two lines to try it: pip install rapid-mlx & rapid-mlx serve gemma-4-26b pip install hermes-agent && hermes @teknaborat @siaborisov #HermesAgent #Gemma4 #LocalLLM

raullen

24,988 Aufrufe • vor 6 Monaten

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