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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...

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raullen

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33,716 просмотров • 3 дней назад •via X (Twitter)

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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
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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

22,760 просмотров • 2 месяцев назад

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
4:10

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 просмотров • 1 год назад

Thrilled to unveil what we've been cooking the last few weeks: the Gravity AI assistant. Local, free, owned by you. Now available for free on macOS -> GravityAI 🌠 Gravity ensures absolute privacy by keeping all conversations on your device. No compromises, your data is exclusively yours. Plus, with LLaVa and OCR, Gravity gains vision – just snap a screenshot, and it's ready to chat about anything on your screen. Everything has been built to optimize the power of Apple silicon: I used Mistral AI as a base LLM and a in-app instance of ollama. It is insanely fast. Gravity will continue to evolve from here. For example, we're working on speech recognition and speaker diarization to passively learn from your meetings and messages in the background, no integrations needed. Incredibly proud of our journey so far and a big shoutout to my co-founder Tye Daniel for grinding to make this happen (we were up till 4am making sure this ships today) We're hiring folks with talent and passion, and we welcome you to join us on this exciting journey!
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Thrilled to unveil what we've been cooking the last few weeks: the Gravity AI assistant. Local, free, owned by you. Now available for free on macOS -> GravityAI 🌠 Gravity ensures absolute privacy by keeping all conversations on your device. No compromises, your data is exclusively yours. Plus, with LLaVa and OCR, Gravity gains vision – just snap a screenshot, and it's ready to chat about anything on your screen. Everything has been built to optimize the power of Apple silicon: I used Mistral AI as a base LLM and a in-app instance of ollama. It is insanely fast. Gravity will continue to evolve from here. For example, we're working on speech recognition and speaker diarization to passively learn from your meetings and messages in the background, no integrations needed. Incredibly proud of our journey so far and a big shoutout to my co-founder Tye Daniel for grinding to make this happen (we were up till 4am making sure this ships today) We're hiring folks with talent and passion, and we welcome you to join us on this exciting journey!

Sulaiman Khan Ghori

71,430 просмотров • 2 лет назад

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:
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Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

385,706 просмотров • 6 дней назад

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!
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Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,745 просмотров • 4 месяцев назад

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.
1:53

Run your coding agent on a local model, with QVAC. OpenCode now plugs into a model on your own machine through our official plugin. That changes two things. Privacy by default. Your repo, your prompts, and every command the agent runs stay on your device. No third-party model ever sees your code. For an agent that reads your codebase and runs commands, that is the point, not a nice-to-have. New workflows. With no meter and no rate limit, the agent does not have to stop. Let it loop on a task, refactor a whole repo overnight, or run a watch-build-fix loop that keeps going. Setup takes a few minutes: one line in opencode.json and OpenCode runs on a local QVAC model. In the video demo, it handles three real developer tasks back to back, fully offline.

QVAC

11,382,628 просмотров • 1 месяц назад

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.
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QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,931,912 просмотров • 1 месяц назад

🚨 OG YOUTUBER PEWDIEPIE JUST LAUNCHED A FREE, OPEN-SOURCE AI RIVAL TO CHATGPT AND CLAUDE While ChatGPT and Claude charge users for premium features, PewDiePie’s new AI workspace, Odysseus, is FREE. In his own words, "NO tracking, NO subscriptions, and can be run on your OWN computer." Odysseus is essentially “Claude and ChatGPT’s web UI, but SELF-hosted”, allowing users to run local AI models or use APIs while keeping control of their own data. Odysseus includes: - An AI agent that can find, create, edit and run files - Memory that learns from past conversations - Deep research with follow-up chat - An email client that flags urgent messages and drafts replies - A document editor for writing, formatting and fact-checking - Calendar, to-do notes and search tools - An image editor - “Cookbook,” which scans hardware, recommends local models and helps users run them - Mobile support Its BIGGEST selling point is privacy and control. “All your data is yours, as it should be.” He also made a firm commitment on pricing: “This project will never cost any money.” The launch comes just days after PewDiePie announced he was ENDING his vlogs. Retirement arc is wild.
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🚨 OG YOUTUBER PEWDIEPIE JUST LAUNCHED A FREE, OPEN-SOURCE AI RIVAL TO CHATGPT AND CLAUDE While ChatGPT and Claude charge users for premium features, PewDiePie’s new AI workspace, Odysseus, is FREE. In his own words, "NO tracking, NO subscriptions, and can be run on your OWN computer." Odysseus is essentially “Claude and ChatGPT’s web UI, but SELF-hosted”, allowing users to run local AI models or use APIs while keeping control of their own data. Odysseus includes: - An AI agent that can find, create, edit and run files - Memory that learns from past conversations - Deep research with follow-up chat - An email client that flags urgent messages and drafts replies - A document editor for writing, formatting and fact-checking - Calendar, to-do notes and search tools - An image editor - “Cookbook,” which scans hardware, recommends local models and helps users run them - Mobile support Its BIGGEST selling point is privacy and control. “All your data is yours, as it should be.” He also made a firm commitment on pricing: “This project will never cost any money.” The launch comes just days after PewDiePie announced he was ENDING his vlogs. Retirement arc is wild.

Coin Bureau

73,975 просмотров • 2 месяцев назад

🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.
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🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.

Charles Maddock

12,731 просмотров • 3 месяцев назад

For the first time, the latest LLMs run on the Apple Neural Engine — and NexaSDK is the only framework that makes it possible, powered by the NexaML engine. Last year, our two co-founders were invited by Apple DMLI team (Data & Machine Learning Innovation) to share their research about on-device multimodal model for local AI agents. One of the big questions in the room was: “Can the newest LLMs actually run on ANE?” At the time, nobody had a clear path. Today, that path exists. NexaSDK now runs Granite-4.0 (IBM), Qwen3 (Qwen), Gemma3 (Google), and Parakeet-v3 (NVIDIA) fully on Apple’s NPU — unlocking low-power, always-on, fast inference across Mac and iPhone. A new wave of NPU-first local AI apps is coming to Apple devices. Start with one line of code on Mac. iOS SDK coming soon.
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For the first time, the latest LLMs run on the Apple Neural Engine — and NexaSDK is the only framework that makes it possible, powered by the NexaML engine. Last year, our two co-founders were invited by Apple DMLI team (Data & Machine Learning Innovation) to share their research about on-device multimodal model for local AI agents. One of the big questions in the room was: “Can the newest LLMs actually run on ANE?” At the time, nobody had a clear path. Today, that path exists. NexaSDK now runs Granite-4.0 (IBM), Qwen3 (Qwen), Gemma3 (Google), and Parakeet-v3 (NVIDIA) fully on Apple’s NPU — unlocking low-power, always-on, fast inference across Mac and iPhone. A new wave of NPU-first local AI apps is coming to Apple devices. Start with one line of code on Mac. iOS SDK coming soon.

NEXA AI

30,213 просмотров • 8 месяцев назад

Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.
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Sam Altman recently said: “GPT-OSS has strong real-world performance comparable to o4-mini—and you can run it locally on your phone.” Many believed running a 20B-parameter model on mobile devices was still years away. At Nexa AI, we’ve built our foundation on deep on-device AI technology—turning that vision into reality. Today, GPT-OSS is running fully local on mobile devices through our app, Nexa Studio. Real performance on Snapdragon Gen 5: - 17 tokens/sec decoding speed - < 3 seconds Time-to-First-Token Developers can now use NexaSDK to build their own local AI apps powered by GPT-OSS. What this unlocks: - Real reasoning models running locally - The next wave of AI Agent use cases—without cloud limits - True data privacy: your data never leaves your device Appreciation to OpenAI Sam Altman Greg Brockman and the entire leadership team for pushing the boundaries of the open-source AI community. Thanks to Qualcomm for the partnership, and to Manoj Khilnani and Chun-Po (Jerry) Chang for the incredible collaboration.

NEXA AI

197,453 просмотров • 10 месяцев назад

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.
1:13

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.

m0h

14,542 просмотров • 2 месяцев назад

River 2.0 is here and live on Product Hunt 😸 🚀 We rebuilt River from the ground up to work for both massive global and hyper local communities. What's new: 🌍 Invite nearby community members – When you host an event within a community, you can automatically invite nearby community members. 🤝 Grow your following – When you host events inside a community, anyone who registers becomes your follower on River, and then you can invite them to your future events. 🚀 Host an event under your name – Run independent events under your own name and invite your followers directly, for free. We'd love your feedback and support on our launch! I'll post the link in the thread 👇 Thanks to @jason for hunting us both on Product Hunt and in real life 😜 And thanks to all the communities and hosts who've been with us since the beginning while we figure out how to build an enterprise-grade event platform with consumer UX and vibes 🙏
1:43

River 2.0 is here and live on Product Hunt 😸 🚀 We rebuilt River from the ground up to work for both massive global and hyper local communities. What's new: 🌍 Invite nearby community members – When you host an event within a community, you can automatically invite nearby community members. 🤝 Grow your following – When you host events inside a community, anyone who registers becomes your follower on River, and then you can invite them to your future events. 🚀 Host an event under your name – Run independent events under your own name and invite your followers directly, for free. We'd love your feedback and support on our launch! I'll post the link in the thread 👇 Thanks to @jason for hunting us both on Product Hunt and in real life 😜 And thanks to all the communities and hosts who've been with us since the beginning while we figure out how to build an enterprise-grade event platform with consumer UX and vibes 🙏

Rae 🍽️

52,926 просмотров • 1 год назад

QVAC SDK 0.11.0 is live. 🛠️ This release focuses entirely on unlocking next-generation local compute and advanced visual workflows. What’s new: Next-Gen Models: Core engine updated to the latest version of Fabric, unlocking full support for Qwen 3.5, Qwen 3.6, and Gemma 4. Multi-GPU Support: The SDK can now split workloads across multiple graphics cards on the same machine, allowing you to run significantly larger models completely locally. Multi-Image Conditioning: Blend multiple reference images together in a single generation for advanced style mixing and composition control. On-Device Upscaling: Boost your generated images to high-quality resolutions, running securely on your own hardware. More improvements are waiting under the hood. Check the change logs, update your SDK today, and start building with
1:11

QVAC SDK 0.11.0 is live. 🛠️ This release focuses entirely on unlocking next-generation local compute and advanced visual workflows. What’s new: Next-Gen Models: Core engine updated to the latest version of Fabric, unlocking full support for Qwen 3.5, Qwen 3.6, and Gemma 4. Multi-GPU Support: The SDK can now split workloads across multiple graphics cards on the same machine, allowing you to run significantly larger models completely locally. Multi-Image Conditioning: Blend multiple reference images together in a single generation for advanced style mixing and composition control. On-Device Upscaling: Boost your generated images to high-quality resolutions, running securely on your own hardware. More improvements are waiting under the hood. Check the change logs, update your SDK today, and start building with

QVAC

2,006,449 просмотров • 2 месяцев назад

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 просмотров • 3 месяцев назад

Today we’re introducing Superhuman Docs, the evolution of Coda (Superhuman Docs) into the best place for teams and AI to work together. I co-founded Coda to reimagine the modern document and enable anyone to make a doc as powerful as an app. Superhuman Docs builds on that mission, and is reimagined for how teams work with AI now. With Superhuman Docs, AI is no longer a solo experience. Docs AI works with you the way a teammate would, with full context of everything your team has built: your data, your briefs, your roadmaps, your connected tools. You can ask it to summarize a sprint, draft a decision record from your meeting notes, or pull a competitor analysis into a new page, and that work stays right where your teammates can pick it up. I’m incredibly proud of the team for bringing Docs to life! There’s so much more we’re launching, including a way to create custom interfaces on top of your data just by describing it. We’re also introducing Superhuman Databases for teams to manage up to 1 million rows, launching Docs MCP to open it up to the AI tools you already use, and debuting a brand new Mac app. We’ve been building toward this moment for a long time, and Docs starts rolling out today. If you’re an existing Coda user, everything you rely on in your docs will remain the same, and if you’re new, now is a great time to jump in.
4:07

Today we’re introducing Superhuman Docs, the evolution of Coda (Superhuman Docs) into the best place for teams and AI to work together. I co-founded Coda to reimagine the modern document and enable anyone to make a doc as powerful as an app. Superhuman Docs builds on that mission, and is reimagined for how teams work with AI now. With Superhuman Docs, AI is no longer a solo experience. Docs AI works with you the way a teammate would, with full context of everything your team has built: your data, your briefs, your roadmaps, your connected tools. You can ask it to summarize a sprint, draft a decision record from your meeting notes, or pull a competitor analysis into a new page, and that work stays right where your teammates can pick it up. I’m incredibly proud of the team for bringing Docs to life! There’s so much more we’re launching, including a way to create custom interfaces on top of your data just by describing it. We’re also introducing Superhuman Databases for teams to manage up to 1 million rows, launching Docs MCP to open it up to the AI tools you already use, and debuting a brand new Mac app. We’ve been building toward this moment for a long time, and Docs starts rolling out today. If you’re an existing Coda user, everything you rely on in your docs will remain the same, and if you’re new, now is a great time to jump in.

Shishir

23,206 просмотров • 1 месяц назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
2:43

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,129 просмотров • 1 год назад

The Skillful AI platform is now live The place where you can unleash the true power of AI and let your creativity run free 🤖 Get 3X the performance by making up to 3 different LLMs work together for your needs 🤖 Create your custom personalities for the AI agents as detailed as you can dream of, then bring them to life on telegram, discord, or monetize them in our AI marketplace 🤖 Feed your Agents specific knowledge and keep it fully private through 's blind computing technology 🤖 Plug your AI into everything with our Skills and Integrations: From social media like X & Telegram, to agents launching their own tokens via @pumpdotfun, trading tokens on Solana to even making your own AI NPC characters for your games through our SDKs ✅ We’ve got a ton of exciting extra features coming to the platform very soon. We are committed to bringing you the ultimate AI ecosystem 🥂 Ps: Have questions about the platform? Drop a ticket in our Discord, and we’ll be happy to assist 🫡 🌐
0:48

The Skillful AI platform is now live The place where you can unleash the true power of AI and let your creativity run free 🤖 Get 3X the performance by making up to 3 different LLMs work together for your needs 🤖 Create your custom personalities for the AI agents as detailed as you can dream of, then bring them to life on telegram, discord, or monetize them in our AI marketplace 🤖 Feed your Agents specific knowledge and keep it fully private through 's blind computing technology 🤖 Plug your AI into everything with our Skills and Integrations: From social media like X & Telegram, to agents launching their own tokens via @pumpdotfun, trading tokens on Solana to even making your own AI NPC characters for your games through our SDKs ✅ We’ve got a ton of exciting extra features coming to the platform very soon. We are committed to bringing you the ultimate AI ecosystem 🥂 Ps: Have questions about the platform? Drop a ticket in our Discord, and we’ll be happy to assist 🫡 🌐

Skillful AI

113,541 просмотров • 1 год назад

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. rohit and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: (requires your own API key) Source code:
0:34

Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. rohit and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: (requires your own API key) Source code:

Andrew Ng

1,133,980 просмотров • 21 дней назад

MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some 30k tokens), I was seeing sub-150ms TTFT (Time To First Token), that's "instant" for a human. We run this on an office server (one of our Mac Minis) for staff to work with, instant answers on critical, confidential company/client documents on phone, iPads or laptops. Imagine the "What were the 3Q25 profits?", "When did Joe start working for us?", "Please list the project milestones and team count" answered within seconds of hitting the return key. It works with voice too. This example is 17k lines of code from 177 source files plus an index of the entire 1,902 files, the entire Claude Code codebase (oops Anthropic!). Zero loss (unlike Anthropic's security). What this means is the ability to work better on local (private) code bases, off-the-shelf private document analysis with better response than the best public models - all locally.
0:33

MLX + TurboQuant = Local Super Power Take a local (private) document(s) or codebase, pre-fill the 256k KV cache (the context) with the document(s) and system prompt, quantise and run on Apple's MLX and you have almost instantaneous, lossless document queries with total privacy. For a 75-page PDF (some 30k tokens), I was seeing sub-150ms TTFT (Time To First Token), that's "instant" for a human. We run this on an office server (one of our Mac Minis) for staff to work with, instant answers on critical, confidential company/client documents on phone, iPads or laptops. Imagine the "What were the 3Q25 profits?", "When did Joe start working for us?", "Please list the project milestones and team count" answered within seconds of hitting the return key. It works with voice too. This example is 17k lines of code from 177 source files plus an index of the entire 1,902 files, the entire Claude Code codebase (oops Anthropic!). Zero loss (unlike Anthropic's security). What this means is the ability to work better on local (private) code bases, off-the-shelf private document analysis with better response than the best public models - all locally.

John T Davies 🇪🇺

85,589 просмотров • 4 месяцев назад