Create sharable AI chats for your documentation site in seconds
If you have developer documentation and want to boost your community with AI this is for you! Just pull in the base url of the site add some customization and get a sharable link for your chat, link it anywhere you want. I saw this trend in some places like gcp with Gemini, or Langchain or Supabase ask ai, but they're all custom-implemented solutions, not everyone wants to advocate developer resources to create the rag, deploy it and maintain it, you just want devs to build with your stuff, the more they can do the better, the quicker the better, and if they get a smooth experience while…
What it does
In the maker’s words, at launch
If you have developer documentation and want to boost your community with AI this is for you! Just pull in the base url of the site add some customization and get a sharable link for your chat, link it anywhere you want. I saw this trend in some places like gcp with Gemini, or Langchain or Supabase ask ai, but they're all custom-implemented solutions, not everyone wants to advocate developer resources to create the rag, deploy it and maintain it, you just want devs to build with your stuff, the more they can do the better, the quicker the better, and if they get a smooth experience while doing it that's legendary already. Just some clicks!! With one base URL you are halfway through. Check it out at https://explainit.mzslabs.com
Does the same job
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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