Clippy – screen-aware voice AI in the browser
A friend and I built a browser prototype that answers questions about whatever’s on your screen using getDisplayMedia, client-side wake-word detection, and server-side multimodal inference. Hard parts: – Getting the model to point to specific UI elements – Keeping it coherent across multi-step workflows (“Help me create a sword in Tinkercad”) – Preventing the infinite mirror effect and confusion between window vs full-screen sharing – Keeping voice → screenshot → inference → voice latency low enough to feel conversational We packaged it as “Clippy” for fun, but the real experiment is letting…
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A friend and I built a browser prototype that answers questions about whatever’s on your screen using getDisplayMedia, client-side wake-word detection, and server-side multimodal inference. Hard parts: – Getting the model to point to specific UI elements – Keeping it coherent across multi-step workflows (“Help me create a sword in Tinkercad”) – Preventing the infinite mirror effect and confusion between window vs full-screen sharing – Keeping voice → screenshot → inference → voice latency low enough to feel conversational We packaged it as “Clippy” for fun, but the real experiment is letting a model tool-call fresh screenshots to help it gather more context. One practical use case is remote tech support — I'm sending this to my mom next time she calls instead of screen sharing. Curious what breaks.
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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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Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
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