Alt – A local AI lecture/meeting notetaker
Most AI notetakers have a 300 minute limit. This is not enough for uni students like me, or people who do remote work. So, I made a notetaker that runs the ASR model on-device. - Free with local ASR & LLM models - No transcription time limit - High accuracy for non-english speech, support for 100 languages - Real-time transcription - Zoom/Google Meet support - No internet needed - Efficient battery usage (~6 hours on full charge on my M2 Pro) This started as a small cli tool I made for myself, then many of my uni friends really liked it, so here it is as an app! I hope a lot of people…
What it does
In the maker’s words, at launch
Most AI notetakers have a 300 minute limit. This is not enough for uni students like me, or people who do remote work. So, I made a notetaker that runs the ASR model on-device. - Free with local ASR & LLM models - No transcription time limit - High accuracy for non-english speech, support for 100 languages - Real-time transcription - Zoom/Google Meet support - No internet needed - Efficient battery usage (~6 hours on full charge on my M2 Pro) This started as a small cli tool I made for myself, then many of my uni friends really liked it, so here it is as an app! I hope a lot of people can use this in their classes or meetings to help with their work. During the development process, I also made the fastest streaming ASR pipeline on Apple Silicon. You can see it here: https://github.com/altalt-org/Lightning-SimulWhisper
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- IBI built a system for active note-taking in regular meetings like 1-1sDec 2025 · withdocket.com · ▲177
Hey HN! Like most here regular meetings have always been a big part of my work. Over the years I've learned the value of active note taking in these meetings. Meaning: not minutes, not transcriptions or AI summaries, but me using my brain to actively pull out the key points in short form bullet-like notes, as the meeting is going on, as I'm talking and listening (and probably typing with one hand). This could be agenda points to cover, any interesting sidebars raised, insights gotten to in a discussion, actions agreed to (and a way to track whether they got done next time!). It's both useful…
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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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