Notewarp
Turn messy thoughts into clear, reusable notes with AI
What it does
Notewarp turns messy captures into clear, reusable notes. Record a voice note, upload audio, paste text, add a file, scan text, or save a link. It keeps the original transcript beside the cleaned note, then reshapes the same source into summaries, action items, emails, study notes, outlines, and custom writing styles. Organise everything with tags, folders, search, and pinned notes; export it or share a polished public page. Available on the web, iPhone, iPad, and Mac.
Record a thought, upload audio, or paste text. Notewarp uses AI to create clear notes, transcripts, summaries, action items, and shareable exports.
Think out loud and leave with clear notes. Record, upload audio, or paste text. Notewarp keeps the transcript and turns one capture into summaries, action items, drafts, tags, exports, and shareable notes. Capture now. Shape it later. Start messy. Notewarp keeps the source, removes the filler, and gives every idea a useful next form. Record, upload, or paste once. Then clean the words, pick a format, search the source, export the result, or share the version that fits the moment. Capture long-form audio in the moment and keep the original recording attached to the note. Record or upload audio, import a file or link, scan text, or paste and write your own. Turn one capture into a clean note,…from notewarp.com
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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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Life & fun · Aug 2026 · toneyalexander.github.io
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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