Noema — Focus & Calm Sound for Deep Work
AI radio that learns your taste and becomes yours
What it does
Adaptive AI radio for deep work, sleep, and calm. Tap a mood or ask Siri; fully hands-free, and sound starts in seconds. The more you listen, the more it becomes yours. No account needed. iOS.
Adaptive music for deep work, sleep and calm. Tap a mood, sound starts in seconds, then quietly adapts to how you listen. No account needed. iOS.
Tap a mood and a station starts instantly — then quietly adapts to how you listen. Open the app and tap a mood like Focus, Relax, Workout, Sleep, Party, or Energy from the grid. Playback starts immediately with haptics. Enjoy a seamless radio station experience. Sign in to generate custom music with guided modes and advanced prompts. Free accounts start with limited credits and a 2-track library preview. Upgrade to Pro to unlock the full experience and more radio features. Noema quietly learns when you listen and what you reach for — from day one, no account required. It culminates in Your Frequency : a Pro station that plays a mood in your order, around what you actually finish. A station…from melodaistudio.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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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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