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AI · July 28, 2026

Minute – Offline meeting notes on macOS with Whisper and llama.cpp

Hello HN, I built Minute because I wanted searchable meeting notes without sending recordings or transcripts to a cloud service. It captures microphone and optional system audio, transcribes locally with Whisper, and generates summaries, decisions, and action items using a local LLM through llama.cpp. After the initial model download, everything runs on-device and in-process. There’s no account, server, or telemetry. The app is built with Tauri, Rust, React, and Metal acceleration. Notes are stored locally as plain text, JSON, and WAV files. I built the project end-to-end with Codex as a…

Minute – Offline meeting notes on macOS with Whisper and llama.cpp launched on July 28, 2026 with 12 votes, #1,321 of 24,623 launches that month and more than 90% of that year's launches.

Visit github.com ↗Alternativestop 5% of July 2026

In plain words

Minute is a macOS app that records meetings and creates searchable notes entirely on a user's computer. It captures audio from the microphone and optionally system audio, then transcribes and summarizes the recording locally using Whisper and llama.cpp. The app extracts decisions and action items automatically. All processing happens on-device with no cloud uploads, accounts, or telemetry, and notes are saved as plain text, JSON, and WAV files. It is built with Tauri, Rust, and React.

written from the facts on this page · September 2026

From the sources

Fully offline meeting notetaker for macOS — records, transcribes, and summarizes on-device with Whisper and a local LLM. Meeting detection, system-audio capture, ask-your-notes with timestamp citations. No cloud, no accounts. - mraza007/minute

In the maker’s words, at launch

Hello HN, I built Minute because I wanted searchable meeting notes without sending recordings or transcripts to a cloud service. It captures microphone and optional system audio, transcribes locally with Whisper, and generates summaries, decisions, and action items using a local LLM through llama.cpp. After the…

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