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A meeting note-taker that talks back.

2,426 starsSwift

OpenGranola – meeting copilot that searches your notes in real time

by yazinsai·Mar 18, 2026·2 points·2 comments

AI Analysis

●●SolidSolve My ProblemNiche Gem

Real-time note retrieval during calls solves a pain Otter and Fireflies ignore.

Strengths
  • Fully offline transcription with Parakeet TDT keeps audio on-device.
  • Hidden window mode stays invisible during screen sharing for discretion.
  • Works with local Ollama models or cloud providers via OpenRouter.
Weaknesses
  • macOS-only limits reach to iPhone and Windows users entirely.
  • Legal consent requirements for recording create friction in many jurisdictions.
Category
Target Audience

Sales reps, researchers, and professionals who take calls with prep materials

Similar To

Otter.ai · Fireflies · Fathom

Post Description

link: https://github.com/yazinsai/OpenGranola

hey HN, I built OpenGranola — a macOS app that sits next to your calls, transcribes both sides of the conversation locally, and surfaces talking points from your own notes in real time.

The idea came from having too many calls where I knew I had the perfect data point or quote somewhere in my notes, but couldn't find it fast enough. I wanted something that would do the retrieval for me, while the conversation is still happening.

How it works:

- Point it at a folder of markdown/text files (meeting prep, research, customer briefs, whatever) - Start a call and hit "Start" - It transcribes both speakers on-device using Parakeet TDT (no audio leaves your Mac) - When the conversation hits a decision point or question, it searches your knowledge base and suggests relevant talking points

The whole thing can run 100% locally — pair it with Ollama/llama.cpp for LLM suggestions and embeddings, and nothing touches the network. Or use OpenRouter + Voyage AI if you prefer cloud models.

A few things I'm happy with:

- The app window is invisible to screen sharing by default, so the other side never sees it - Transcription is fully offline via Apple Silicon neural engine (~600MB one-time model download) - Sessions auto-save as plain text transcripts - MIT licensed, Swift/SwiftUI, ~3k LOC

Tech stack: Swift 6.2, SwiftUI, FluidAudio (on-device ASR), RAG over local files with vector embeddings.

Requires Apple Silicon, macOS 26+. DMG on the releases page or build from source with one script.

Would love feedback — especially on the suggestion relevance and what kind of knowledge base content works best for you.

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