anarlog – Open-Source AI Meeting Notetaker, On-Device

TL;DR

TL;DR: anarlog is a self-hosted, MIT-licensed AI meeting notetaker that transcribes and summarizes meetings entirely on your machine — no cloud APIs, no accounts, no data leaving your device.

Source and Accuracy Notes

• Project page: anarlog on GitHub
• Direct product site: anarlog.com (redirects to GitHub)
• License: MIT (verified via GitHub license.spdx_id)
• HN launch thread: news.ycombinator.com/item?id=44725306
• Latest release: desktop_v1.0.46 (verified via GitHub Releases API, 2026-06-18)

What Is anarlog?

anarlog (formerly Hyprnote, then briefly “char”) is an open-source AI meeting notetaker that runs entirely on-device. It records meetings, transcribes them locally, and generates markdown notes — all without sending audio or conversation data to any cloud service.

The project started as a response to workplace data privacy concerns: some companies banned certain meeting notetakers, and individuals felt uncomfortable sending sensitive meeting content to third-party APIs. anarlog solves this by keeping every step of the pipeline local.

Key claims from the README (verbatim):

“An open-source AI meeting notetaker that is local-first, privacy-first, and yours to fork. Granola, rearranged.”

“Your data, your disk. Every meeting is a .md file you can inspect, search, and sync through Dropbox, iCloud, Syncthing, or git.”

“Local transcription. Transcription runs on-device, so audio never leaves your machine.”

Setup Workflow

Step 1: Download a Release

Head to github.com/fastrepl/anarlog/releases/latest and grab the installer for your platform:

# macOS ARM (Apple Silicon)
hyprnote-macos-aarch64.dmg

# macOS Intel
hyprnote-macos-x86_64.dmg
```bash

SHA-256 checksums are published alongside each release for verification.

### Step 2: Install and Join a Meeting

Open the app, join your meeting as you normally would. anarlog records and transcribes in real time. Audio stays on your machine.

### Step 3: Bring Your Own LLM

After the meeting, anarlog generates a summary. You configure your own LLM provider in settings:

> "Bring your own AI. Use any LLM provider, including OpenAI-compatible services and local models."

Supported providers (from README): OpenAI, Anthropic, Gemini, OpenRouter, Ollama, LM Studio, or any OpenAI-compatible API. This means you can point it at a local Ollama instance for fully offline summarization.

### Step 4: Notes Are Just Markdown Files

Every meeting produces a `.md` file saved to disk. You own it, you can sync it anywhere, and you can edit it with any text editor.

## Deeper Analysis

### Privacy Model

The privacy architecture has two layers:

1. **Local transcription** — Audio never leaves the device during the recording session. This is the core differentiator from cloud-first alternatives like Otter.ai or Fireflies.ai.
2. **Self-hosted summarization** — You configure your own LLM endpoint. If you use Ollama or LM Studio, the entire pipeline is offline.

### Self-Hosting

To self-host without the desktop app:

```bash
git clone https://github.com/fastrepl/anarlog.git
cd anarlog
# Follow build instructions in README

The repo contains the full source for both the desktop app and the backend pipeline.

Open-Source Forking

anarlog is MIT-licensed. The README explicitly invites commercial use:

“Open source, MIT. Fork it, sell it, or self-host it.”

This is a meaningful differentiator from hosted alternatives that restrict API access or prohibit commercial use of outputs.

Project History

The project has gone through a few name changes:

• Started as Hyprnote (YC S25)
• Briefly rebranded to char
• Split into two projects: char (team’s current productivity app) and anarlog (open-source, local-first path)

The repo maintainers note: “anarlog is not being retired. It keeps the open-source path: MIT-licensed, forkable, self-hostable, and built for local notes you control.”

Practical Evaluation Checklist

• Local transcription — audio stays on device
• No accounts or tracking — no cloud backend, no hosted account model
• Markdown output — .md files you own and control
• Bring your own LLM — OpenAI, Anthropic, Gemini, Ollama, LM Studio, or any OpenAI-compatible endpoint
• MIT licensed — fully open-source, commercial use permitted
• Cross-platform — macOS (Intel + Apple Silicon) at launch
• Self-hostable — clone, build, run your own instance

Security Notes

• Audio data never leaves the machine during transcription (local-only pipeline)
• LLM API calls only occur if you configure a cloud provider; local Ollama/LM Studio keeps everything offline
• No cloud backend means no third-party data retention to worry about
• As with any meeting recording tool, be mindful of consent laws in your jurisdiction

FAQ

Q: Does it work offline? A: Yes, if you use a local LLM provider (Ollama or LM Studio). Cloud LLM providers (OpenAI, Anthropic, etc.) require an internet connection.

Q: What transcription model does it use? A: The README does not specify the transcription engine. Check the releases and repo documentation for the current implementation details.

Q: How does it compare to Granola? A: anarlog is described as “Granola, rearranged” — the same core concept (AI meeting notes) but with a fully local, self-hosted architecture and open-source code. Granola is a hosted, proprietary service.

Q: Can I use this for work meetings on macOS? A: Yes, the desktop app supports macOS (Intel and Apple Silicon). You bring your own LLM API key if using a cloud provider.

Q: Is the code commercial-friendly? A: Yes, MIT license means you can fork, sell, or self-host without restrictions.

Conclusion

anarlog fills a specific niche: AI meeting notes with a strict local-first, privacy-first architecture. For teams that cannot or will not send meeting content to third-party APIs, this MIT-licensed open-source tool provides a credible alternative to hosted services like Granola, Otter.ai, or Fireflies.ai.

The ability to use any OpenAI-compatible LLM endpoint — including fully local options like Ollama — makes it adaptable to both cloud-heavy and air-gapped environments. Download the latest release from the GitHub releases page to get started.