I wanted to figure out how I could dependably leverage my voice notes in AI-powered workflows. So I built a local pipeline that turns iPhone recordings into structured Obsidian notes, transcribed on your own machine with no audio sent to the cloud.
This talk walks through the architecture and live demo of Memnon: a Python pipeline small enough to read in an afternoon that goes from iPhone mic to AI-summarized Markdown note in under 60 seconds.
The stack: iOS Shortcut, iCloud Drive sync, whisper.cpp for local transcription, OpenAI or Ollama for structuring, and an Obsidian inbox note automated by a macOS launchd agent. A TypeScript MCP server layer lets Claude query your entire note history, a natural extension point for JavaScript developers.
Designed to be forked. Attendees leave with a working repo, a one-command setup script, and a clear surface for building their own AI knowledge workflows.
Repo: https://github.com/eitanfire/memnon
I wanted to figure out how I could dependably leverage my voice notes in AI-powered workflows. So I built a local pipeline that turns iPhone recordings into structured Obsidian notes, transcribed on your own machine with no audio sent to the cloud.
This talk walks through the architecture and live demo of Memnon: a Python pipeline small enough to read in an afternoon that goes from iPhone mic to AI-summarized Markdown note in under 60 seconds.
The stack: iOS Shortcut, iCloud Drive sync, whisper.cpp for local transcription, OpenAI or Ollama for structuring, and an Obsidian inbox note automated by a macOS launchd agent. A TypeScript MCP server layer lets Claude query your entire note history, a natural extension point for JavaScript developers.
Designed to be forked. Attendees leave with a working repo, a one-command setup script, and a clear surface for building their own AI knowledge workflows.
Repo: https://github.com/eitanfire/memnon