Problem
Notetakers summarize after the meeting. Reading a transcript later does nothing for you during the conversation — when you actually want to see the structure forming and catch the action item as it’s assigned.
Approach
A local web app that transcribes the mic in real time and runs two separate model passes. One consolidates the talk into notes — open questions, decisions, action items. The other draws an evolving flow diagram of how the topics connect. The diagram reads from the abstracted notes rather than the raw transcript, so it stays structural instead of noisy, and warm-up topics drop out once the conversation pivots.
Stack
FastAPI WebSocket backend, AssemblyAI streaming transcription with diarization, a gpt-4o-mini notes analyzer and a Haiku diagram model via OpenRouter, and a vanilla-JS front end using an AudioWorklet and Mermaid. No build step, no database — it runs on localhost.
What shipped
End-to-end on localhost: a live transcript, a notes panel, and a Mermaid diagram that all update as you speak, plus one-click export of notes, diagram, and transcript. Backed by a self-test harness of 116 tests that drives the prompts through injected transcripts with an LLM-as-judge eval.
What’s next
Tighter speaker attribution on direct-address handoffs (“Marcus, can you draft this?”), and an auto-iteration mode where the harness reads its own scorecard and proposes prompt changes.