A finished research run on an example topic, shown in full — the findings, the sources behind them and the product versions are all the run's own output, except that a product name may have been changed where it matched an existing brand.

Back to the run overview AI meeting notes

Experimental

Consensus Protocol

An open local data standard and lightweight sync engine that lets any local-first notetaker (Meetily, Hyprnote, or the many solo repos) export to a shared format, enabling interoperability across the fragmented open-source ecosystem.

01

What to build

  • MVP

    A published open schema plus a reference CLI/library that converts output from three or four popular local notetaker repos into one common JSON transcript+diarization format, with a simple local search index built on top.

  • Why it can be better

    The open-source scene is a long tail of single-maintainer, single-OS repos with no shared format, so users accumulate disconnected local histories; a shared standard turns fragmentation into an interoperable ecosystem rather than requiring one company to rebuild every capture mode itself.

  • Revenue model

    Open-core model: the standard and CLI are free, with a paid hosted (still local-encrypted) sync and search app for teams wanting cross-device consolidation.

  • Target user

    Developers and privacy-focused power users already running open-source local notetakers who want their tools to interoperate.

02

Context

The opportunity this answers

Build a local-first meeting-memory layer for professionals that unifies transcripts from any notetaker or capture mode into one private, searchable history.

Why now

A wave of open-source local-first notetakers built on Rust/Swift/Tauri has already proven demand for on-device processing, with diarization and Whisper models now standard, high-download building blocks anyone can bundle, while search interest for generic 'AI meeting notes' has plateaued — signaling the crowded cloud tier is saturated even as privacy-driven local alternatives keep multiplying, and none of them yet unify capture contexts, ingest rival platforms' exports, or sync back out privately.

User problems surfaced

4 findings8 sources

Back to the idea
  1. 01

    Privacy backlash against cloud notetakers

    Users are turning to open-source, on-device meeting note apps explicitly because they distrust cloud vendors with sensitive recordings and transcripts.

    Evidence

    Minutes advertises being 'build with privacy in mind, meaning Minutes will never sell or provide third-party' access, and multiple new local-first repositories market themselves purely on '100% local processing, no cloud required' as their differentiator.

    Sources apps.apple.com (opens in a new tab)

  2. 02

    Fragmented capture across contexts

    No single tool cleanly covers every capture context, so people juggle different apps for video calls, in-person meetings, lectures, and phone calls.

    Evidence

    Otter offers bot, bot-free desktop, and mobile capture as separate modes; Summary AI and Minutes separately tout support for lectures, podcasts, phone calls, and uploaded video alongside meetings, implying no default mode covers all of them.

    Sources otter.ai (opens in a new tab) pub.towardsai.net (opens in a new tab) apps.apple.com (opens in a new tab) summary.ai (opens in a new tab)

  3. 03

    Notes locked in platform silos

    Meeting notes end up siloed inside whichever platform captured them, undermining the promise of searching across a person's whole meeting history.

    Evidence

    Google Meet's Gemini notes are emailed and attached only to that meeting's Doc/Calendar event, while Notion routes recaps to Slack/email inside its own workspace, keeping each vendor's notes separate from the others.

    Sources knowledge.workspace.google.com (opens in a new tab) notion.com (opens in a new tab)

  4. 04

    Comparison overload for buyers

    Buyers face decision fatigue from a crowded field of near-identical tools, forcing manual side-by-side comparisons before committing.

    Evidence

    A hands-on review benchmarks 8 tools including Otter, Fathom, and Fireflies, and a separate discussion has practitioners actively cross-shopping Fireflies against Sembly AI feature by feature.

    Sources simular.ai (opens in a new tab) reddit.com (opens in a new tab)