Migas – Meeting copilot with live speaker labels (no bot, no cloud STT)

Hacker News

I used to use Granola for my meetings, but the one thing it couldn't do was tell me who said what. So I built Migas, which does voice fingerprinting on device to provide real time speaker labels. Because it knows who's speaking in real time, the AI can do things that transcript-only tools can't: "What did Sarah commit to?", or "Based on what you know about the CTO from our last three meetings, what question should I ask right now?" It builds speaker profiles across meetings, so the context compounds over time. It captures system audio via macOS APIs, runs speech-to-text locally using Moonshine on Apple Silicon, and does speaker identification with TitaNet neural embeddings, all on-device. The only thing that touches the cloud is AI chat, and only when you explicitly ask it a question, and then it only sends transcript text, never audio. Built with Rust/Tauri for the native app, React for the UI, and a Python sidecar for the ML pipeline. Works with any meeting platform (Zoom, Meet, Teams, whatever) since it just listens to system audio. No calendar access, no integrations, no account required. Free tier has unlimited transcription. I'm a solo dev and would love feedback on what could be improved.

Visit product View on Hacker NewsMar 31, 2026

Share card

Actual performance

1points

2comments

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apple · Missing: agents, agent, cursor

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, real time, audio · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Contain Yourself: my debrief of Container.Camp as a speaker37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Contain Yourself: my debrief of Container.Camp as a speaker

Hacker News3

Seply46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI Speaker Separation

Indie Hackers1$100/momusic-audio

Always-on Meeting AI by SuperIntern72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Live Notes & Translation for Every Meeting — No Bot Needed

Product Hunt+139Productivity

Speaker Copilot10%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The stage timing copilot for live speakers

Indie Hackers1communication

WalletLabels – Turn anon Ethereum addresses into understandable labels43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

WalletLabels – Turn anon Ethereum addresses into understandable labels

Hacker News1

Thingy34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Smart labels for your thingies

Product Hunt+117Home

Zaasmi38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Zaasmi is the undetectable AI meeting copilot for live calls

Indie Hackers1ai

Whisper Snapper for Mac70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Local transcripts with speaker labels, timestamps, + export

Product Hunt+139Productivity

Smart speaker that curses (and lot more)43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Smart speaker that curses (and lot more)

Hacker News7

speaker13%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Top 6 wireless\Bluetooth speaker 2018

Indie Hackersfounders-solo

Browse all products Analyze your launch