Local meeting notes transcriber, but linuxy

Hacker News

Granola started clamping down on downloading your notes without paying for a subscription, and I got sick of all the barely maintained GUIs on top of various local models (MacWhisper ). All-Ears is a set of composable tools to transcribe and summarise meetings. - A daemon that records audio sources - `transcribe` to transcribe the audio (with diarisation) - `cleanup` to clean the transcription, using known word dictionary - `summarize` to summarise the transcript You can switch out models for transcription or diarization. You can use whatever prompt you want for cleanup and summarization. One feature I'm still working on: There's a browser extension. It automatically starts and stops a meeting transcription when you join a Google meet/zoom/teams in the browser. It extracts the audio streams from each participant, so the transcription engine gets audio for each participant rather than the combined audio. Also it automatically gets their name out of the DOM so the transcript has names assigned automatically. I'd also like a MacOS menu bar icon, and it's built to allow any front-end to hook in, but that's for later. If you like the idea, stars and PRs are welcome.

Visit product View on Hacker NewsJul 29, 2026

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

93%93% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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