A botless meeting recorder and summarizer that runs in the browser

Hacker News

I built a browser app for recording calls and turning them into transcripts/summaries without inviting a meeting bot. The current version is BYOK: you provide an OpenAI API key, stored locally in your browser. Recordings/projects stay in browser storage; selected audio/transcript text is sent to OpenAI only when you run transcription/summary. I am testing whether the wedge is real: no bot in the meeting, no SaaS workspace for recordings, summaries/exports afterward. I would appreciate feedback on the trust boundary and whether BYOK is acceptable for a first version. I used it once to record a meeting I didn't want to spend 1 hour on, and just needed the summary - the tool captured the meeting audio even after I turned off the laptop audio.

Visit product View on Hacker NewsMay 22, 2026

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Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: recordings, openai, open · Missing: mac, agents, macos

84%84% 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

54%54% predicted probability of success on Indie Hackers, 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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

20%20% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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