Live audio dubbing from any browser tab

Hacker News

Hey HN, at Pinch we've been working on something to reduce friction to getting accurate, fast audio translation for live media. Initially we launched a desktop app that sat between your hardware mic/speakers and your meeting platform, but this required users to install our audio driver which introduced permissions hoops to jump through as well as stability issues across updates, not to mention trying to get our device driver approved by Microsoft.. So today we're launching Pinch Sidecar, which offers speech to speech translation in-browser for any Chrome browser tab. This is accomplished by sharing the tab audio with our page, where we proceed to mute the original tab and mix in our translated audio with speaker-matched voices. We also offer meeting creators using Google Meet, Zoom, or Teams the ability to host a translated meeting where listeners can listen and interact in their own language through our Sidecar. The tab translation mode is open for free testing for this HN launch. Our ask is just to let us know how it goes, and to share the link to anyone you know organizing multilingual webinars/meetings.

Visit product View on Hacker NewsSep 10, 2026

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Actual performance

3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos

97%97% 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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

46%46% predicted probability of success on TrustMRR, 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

45%45% 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

19%19% 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, introduce · 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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