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Pinch – macOS voice translation for real-time conversations

Hacker News

Pinch – macOS voice translation for real-time conversations

Hey HN! I’m Christian, daily lurker and some might remember our original launch post ( https://news.ycombinator.com/item?id=42935355 ). Today we're launching Pinch for Mac, which we believe is a step-change improvement in real-time AI translation. Our vision is to make cross-lingual conversations feel as natural as regular conversations. TL:DR During an online meeting, the app instantly transcribes and translates all audio you hear, and allows you to decide when you translate your voice and when you don't. It's invisible to others (like Granola), and works everywhere without any meeting bots. Try it at startpinch.com Here's a live demo we recorded this morning, without cuts: https://youtu.be/ltM2p-SosLc When we first launched Pinch, we shipped a video conferencing solution with a human-like AI interpreter that was an active participant in your call. Our users hold the spacebar down while speaking to the translator, and when they release the spacebar the translator speaks out to the entire room. That design was intentional - it puts the task of context selection on the user and prevents people from interrupting each other awkwardly (only one person can press spacebar at a time). It also comes with heavy tradeoffs, namely: * Latency - Up to 2x longer meeting lengths due to everyone hearing your full sentence and then the translation of your full sentence * Friction with first-time users - Customers using Pinch for external communication often meet with new people each time, and we've learned of several that send out an instruction doc pre-meeting on how to join and use translation in the Pinch call. Bad signal for our UX. * Restricting our customers to those who are meeting creators Benefits of the desktop app: 1. It creates a virtual microphone that you can use in any meeting app 2. Instant transcription+translation means you can understand what's going on in real-time and interrupt where necessary 3. Simultaneous translation - after you start speaking, the others will hear your translated audio as fast as we can generate it, without interrupting your flow. Over the last months our focus has been on developing a model and UX to support high translation accuracy while automating context selection - knowing exactly when it has enough words to start the translated sentence. We’ve rolled this out to the desktop app first. We're incredibly excited to go public beta today, you can give it a try at www.startpinch.com Cheers, - Christian

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor
94%94% 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
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
58%58% 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: users · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, users · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% 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.

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