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I built an app for when I talk too much in online meetings

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I built an app for when I talk too much in online meetings

Hey HN! Alexis here, I’m a product manager and software developer in Berlin by way of New York. I want to show you this app I made – It’s like a "buddy" for those, like myself, who inadvertedly talk too much in meetings. The app gives me feedback and a little more in control of what I have influence over by: * Keeping track of how long I’ve been speaking * Catching myself before I talk too much * Developing a better sense of timing I truly love having conversations with people in real-life. But online meetings, especially group calls, tend to make me nervous. I can't read body language. The tone of voice, micro-experessions and social cues get lost. If you, too, accidentally talk too much too often, check it out "Unblah". Watch the quick 2-minute demo and download the macOS app over at https://unblah.me/ . Cheers! Alexis PS: There’s a whole FAQ section for common questions you may have – Including if this is yet another "native" Electron app ;) edit: bullet-list formatting

Visit productView on Hacker NewsJul 14, 2022
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, new · Missing: agents, agent, cursor
85%85% 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: including · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
56%56% 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: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · 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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