Phone Booth Privacy – Call Spam and Scam Protection for Android

Hacker News

Hi HN, I have been working on this app for a while now and released it today it as Early Access to get feedback. Call spam is one of the major problems that I face everyday and that I want to solve for myself. Just to give a background, I’m a very private person, using barely any social media (usually 1-to-1 messaging apps or no-login lurker), and hate ads, dark patterns and dumbing down stuff for users by hiding away information. As such, I've been developing this app while ensuring it passes my own bar for my personal use. It is bare-bones as of now, and provides basic call management and block list. I’m working to add more stuff to it, including advanced blocking features and caller ID. This is a product-of-one, and will always remain so. Please download it from Google Play Store for Android and let me know your feedback. There is no sign-up required, and even though the Data Sharing section on Play Store page and Privacy Policy mentions about data collection (like Call Log), the backend hasn’t been integrated in this version of the app, so, it is completely offline / standalone. https://play.google.com/store/apps/details?id=com.hyprgeek.p... Thanks,

Visit product View on Hacker NewsNov 22, 2022

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2points

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% 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, including · Missing: https docs, excited, just released

40%40% 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

32%32% 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

15%15% 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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