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StandBy Mode Pro – An Android Take on iOS 17 StandBy
Hacker NewsStandBy Mode Pro – An Android Take on iOS 17 StandBy
- Player control - Duo widget UI with editing options - Multiple digital/analog watch designs with smooth an fun animations - Weather updates - Calendar integration - Timer - Night Mode, scheduled night mode and light sensor activation - Countdown to special dates - Quick Launch - Burn In Protection - Photo frame - Focus Mode - Smooth animations for all features I have been developing this app for 6 months now part time. Feedback is very welcomed.
Share cardActual performance
1points
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting82%82% 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 · Missing: mac, agents, macos65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, month · Missing: mobile apps, personal, entrepreneurs52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released29%29% 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, revenue25%25% 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, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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