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Good Night Hue (iOS, Ruby Motion, Pro Motion, Teacup, ...)
Hacker NewsGood Night Hue (iOS, Ruby Motion, Pro Motion, Teacup, ...)
Hi there, I made my first iOS app - https://itunes.apple.com/us/app/good-night-hue/id788303232 It just schedules your Philips Hue lights to turn off. It is built on Ruby Motion, Pro Motion, Teacup and other goodies. Really thanks everybody for these tools. I wonder, do you think doing support on GitHub is ok when I plan to make it Open Source? Btw any suggestions regarding the usability/bugs are welcomed. Thx Michal
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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 · Strong signals: apple, open · Missing: mac, agents, macos77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released31%31% 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, revenue19%19% 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, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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