A

A simple open-source Android puzzle game

Hacker News

A simple open-source Android puzzle game

Hello HN! I made a simple puzzle game for Android! Link: https://play.google.com/store/apps/details?id=fr.neamar.turnmeon I built "Turn me on" as a tribute to the old games of before: back then, you didn't have to log in every day, you didn't have to invite three friends to play, you didn't have infinite progression, you didn't have to watch ads til kingdom come. The game would just build on top of a basic idea, and once you're done with the game, it wouldn't try to get you back at any cost every week. This is a one time experience, kinda like an escape game. See this game as digital detox: once you've finished the 43 levels, you're done. You can resume your work, or go back to your life, hopefully a little happier than before. Turn me on is also open-source, and implemented in Flutter! If you're curious, you can visit https://github.com/Neamar/turn_me_on/ Feedback welcome!

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Actual performance

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1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, google · Missing: mobile apps, ios, personal
52%52% 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 · Missing: https docs, excited, just released
47%47% 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 · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps, open · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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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