I created an Opposites Game for my daughter and wanted to share:)

Hacker News

Hello All, After going through my daughters prep interview process, the teachers advised that she needed to sharpen up on her opposites. After searching the Google Play store and being a little disappointed at the state of the opposite games, I decided to create my own version for her (I also wanted an excuse to make a kids game :) So without further ado, I give you the opposites game! :) https://oppositesgame.com/ A couple of notes: * I didn't create a scoring system as I didn't want to give the sense of "pressure" in the game * A couple of ideas have popped up in my head about how I can make a spin on the game (maybe a bejewled style gameplay where you flip cards to match them?) * I am in the process of creating an iOS version for the app store * Would love thoughts, ideas and feedback Other than the above, I hope you all enjoy :)

Visit product View on Hacker NewsNov 12, 2019

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

62%62% 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.

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, notes · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, 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

21%21% 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, revenue

17%17% 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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