I made a game for learning world flags and capitals

Hacker News

I’ve always loved geography, and due to my childhood interest in football, I learned a lot of country flags and capital names. A lot, but not all of them! So, I decided to make a free game for anyone who enjoys flags and wants to improve their knowledge. You can compete against others because each game is timed and results are displayed on the leaderboard. I launched this game just over a month ago, and since then, it has received numerous major updates, including: - User profiles - Personal statistics - Capital-to-country quiz - Daily challenges Have fun!

Visit product View on Hacker NewsAug 22, 2024

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

70%70% 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: personal, month, way · Missing: mobile apps, ios, entrepreneurs

56%56% 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, including · Missing: https docs, excited, just released

50%50% 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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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

13%13% 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

1%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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