FindWord – A Challenging Word Game

Hacker News

Hi HN, I’ve built FindWord, a simple yet challenging word game that helps expand vocabulary and sharpen language skills. The game is designed to be fun, engaging, and educational, making it a great tool for both casual players and language learners. Key features: Available on both App Store and Google Play Simple mechanics but progressively challenging gameplay Great for improving word recognition and vocabulary skills You can check out more details here: https://englishtests.online/findword I’d love to hear your thoughts—any feedback or suggestions for improvement are welcome!

Visit product View on Hacker NewsMar 18, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

75%75% 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: google, education · Missing: mobile apps, ios, personal

45%45% 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

43%43% 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 released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

21%21% 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

14%14% 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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