Scryptogram – A game where you fill in the blanks to guess the subject

Hacker News

Hey HN! I built Scryptogram, a puzzle game where you’re given a category and a summary written in blanks, and you fill them in to reveal the subject. I’ve always loved crosswords, Wordle, and trivia, but wanted something that combines the “aha!” moment of trivia with the letter-filling satisfaction of word games. Each puzzle starts with a category (e.g. “Fictional Character”) and a blanked-out summary. As you guess letters, the words slowly fill in, and once you figure out the subject you can keep completing the rest of the puzzle. I built it with React on the frontend and AWS (S3 for puzzles, DynamoDB for stats + Archive, Cognito for auth) on the backend. I’m still experimenting with puzzle difficulty, retention features, and daily packs. You can try it here: https://scryptogram.com - all feedback is welcome!

Visit product View on Hacker NewsAug 30, 2025

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Actual performance

2points

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.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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 · Strong signals: crypto · Missing: web3, chat, cryptocurrency

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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