Lettered – a daily phrase puzzle game

Hacker News

Hey all! I'm a big fan of daily puzzles and came up with my own game called Lettered. Imagine you took a wheel of fortune phrase and made it solvable via tetris pieces. I love making things that people enjoy using and feels magical to interact with. The age of ai has been incredible to develop quick games like these but I still think there needs to be a human touch to it. I curate all the puzzles myself and found that agents still get the nitty gritty details of optimizing touch events to be lacking. It's been live for about 6 months now and has been played more times than I ever imagined with over 100k games played in 1M moves by 22k people from all over the world! Figured I'd share it here too in case anyone else is a lover of daily puzzle games :) Lmk if you think of any improvements you want to see or if you want to curate a puzzle yourself! (The puzzle generation algorithm is also a bit neat in case anyone is interested)

Visit product View on Hacker NewsJun 26, 2026

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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

83%83% 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.

Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent, using · Missing: mac, macos, cursor

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

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

44%44% predicted probability of success on TrustMRR, 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

40%40% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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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