Three Magic Words

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Three Magic Words

Here’s a free, fun, novel five-letter word game for the web! It’s a game I originally wrote for the iPhone in 2010, but wasn’t able to finish before my first child was born. When I left my senior web developer job in September 2021 I figured I would postpone looking for work and finish the game before another 11 years passed, and expose myself to new skills doing it (in this case, Swift). I released it on the App Store in December, then turned my attention to doing a web version — when suddenly Wordle was in The NY Times, and then everywhere. Perhaps foolishly, I plowed ahead and here we are. Like Wordle and some other NY Times word games, there is a single daily puzzle, but like traditional crossword puzzles, it gets harder throughout the week.

Visit product View on Hacker NewsFeb 8, 2022

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

64%64% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% 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 · Missing: mobile apps, ios, personal

39%39% 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

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

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

32%32% 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

15%15% 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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