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Triplit, a new daily word game

Hacker News

Triplit, a new daily word game

My partner and I built a daily word game that has elements of the Mini Crossword, Wordle, and Spelling Bee, with some twists all its own. I’ve been fairly obsessed with the New York Times crossword puzzle ever since I saw the documentary Wordplay about 12 years ago. I started with Mondays and gradually made my way up to Saturdays, and when I began to appreciate the puzzles as aesthetic objects worthy of appreciation and criticism (rather than just a slog to survive and then brag about), I began to wonder what it would be like to create puzzles. But making full crossword puzzles was always very daunting to me. Luckily, my partner got an idea for a new word game while on vacation in Italy over-caffeinated on espresso, and boom! Triplit was born. We feel that after the smashing success of Wordle, the time is ripe for a new quick daily word game. To us, Triplit combines the playful, at times cryptic clueing of crosswords with the mental manipulation of Spelling Bee and the feedback and scoring system of Wordle. It also introduces the concept of random clue order, so unlike a crossword you don’t know which clue applies to which answer. And Triplit also has multiple difficulty level settings to make it fun for a variety of players. It has been so fun to build and iterate on! We have more features and improvements planned. We hope you enjoy playing, and would love to hear your feedback. And yes, we know there is a Triplit database, and we mean no disrespect to them :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% 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
33%33% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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