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Tidy Baby is a SET game but with words

Hacker News

Tidy Baby is a SET game but with words

Hi HN — Tidy Baby is a new game made by me and Wyna Liu (of NYT Connections!) that is inspired by the legendary card-based game SET that we assume many of you love (we too love SET). In SET, you’ve got four dimensions: shape, number, color, and shading, each with three variants. In Tidy Baby you only have to deal with three dimensions: - word length (3, 4, or 5 letters) - part of speech (noun, verb, or adjective) - style (bold, underline, or italic) Like in SET, you are trying to form sets of three cards where, along each dimension, the set is either all the same or all different. If you’ve never played SET there are more details/examples at “how to play” in the game. The mechanics of Tidy Baby are sort of inspired by a solitaire/practice version of SET I sometimes play where you draw two random cards and have to name the third card that would make a valid set. In Tidy Baby you are presented with two “game cards” and a grid of up to nine candidates to complete a valid set – your job is to pick the right one before the clock runs out. Unlike in SET, you get points for “partial” sets where your set is valid on one or two dimensions (but not all three). It’s actually a pretty fun challenge to try to get only sets that are invalid along all three dimensions. In building the game, we were sort of surprised that the biggest challenge was ensuring that all words were unambiguously one part of speech. You’d be surprised how hard it is to find three-letter adjectives that are not also common verbs or nouns. We did our best! We’ve got three “paces” in the game: Steady, Strenuous, and Grueling (s/o MECC!) Let us know what you think!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
34%34% 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: new · Missing: mac, agents, macos
28%28% 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
18%18% 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.

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