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Dozenal – A Game of Spatial Arithmetic

Hacker News

Dozenal – A Game of Spatial Arithmetic

Hello HN! We're Rory and Chris, and we've made a game called Dozenal[0], a grid-based number puzzle game where the objective is to use all the tiles on the board to create totals of 12 (hence the name). We think the mechanic is quite unique, although keen to hear if people know of similar games! You essentially drag tiles across the board, and the distance between two numbers decides the arithmetic operation when they connect to each other: zero spaces is addition, one space is subtraction, two spaces is multiplication: e.g. 5 → 4 = 9 5 → _ → 4 = 1 5 → _ → _ → 4 = 20 We've spent the past month getting people to test the game. The two biggest points of feedback are: 1. People generally find the learning curve a little too steep -- we are open to any feedback on how to make the rules clearer or easier -- but we've found that almost everyone that sticks with it for more than a couple of days can solve the puzzles. 2. People find that "mixing" arithmetic is confusing -- you can combine up to two arithmetic operations (e.g. addition and multiplication) in a single chain to reach a "12 total", but there's a catch in that you can only do this for one move. We release a new puzzle every day (all of them are set by Chris), and our aim is to make something that people enjoy coming back to and that strikes the right balance between a time-killer and a bit of a challenge. A bit of backstory: Chris and I came up with the idea over a decade ago while we were students. We were always tinkering with prototypes but never released anything. AI coding assistants made it a lot easier to work on the game in the past couple of months (it's a mixture of human and AI development, not fully vibe-coded). Give it a try and let us know what you think! [0] - https://dozenal.game

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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 HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
68%68% 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
45%45% 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: month, way · Missing: mobile apps, ios, personal
45%45% 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
23%23% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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