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Quad – A Daily Tile Puzzle

Hacker News

Quad – A Daily Tile Puzzle

I woke up with the idea for this game in a dream. After waking, I spent the next two hours in a recliner refining the idea, then I shot off a message to some friends to see who wanted to collaborate. My friend, Jacob, bit! He helped with the design and made all the art. This is my first Svelte project. The most technically challenging part is the level generation script, which I wrote in rust because it needed to be 100x faster than my initial typescript version. Every puzzle has 1 solution. You might notice some theory from a classic game transfers well to Quad, but with enough difference to keep you on your toes :)

Visit product View on Hacker NewsSep 30, 2025

Actual performance

1points

3comments

Did not reach leaderboard

Launch Intel predictions

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

66%66% 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.

TrustMRRLess likely to generate early MRR · 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

43%43% predicted probability of success on AppSumo, 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

41%41% 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 · Missing: mac, agents, macos

26%26% 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 · Strong signals: collaborate · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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