Tincture – A Color-Matching Puzzle Game I Vibe-Coded with AI in 4 Hours

Hacker News

Hi, Eight years ago, I had an idea for a color-matching puzzle game. I taught myself Swift and Xcode, spent months building an MVP… and then abandoned it. Last week, I decided to revisit the concept to see how far AI coding tools have come. So I played around with an assistant, and vibe-coded a playable version in about 4 hours. It’s called Tincture. It’s a calm, minimalist puzzle game. There are no timers, or ads, just a grid of colors and some simple rules that prioritize efficiency and pattern retention. What began as a random thought, turned into something that’s actually pretty satisfying to play. Turns out you can use math (modular arithmetic) to optimize the solution of the basic mechanics, so I added some complexity in later levels. I followed what felt fun and used a free AI-powered IDE to reach an MVP that would've taken me MUCH longer to dust off my Swift version or start from scratch learning JavaScript. I’d love your thoughts: • Is the core mechanic intuitive? • Did it hit a “this is kinda fun” moment? • Is it worth polishing further, or should it stay a weird little side project? Thanks for taking a look!

Visit product View on Hacker NewsMay 25, 2025

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

82%82% 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 HuntUnlikely to reach the leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews

40%40% 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

39%39% 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 · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

11%11% 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.

Correct prediction on native model

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