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Perlin noise Wang tile generator

Hacker News

Perlin noise Wang tile generator

This is a little script I wrote a while ago to help generate sprites for a game I was working on. You can use it to build a seemingly-infinite aperiodic noise texture that is actually composed of a relatively small number of tiles. For example, imagine a large grassy field or body of water in a 2D pixel art game, but without the obvious repeated patterns that arise from simple tiling. The use case is pretty niche (for example, in many cases you could just evaluate the perlin noise function directly in a shader) but I thought it was a cool idea regardless.

Visit product View on Hacker NewsJun 27, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

53%53% 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 · Missing: mobile apps, ios, personal

48%48% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

18%18% 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

17%17% 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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