Color Orbs (Generative Art)

Hacker News

Hi all, This is a generative art project I worked on over the past year or so consisting of about 100 little artworks generated using P5.js. I have an overview and technical write up here: https://dannyking.uk/artwork/colororbs You can play with my interactive demo and make your own here: https://dannyking.uk/artwork/colororbs/designer And the gallery is here: https://dannyking.uk/artwork/colororbs/gallery This project came about after reading a HN post about a year ago ( https://news.ycombinator.com/item?id=25712767 ) by Atul Vinayak on generating beautiful 'Noise Planets' and I got kind of obsessed from there!

Visit product View on Hacker NewsMay 2, 2022

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

62%62% 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

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

45%45% 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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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