Zazow – Generative Art Toy (fractals, spirograph, satori, etc.)

Hacker News

Im finally ready to share a fun side project that I've been working on. For 20 years, Ive been fascinated with Mandelbrot fractals. Back in the day, I made a MacOS9 app that rendered them, and I missed it. So, I decided to bring it up to the modern age and build a website to explore this and other similar fractals. In the process, I discovered some other types of generative art that looked fun, so I made those too. One is a spirograph, another is similar to an old AfterDark screensaver called Satori (anyone remember that?). Others draw paint splatters and random squiggly lines. The user is given a bunch of interactive settings to control how the artwork is generated. Im hoping to continue adding different styles of generative art to the website over time. Its a fun, relaxing project to work on and I hope other people enjoy using it to create pleasing images.

Visit product View on Hacker NewsJul 22, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, user · Missing: agents, agent, cursor

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% predicted probability of success on Indie Hackers, 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

12%12% 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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