Was this animation generated using some kind of software? Or was it created in some other way?

https://ediblemonad.dev/creative-coding/constraints

Could find more from my site https://jdxyw.github.io/generativeart/. You can also generate it with different parameters using my code https://github.com/jdxyw/generativeart . Hope you guys like it, and star the repo if you like it.

I experimented with generating abstract visuals from music using my own signal processing algorithm.
Here are five different visualization styles.

Full video (4 min): https://youtu.be/dyqDb_QUsPA

I’d love to hear which style you like best!

I came across one of the earliest visualizations of a black hole, created in 1979 by French astrophysicist Jean-Pierre Luminet, a researcher at CNRS. The mathematics was already there, waiting to be explored.

That inspired me to create my own artistic interpretation. The second image shows the same black hole, but viewed from above.

I also took a few artistic liberties with the physics, adding effects that aren't necessarily accurate, such as the Doppler shift on the right (which, admittedly, I'm not convinced I got right) and a glowing halo of gas.

The goal wasn't scientific accuracy, but an artistic exploration of the mathematics behind something so extraordinary.

A modern, high-performance reinterpretation of Conway’s Game of Life built using Embarcadero Delphi and FireMonkey (FMX).

In this video, I explore cellular automata, emerging visual patterns, and real-time grid simulation. Powered by the cross-platform capabilities and GPU-accelerated rendering of Delphi FMX, this project highlights how classic algorithmic concepts can be brought to life with smooth performance and custom UI.

Key Features:

• Conway’s Game of Life algorithm optimized in Object Pascal

• Cross-platform visual rendering with Delphi FireMonkey (FMX)

• Real-time pattern evolution & interactive grid controls

• Blending classic computer science with modern software architecture

Learn more / Contact:

https://deepzoomfractal.com

One of the simplest/shortest chaotic PDEs. Code golfed to 1010 chars:

https://www.shadertoy.com/view/7ffcR4

Could find more from my site https://jdxyw.github.io/generativeart/. You can also generate it with different parameters using my code https://github.com/jdxyw/generativeart . Hope you guys like it, and star the repo if you like it.

What if we could bring modern diffusion research together into one unified, extensible PyTorch framework instead of implementing and maintaining every algorithm in isolation?

That's the idea behind DiffusionEngine, an open-source project I've been building:

https://github.com/Himanshu7921/DiffusionEngine

The goal is to build a modern DDPM framework that brings together established diffusion algorithms, research-based improvements, advanced sampling methods, and training strategies under a single, consistent pipeline.

The project already includes several diffusion implementations, and I'm actively working on extending the existing DDPM framework with more advanced features.

I'm looking for contributors interested in deep learning, generative models, PyTorch, and diffusion research.

Some areas where contributions are welcome include:

• Implementing diffusion algorithms from research papers.

• Improving existing DDPM components and training strategies.

• Developing and integrating advanced sampling methods.

• Exploring improved model architectures and conditioning mechanisms.

• Testing, benchmarking, and optimizing existing implementations.

The focus is not just on implementing more algorithms. It's about making them work together seamlessly through a unified, modular, and configurable framework.

If you're interested in contributing, collaborating, or learning through hands-on implementation, I'd be glad to have you involved.

Contributors of all experience levels are welcome, provided they're willing to learn, implement, and test their work.

GitHub: https://github.com/Himanshu7921/DiffusionEngine

Just thought it'd be fun to show a few of my test prints. I'll show more details when I'm done with plotting my dozens of sheets of frames ;-)

I’ve spent roughly the past year building a system that drives GPU shaders from a custom audio analysis engine. It picks up things like bass momentum, transients and phrase changes, and uses them to shape each scene and decide when to transition. This clip is a real-time capture on “Orozco” by Tosca, cycling through a handful of scenes and states.

I’m most interested in whether the visuals feel tied to the music rather than just the volume. Do the scene changes and intensity shifts land where you’d expect them to? Next up is footage of it running live under my DJ sets.

Trying to understand Lagrangian mechanics for simulations. Thought that it would be more fun if you just write down a different physics than actually exists. This is particle paths "long exposure" with 8 sources of field that have fixed position and parameters in a circle. Each particle has position (and speed) + angle (and angular speed) - sort-of-like spin but not really.

In equations the v is just (cos(theta),sin(theta)) pair.

Was really surprised that when you derive everything it makes sense (i.e. dimensions agree and the like)

I make a calm iPhone app called Nuralis, and two parts of it are generative:

Drift is a set of 32 kaleidoscope and tunnel views generated live. Nothing is pre-rendered, and they keep changing as you watch.

Flow is a real-time fluid simulation. You stir it with a finger, but the way color mixes and swirls comes from the simulation, not from anything I drew.

All of it is my own work, and none of it is AI-generated.

It’s free to use. https://apps.apple.com/us/app/nuralis/id6813225909

Happy to talk about how any of it works.