I'd bet that screen recordings + mouse clicks + keyboard inputs made their way into the training data too.

What tool did you use to make the art on your website?

Your style relies heavily on recursion and similarities between scales, which conv nets are not good at, but programatic descriptions of images like LOGO are very good at. My strategy would be to manually write simple LOGO, python (or whatever tool you initially used) programs that generate each of the images on your site, and then prompt Chat-GPT with “write a program that generates an image combining ideas from <Program A> and <Program B>

The people want to know

You have to learn by doing, but you can do a surprising amount with small data, which will mean you can implement a paper and learn a whole lot faster since you aren't waiting on training. For example, if all you have is MNIST:

Supervised MLP classifier

Supervised convolutional classifier

Supervised transformer classifier

MLP GAN

Convolutional GAN

Gan regularizers (W-GAN, GAN-GP, etc- https://avg.is.mpg.de/publications/meschedericml2018 is mandatory reading + replicate experiments if you want to work on GANs)

Variational Autoencoder

Vector quantized variational autoencoder (VQVAE)

Diffusion model

Represent MNIST Digits using an MLP that maps pixel x, y -> brightness (Kmart NeRF)

I've done most of these projects (still need to do diffusion and my vqvae implementation doesn't work) and they each take about 2 days to grok the paper, translate to code, and implement on MNIST (~6 hours of coding?) using pytorch and the pytorch documentation + reading the relevant papers. very educational!