In this paper, we bridge algorithmic and AI art by adding functionality to the creative coding environment. We create two systems that demonstrate how AI features can enhance algorithmic art and, conversely, how AI art can be styled based on algorithmically-generated artifacts. The first library, GenP5, extends p5.js to allow the artist to apply diffusion models to style and ‘condition’ their algorithmically-constructed art. The second, P52Style, can learn the ‘style’ of an algorithmically generated artifact and apply that when creating new AI art. We provide all the code, demos, and art examples at https://github.com/KolvacS-W/GenP5-P52Style .