Exploring how constraint-based machine learning models can generate design assets from natural language prompts and geometric constraints.
A custom AI model that translates design intent into vector graphics and type patterns. Unlike mainstream generative systems, this approach uses mathematical constraints to ensure design elements adhere to geometric harmony and typographic principles.
Implements geometric rules for design proportion and alignment
Applies typographic variations while maintaining legibility
Uses Prolog-based constraint engines to enforce design rules while maintaining creative expression.
Fine-tuned GAN models trained on 19th century design patterns and 21st century vector art.
Prompt: "Victorian-style geometric pattern for window blinds"
Prompt: "Minimalist Japanese garden layout using golden ratio"