UMAP
2 stories filed under UMAP on Beyond Market Intelligence. The newest of them: “Uncovering Hidden Structure: How Font Embeddings Bloom Into Visual Patterns” and “Automated feature engineering evolves with genetic algorithms and open-source simplicity.”. When Dylan Berndt fed thousands of Google Fonts through a custom neural network and compressed the resulting embeddings into color and position, something unexpected bloomed: a flower. Feature engineering remains the quiet battleground where tabular ML models are won or lost. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every UMAP story on Beyond Market Intelligence, newest first.
Uncovering Hidden Structure: How Font Embeddings Bloom Into Visual Patterns
When Dylan Berndt fed thousands of Google Fonts through a custom neural network and compressed the resulting embeddings into color and position, something unexpected bloomed: a flower. The structure emerged organically, with cursive fonts clustering in the stamen. It's a striking reminder that pre-trained models can reveal visual relationships we didn't design them to find. For more on how data can uncover hidden patterns, explore how language models learn to copy context with hash tables.
Automated feature engineering evolves with genetic algorithms and open-source simplicity.
Feature engineering remains the quiet battleground where tabular ML models are won or lost. py-evoFE (v0.3.0) takes a different path: genetic algorithms that evolve compact, high-impact feature recipes instead of brute-forcing thousands of noisy combinations. Its hierarchical chaining, Polars-powered vectorization, and multi-fidelity screening are thoughtful engineering. For anyone tired of manual transformations or explosive feature spaces, this library invites exploration. It is practical, open-source, and built for the workflows teams actually use. That is worth a closer look.