About
This is a collection of technical explorations in deep learning and machine intelligence. The articles here aim to build intuition about complex topics through clear explanations and interactive demonstrations.
I write about attention mechanisms, interpretability, scaling laws, and whatever else catches my interest in the rapidly evolving world of AI research. The goal is to take dense academic papers and distill them into something more approachable without sacrificing technical depth.
The Format
These articles are inspired by Distill.pub's commitment to clear scientific communication. You'll find margin notes for tangential details, interactive visualizations where they help build intuition, and code examples that you can actually run.
Unlike traditional blog posts, articles here are meant to be reference material. I update them as my understanding improves and as the field evolves.