AI Engineering

Can an AI predict how fluid moves?

In this piece, I explore how a physics foundation model—WALRUS—learns to forecast the evolution of a heated fluid system. Instead of solving equations, the model observes patterns and generates its own version of the future. The result is a striking simulation that reveals both the power and the limits of AI in modeling the physical world.

Can AI help us understand how medicines interact with the body?
In this edition, I explore a beginner-friendly experiment where I used AI tools like AlphaFold and AutoDock Vina to go from a protein sequence to a plausible drug-binding hypothesis—all on a laptop. A simple walkthrough, even if you’re new to biology.

What if you could test your product idea before having real users?
In this post, I show how to use AI personas to build a synthetic focus group—generate surveys, simulate responses, and uncover insights early.

Earth Models, World Models, and Global Models are three fast-growing “families” of AI systems that are often mentioned together—but they solve very different problems. This blog provides an accessible, research-oriented map of the landscape for readers who are new to AI (and for technical readers entering the field).

What does learning deep learning have in common with cooking pasta?

More than you might think.

In this article, I explain deep learning concepts for beginners using the familiar process of learning to cook pasta—preparing ingredients, tasting, adjusting, and improving with every attempt. It’s a simple, intuitive way to understand how neural networks train, make mistakes, and get better over time—without heavy math or jargon.

What if understanding neural networks felt as natural as watching someone learn basketball?

In this short read, I explain how a neural network learns—using the simple, familiar journey of a player practicing shots, missing, adjusting, and improving. No heavy math. Just intuition, clarity, and a fresh way to see how AI actually learns.