AI Engineering
Practical AI projects, experiments, tutorials, and applied research notes
From Simulation to Prediction: Learning Fluid Dynamics with AI
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…
AI-Assisted Protein Analysis: From Sequence to Drug-Binding Hypothesis
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…
Building a Persona-Driven Survey Engine Using AI
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, in a Bottle. A Robot, with Imagination. And an AI That “Knows Everything.”
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…
Deep Learning Concepts for Beginners : Cooking Pasta Analogy
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,…
Deep Learning Concepts for Beginners : Basketball Player Analogy
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….
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems.The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.
