Before a single piece is placed, the 64 squares must be drawn. This chapter covers the core motivation for building Eschess from scratch.
Chess is Fun! But Also Hard…
I’ve always been drawn to games of all kinds, whether physical or mental, and chess is one of my favorite hobbies. I started playing in high school simply because one of my favorite characters in a show excelled at it, and I quickly became hooked. What makes the game really intriguing is its endless possibilities. Throughout the thousands of games I’ve played, every single one has brought a different experience and feeling—whether that be frustration, excitement, or intense focus.
Lately, my Elo on Chess.com has fluctuated around 1500–1600. Although I didn’t have a specific goal rating in mind, I still wanted to improve my skills. The traditional route would be doing more puzzles, watching tutorials on openings and endgame tricks, and reviewing my own games. But the world is evolving, and I realized there might be a cooler way to understand the game: through the power of code.
My stats on Chess.com, if you ever wonder…
Chess Engine: What is that?
This project was initially inspired by this YouTube video on a guy who coded a chess engine. I clicked on it out of pure curiosity, thinking it would just be fun trivia to learn. But as the video went on, I grew more and more interested in the intricate details of chess engine systems, mainly because they relate so strongly to what I study. As a rising junior at the University of Michigan majoring in Robotics and minoring in CS, my interests lie deeply in the software side of autonomy.
So, what did I already know about software and computer architecture? I had learned programming and data structures in C++ (EECS 280), algorithms (EECS 281), logic design (EECS 270), computer architecture (EECS 370), machine learning (EECS 445), and computer vision (EECS 504). Surprisingly, all these technical courses from my sophomore year turned out to be the necessary “pieces” for building a chess engine!
Armed with these tools, I created the Eschess project with a specific goal in mind: “seeing” how an engine thinks. To understand how it analyzes a position, I programmed the backend from scratch to gain full control. I customized visualization elements, like arrows and highlighted squares in Pygame, to show exactly where the engine is looking during its search.
I want to learn how to analyze a board the way a powerful engine does, which leads to the ultimate question: what makes an engine so strong? Is it pure, crazy brute force? An ML black box? Or just magnificent algorithms?
The short answer? It’s a beautifully engineered collision of all three. A powerful engine relies on magnificent algorithms to cleverly prune away bad moves, raw bare-metal brute force to calculate millions of positions per second, and, increasingly, machine learning to provide the “intuition” to evaluate a complex board state.
That intersection is exactly what Eschess is built to explore. This project isn’t just about creating an opponent to play against; it’s my attempt to bridge raw numerical search, systems programming, and neural networks into one unified architecture. By building it from the ground up, the engine becomes an interactive sandbox designed to expose the hidden gears of how a machine thinks, searches, and decides.
The 64 squares are drawn, and the arena is set. Let’s place the pieces.