Artificial
A shelf of AI and optimization algorithms — chess, evolution, satisfiability, search.
Story
This is where I keep the classic AI and optimization algorithms — the ones every course gestures at and few people sit down and actually implement end to end. Each is its own small, runnable program behind a unified Python CLI, in Python or C++ depending on what the problem rewards.
The one at the top is the chess engine: minimax with alpha-beta pruning, in C++, because search this deep wants the speed. Next to it lives a shape packer that uses an evolutionary algorithm to cram 2D shapes into as little space as possible, a SAT solver that treats Boolean satisfiability as another population to evolve, a match-3 solver, a hill climber for 2D function optimization, and a minimax tic-tac-toe that never loses.
They share a theme: most interesting problems are search problems, and the art is in how you prune. Alpha-beta throws away chess lines that cannot matter; evolution keeps the packings and assignments that score well and mutates the rest. Same instinct, different clothes. It is MIT- licensed and meant to be read — every module has a runnable demo and its own documentation.