Core systems
CChess
Small, direct engine work built around explicit state, deterministic search, and performance-aware implementation.
A pure C chess engine focused on board state, search, pruning, and evaluation with minimal abstraction.
- Keeps the code close to the machine, with explicit state handling and minimal abstraction overhead.
- Centers the engine around search, pruning, and evaluation rather than framework structure.
- Stays focused on deterministic behavior and debuggable control flow.
C / engine logic / minimax / alpha-beta pruning / board evaluation
Search layers
1. Board state and move logic stay explicit so search behavior remains understandable and debuggable.
2. Minimax and alpha-beta pruning form the decision core for move selection.
3. Evaluation logic scores positions through heuristic reasoning rather than heavyweight abstraction.
4. I kept the project focused on deterministic control flow and performance-aware implementation.
Language
Pure C
Domain
Chess engine
Core techniques
Minimax, alpha-beta pruning, and evaluation heuristics
Portfolio role
Low-level systems signal outside AI
Why I keep it in the portfolio
Keeps the implementation small enough to reason about directly.
Emphasizes memory, algorithms, and debugging discipline in a plain C codebase.
Shows the same systems mindset as the AI projects without relying on model tooling.
Engineering notes
CChess is a compact chess engine I wrote in plain C. The point of the project is not breadth. It is to keep the implementation direct enough that board state, search behavior, and evaluation logic all stay visible in the code.
Core focus
The engine is built around the classic problems that make chess programs interesting:
- board state management
- move generation
- search
- pruning
- evaluation
That combination keeps the project grounded in explicit state and algorithmic decision-making. There is very little abstraction hiding what the engine is doing.
Implementation approach
I kept the code close to the problem:
- board and move logic stay explicit so search behavior remains inspectable
- minimax and alpha-beta pruning drive move selection
- evaluation remains heuristic and lightweight instead of buried behind unnecessary structure
- deterministic control flow makes the engine easier to debug and reason about
That is what I wanted from the project: a small codebase where the important parts stay understandable.
Result
CChess is a straightforward systems project centered on logic, search, and implementation discipline. It is smaller than the AI work, but it shows the same preference for explicit control over opaque abstraction.