Coding Adventure: Rubik's Cube

Sebastian Lague’s latest Coding Adventure walks through building a program that solves a standard 3x3 Rubik’s Cube. The project starts with modeling the cube, then adds move handling, a binary state representation, and search/optimization work to make solving feasible.

The video is useful because it’s not just about “solving a puzzle,” it’s about how you represent complex state and how that representation affects the quality and speed of your solver. Lague compares multiple approaches, including a greedy solver, a sandwich solver, and an oriented solver, which makes the tradeoffs between heuristic strength and implementation complexity very concrete.

For game developers, the takeaways map directly to AI and systems work: compact state encoding, heuristic evaluation, and iterative refinement of search strategies. Even though this is a puzzle project, the same patterns show up in pathfinding variants, procedural systems, and any feature where you need to search a large combinatorial space efficiently.

He also notes a correction about the evaluation function at 22:32: the fallback to the greedy evaluator included a small bonus for correctly oriented corners. That kind of detail is a good reminder that tiny scoring changes can materially affect solver behavior, especially when heuristics are layered or reused.