This project implements a scalable NxNxN Rubik’s Cube solver & algorithm explorer in Python.
It supports:
✅ “Patched” refers to correcting OLL parity and PLL parity on even-layered cubes (4x4, 6x6, etc.), which do not exist on odd cubes.
On cubes with even layer count (4x4, 6x6, …), the reduction to 3x3 can leave:
The “patched” algorithm applies specific move sequences to fix these without breaking solved centers/edges. nxnxn rubik 39scube algorithm github python patched
cube = RubikNNN(3) # 3x3x3 cube.move("U") cube.move("R'") cube.move("U2") print(cube)
piece = cube.get_piece("corner", ("U", "R", "F")) print("Piece:", piece)
Patched Python implementations of NxNxN Rubik’s Cube algorithms on GitHub significantly improve stability, speed, and correctness over original versions. The most active and reliable patched solver is dwalton76’s fork with community patches, supporting N up to 11. Future work should focus on N > 20 via numpy vectorization or JIT compilation (Numba). This project implements a scalable NxNxN Rubik’s Cube
Appendix: Useful Commands
# Find all patched forks
gh search repos "rubiks cube NxNxN solver" --language=python --fork=true
Solving an NxNxN cube is an extension of the 3x3x3 problem.
Common approaches:
For large N (e.g., 100x100x100), practical solvers use commutators and move sequences rather than optimal solvers. ✅ “Patched” refers to correcting OLL parity and
The search terms you provided likely refer to the dwalton76/rubiks-cube-NxNxN-solver
, a popular Python-based tool on GitHub for solving Rubik's cubes of any size (tested up to 17x17x17).
While "39scube" and "patched" may refer to specific forks or community modifications (such as those used in Kaggle competitions or for specific speed-solving benchmarks), the standard setup for this algorithm is as follows: 1. Prerequisites & Installation
You will need a Linux/Unix environment (or WSL on Windows) as the solver relies on and C++ components for speed. Clone the Repository
