$ show norvig/pytudes
This repository is a collection of educational Python programs and Jupyter notebooks by Peter Norvig, focused on algorithmic problem-solving, puzzle-solving, and data analysis. It is not a deployable application or a formal research pipeline, but rather a set of self-contained explorations that demonstrate a wide range of programming techniques: constraint propagation, recursive search, dynamic programming, Monte Carlo simulation, n-gram language models, and symbolic computation. The code is well-structured, clearly documented, and often includes inline tests and performance benchmarks. While each notebook or script is independent, many contain reusable utility functions and algorithmic patterns that could be extracted for other projects. The repository includes data files (word lists, n-gram counts, bike ride logs, Sudoku puzzles) that are used as inputs for the explorations. The primary value is educational: it teaches how to think algorithmically and write clean, efficient Python code.
The LLM identified 5 files worth highlighting for reuse:
A classic Sudoku solver using constraint propagation (arc consistency, naked pairs) and depth-first search. The code is clean, well-documented, and demonstrates a general pattern for solving constraint satisfaction problems. It can be reused as a library for any CSP with similar structure.
The famous spelling corrector that uses a probabilistic model (word frequency and edit distance) to suggest corrections. It is a self-contained module that can be reused in any text-processing pipeline. The algorithm is simple yet effective, and the code is a model of clarity.
A complete Scheme/Lisp interpreter written in Python, including tokenizer, parser, environment handling, and evaluation. It demonstrates how to build a language interpreter from scratch and is reusable as a teaching tool or as a base for domain-specific languages.
A notebook containing reusable utility functions for Advent of Code problems, including Grid class, A* search, priority queue, and various parsing helpers. These functions are generic and can be extracted into a standalone module for any grid-based or pathfinding problem.
A notebook that defines utility classes for prime generation (sieve), prime factorization, divisor counting, and totient function. These are well-tested and can be reused in any number-theoretic computation.