This repository is a comprehensive, open-source textbook for data structures and algorithms, known as 'Hello Algo'. It provides implementations of over 200 classic algorithms across 12 programming languages (Python, Java, C++, Go, Rust, etc.) and multiple human languages (Chinese, English, Japanese, Russian). The code is organized into chapters covering complexity analysis, arrays, linked lists, stacks, queues, hashing, trees, heaps, graphs, sorting, searching, divide-and-conquer, backtracking, dynamic programming, and greedy algorithms. Each algorithm is presented as a self-contained, runnable example with driver code and inline comments explaining the logic. The repository is not a production application but a pedagogical reference, making it valuable for students, educators, and self-learners. While the code is well-structured and consistent across languages, its high duplication across languages and translations means it is not designed for production reuse. However, the utility modules for tree/linked list construction and printing are cleanly isolated and can be extracted for other projects.
This repository contains the complete LaTeX source for 'An Infinitely Large Napkin', a comprehensive advanced mathematics textbook by Evan Chen. The project is primarily a document production pipeline: the core content is a collection of modular .tex files organized by topic (algebra, topology, analysis, etc.), with extensive cross-references, custom macros, and Asymptote figures. However, the repository also exhibits strong software engineering practices: it includes a Nix flake for reproducible builds, GitHub Actions CI/CD workflows that compile the PDF and deploy to GitHub Pages, pre-commit hooks for code quality, and helper scripts (export-flowchart.sh, print-toc.py) for generating dependency graphs and split PDFs. This hybrid nature makes it both a textbook source and a well-automated document production application.
This repository is the source and build system for the multi-language edition of the classic computer science textbook *Structure and Interpretation of Computer Programs* (SICP). It contains the full text, exercises, and code snippets in XML, organized by chapter and section, for the original Scheme version as well as modern adaptations in JavaScript and Python. The build toolchain, implemented in TypeScript and JavaScript, parses the XML into multiple output formats: PDF (via LaTeX), a static HTML web edition, a JSON edition for the interactive Source Academy frontend, Markdown, and standalone program files. The system handles cross-references, code snippet extraction with dependency resolution, figure inclusion, index generation, and search index construction. The repository also includes CI/CD pipelines for testing generated programs against actual language runtimes (Node, CPython, MIT Scheme) and deploying the web edition to GitHub Pages. Additionally, AI-generated chatbot knowledge base modules for the Python edition are checked in. The codebase is well-structured with a clean edition abstraction and a modular processing pipeline, though it is tightly coupled to the SICP XML schema and the Source Academy ecosystem.
This repository is the content generation and build system for the interactive textbook *Mathematics in Lean*. It is a hybrid project: primarily a set of Python scripts and a Lean 4 project that together produce the textbook in multiple formats (Sphinx RST/HTML/PDF), as well as a separate user repository with exercise stubs and solutions. The core pipeline parses Lean source files annotated with custom directives (`TEXT`, `EXAMPLES`, `SOLUTIONS`) via `scripts/mkdoc.py` to generate textbook prose and Lean exercise files. A Sphinx build then produces the final HTML/PDF documentation. The Lean source files themselves cover mathematics from basic arithmetic to measure theory, heavily leveraging the Mathlib library. The repository includes CI/CD workflows (GitHub Actions), developer environments (Devcontainers, Gitpod), and testing scripts that verify the generated Lean files compile. While not a traditional application, it functions as a specialized toolchain for creating and deploying formal mathematics educational content.
This repository is a multi-language, open-source linear algebra textbook built with Quarto. It is not a software application or a data research pipeline; it is a documentation project that produces static HTML, PDF, EPUB, and LaTeX outputs. The core code consists of a Makefile for build automation, Python scripts for machine translation of the book content into six languages (Chinese, Vietnamese, Japanese, Korean, Spanish, German), and GitHub Actions workflows for CI/CD and deployment to GitHub Pages. The translation pipeline is the most sophisticated software component, featuring chunking with math/protection, SHA-256 checkpointing for resumability, parallel workers, and verification of translation completeness. The book content itself is extensive (100+ labs) but is structured as Quarto markdown files. The repository is actively maintained with recent commits, a changelog, and issue tracking. The primary value is educational, but the translation infrastructure and Quarto multi-language book pattern are reusable assets for other documentation projects.
This repository is an open-source textbook, 'Machine Learning Systems: Design and Implementation', produced as a static website via mdBook. It is not a running application or data pipeline; rather, it is a documentation project with a custom build toolchain that transforms Markdown chapters (with extended syntax for equations, citations, cross-references) into a bilingual HTML site. The primary code comprises Python scripts for preprocessing Markdown, assembling the publish tree, and ensuring resource symlinks, along with CI/CD workflows for testing and deployment to GitHub Pages. The content covers the full ML system stack including computational graphs, compilers, accelerators, distributed training, and model serving, with both English and Chinese editions. The repository is actively maintained with frequent commits, but technical drift exists due to pinned Python dependencies and incomplete migration to v2 (many chapter placeholders). It is hybrid in nature: primarily a documentation project, but with application-like build infrastructure and reusable preprocessing components.
This repository is primarily an extensive educational compendium covering mathematics, computer science, and artificial intelligence from first principles to state-of-the-art topics. It contains over 100 markdown files organized into 20 chapters, each with detailed explanations, diagrams, and interactive coding tasks (primarily in Python/JAX). The repo also includes auxiliary tooling: a MkDocs-based documentation site with MathJax and a Pyodide in-browser Python runner that includes a JAX compatibility shim, and a TypeScript MCP (Model Context Protocol) server that provides programmatic access to the compendium content for AI assistants. While the vast majority of the repository is educational content rather than runnable software, the MCP server and interactive runner are functional application components. The compendium is well-structured and current, but the code examples are pedagogical (simplified, not production-ready) and the JAX shim is a fragile polyfill. The project is not a data research pipeline—it contains no datasets, experiment logs, or trained models.
This repository is the Chinese translation of the landmark Deep Learning textbook by Goodfellow, Bengio, and Courville. It contains the full LaTeX source of the translated book (20 chapters), a Jekyll-based website for online reading, and scripts to convert LaTeX into Markdown for the website. The repository also includes community annotations per chapter (hypothes.is) for proofreading and translation refinement. It is a static documentation project, not a software application or data pipeline, though it does have a build system (Makefile) and a web deployment pipeline. The translation is largely complete, but the content is based on the 2016 edition and has not been updated since 2019; the project is currently in a stale state with no active development.
This repository is a Jekyll-based static site serving as an open-source Korean textbook on convex optimization, titled 'Convex Optimization for All'. It contains 25 chapters covering theory (convex sets, functions, duality) and algorithms (gradient descent, subgradient, Newton, ADMM, etc.), with mathematical notation and practical examples. The site is built for GitHub Pages, uses the Lanyon theme, and includes comment functionality via Utterances. While the content is comprehensive and well-structured, the repository is primarily a content delivery platform rather than a software application. There is a small Python utility script for GitHub issue management, but the core value is the educational material. The project appears to be in a maintenance stalemate, with most content last updated in 2021 and no recent commits. The architecture is standard Jekyll with no novel software engineering patterns.