This repository is a comprehensive collection of annotated PyTorch implementations of deep learning papers, spanning transformers, GANs, reinforcement learning, diffusion models, and more. Each implementation includes side-by-side notes explaining the code and underlying concepts, making it an educational resource for understanding and reproducing paper results. The codebase is organized by paper/model type, with standalone experiment scripts, reusable modules, and configuration-driven training loops. Key strengths include clean abstractions for transformer components, a flexible configs system, and extensive coverage of both foundational and modern architectures. However, the project has not seen commits since early 2024, some dependencies are outdated, and certain experiments require large GPU resources or external data not bundled in the repo. While individual components are well-crafted and reusable, the overall collection is best used as a reference and learning tool rather than a production framework.
This repository is a comprehensive collection of examples for the **knitr** R package, demonstrating features such as chunk options, hooks, multiple language engines (Python, Bash, C++, etc.), caching, child documents, and output formats (Markdown, LaTeX, HTML, reST, etc.). It serves as both a tutorial for new users and an integration test suite for the knitr package itself. The examples are self-contained, using built-in R datasets (e.g., iris, mtcars, cars) and simple computational tasks. While not a production application or data pipeline, the repo provides a valuable reference for literate programming and reproducible research with R. It is actively maintained by the knitr author and community.
FSharp.Formatting is a mature, production-grade documentation generation toolchain for the F# ecosystem. It consists of several libraries (Markdown parsing, code syntax highlighting with F# Compiler Service integration, API documentation generation from XML doc comments, literate programming support for .fsx and .ipynb files) and a CLI tool (`fsdocs`) that builds static documentation websites with search, theme toggling, and a live-reload dev server. The system is primarily a developer tool used by F# projects to produce API reference sites and literate documentation. Its architecture is modular: the Markdown parser, code formatter, literate processor, and template engine are separate libraries that can be used independently. The CLI orchestrates these libraries, cracking MSBuild projects for metadata, evaluating code via F# Interactive, and generating output in HTML, LaTeX, Markdown, and Jupyter notebook formats. External dependencies include the F# Compiler Service, Newtonsoft.Json, and a bundled version of Yaaf.FSharp.Scripting. The codebase demonstrates deep integration with the F# compiler's type-checking and symbol resolution APIs, but also shows signs of organic growth: duplicated test data, a legacy C# MarkdownSharp project, and some conditionally compiled code for .NET Framework compatibility.
This repository provides a Claude Code skill for literate programming, along with supporting CLI tools. The skill (SKILL.md) defines a slash command that transforms a codebase into a `.lit.md` file – a single Markdown document that weaves prose, diagrams, and named code chunks together. The actual tools are three TypeScript scripts: `tangle.ts` extracts source files from a `.lit.md`, `untangle.ts` reverse-syncs edits back into the `.lit.md`, and `hook-reverse-sync.ts` is a Claude Code PostToolUse hook for automatic reverse-sync. The architecture is simple: standalone scripts with no external dependencies beyond Bun's runtime. The tangler parses fenced code blocks with `{chunk="name" file="path"}` attributes, builds a dictionary of chunks (supporting additive chunks), and recursively expands `<<reference>>` with cycle detection and indent preservation. The untangler uses a `.lit.map.json` source map to splice edited lines back into the original `.lit.md`, then re-tangles. The code is clean, well-documented, and demonstrates good engineering practices for a small tool. It is not a data research project nor a typical application – it is a developer tool for literate programming workflows.