Guava is Google's production-grade core Java library, consumed as a dependency by thousands of projects. It provides high-performance collection types (immutable collections, multimaps, multisets, bimaps, tables), concurrency utilities (ListenableFuture, Futures, RateLimiter, Monitor, Service), a graph model and traversal APIs, caching, hashing, I/O, math, primitives, and string utilities. There are no application entry points, web endpoints, or CLI tools; the repository's "entry points" are the public APIs under com.google.common.*. Architecturally it is a multi-module Maven project with separate JRE and Android flavors, plus GWT/J2CL emulation sources and a dedicated test-support module, guava-testlib. The codebase demonstrates exceptionally high craftsmanship: lock-free concurrency, carefully memory-tuned immutable data structures, custom heap algorithms, and a generic collection-contract testing framework. It is actively maintained with regular releases, GitHub Actions CI, and security scorecard automation. The dual JRE/Android source trees and cross-platform emulation layers require deliberate synchronization, but this complexity is managed rigorously rather than showing signs of abandonment. No data-research or pipeline elements exist; this is a pure library/application dependency, not a data project.
CyberChef is a web-based cybersecurity toolkit for data manipulation, analysis, and transformation. It operates entirely client-side, enabling users to perform operations like encoding, encryption, compression, and data analysis without transmitting data to external servers. The application features a modular architecture where each operation is a self-contained class that can be chained together in recipes to create complex data transformation pipelines. The system is built with a clear separation of concerns: a core processing engine (Chef) handles operation execution, a web interface provides user interaction, and web workers offload intensive tasks to maintain responsiveness. Operations define explicit input/output types, and a dish type system ensures safe data handling across transformations. The project includes a Node.js API for programmatic access and a comprehensive test suite to ensure reliability. Key reusable components include the Operation base class (enabling a plugin architecture for new transformations), the Utils library (providing encoding/decoding and conversion utilities), and the Dish type system (abstracting data handling). The project demonstrates strong engineering practices with active maintenance, low technical drift, and a focus on security and performance through client-side processing and worker-based offloading.
HAITI (HAsh IdenTIfIer) is a Ruby library and CLI tool for identifying hash types from input strings. It matches against a comprehensive database of over 675 hash types (including modern algorithms like SHA3, Blake2, and VeraCrypt) using regex patterns. The library provides concise output with hashcat mode numbers, John the Ripper format names, and example hashes. Multiple CLI entry points exist: the main `haiti` command, `haiti-parsable` for script-friendly output, `haiti-fzf` for interactive selection, and `hashcat-haiti`/`john-haiti` for converting hashes into hashcat/JtR formats. The codebase is well-structured, with clean separation between the identification engine, data definitions, and CLI wrappers. It includes comprehensive documentation (man pages, YARD API docs, a docsify site) and is packaged for numerous Linux distributions and Homebrew. The identification logic is straightforward but effective: iterate over a JSON array of prototypes, apply each regex to the input, and return matching types. The data file (`prototypes.json`) is manually maintained and extended with each release.
This repository is a curated collection of machine unlearning research, combining a comprehensive survey paper (in the form of a Kaggle notebook) with executable implementations of several unlearning algorithms across different domains. It includes code for graph neural network unlearning (GraphProjector), federated learning unlearning (FedEraser), and approximate nearest neighbor search with unlearning (UnfoldedSelf-ReconstructionHashing, a fork of ann-benchmarks). The repo serves both as a literature index (taxonomy, evaluation metrics, datasets) and a benchmark hub for reproducing and comparing unlearning methods. While the survey is actively maintained, the algorithm codebases are research prototypes with varying levels of polish and portability.