Chucker is a production-grade Android library for inspecting HTTP(S) traffic in-app. It acts as an OkHttp interceptor, capturing full request/response details (headers, bodies, timing, errors) and persisting them to a local Room database. The library provides a built-in Material Design UI for browsing, searching, filtering, and exporting transactions (HAR 1.2, plain text, curl commands). It is designed as a debug-only tool via a no-op variant for release builds. The architecture follows a clean separation: public API (ChuckerInterceptor, ChuckerCollector, BodyDecoder), internal data layer (Room entities, DAO, repository), support utilities (okio stream manipulation, JSON formatting, background services), and a full set of UI fragments/adapters. The codebase is well-tested (unit tests for interceptors, DAO, repository, utilities, ViewModels) and follows modern Android development practices (Kotlin, Coroutines, LiveData/Flow, ViewBinding, Compose sample). It handles edge cases like compressed bodies, large payloads, binary formats (protobuf), GraphQL, and SSL/TLS information. The project also includes CI/CD pipelines, automated dependency updates via Renovate, and migration guides for previous versions. While primarily an application library, it has a strong data storage component (Room database) and export functionality (HAR generation), making it useful for debugging network issues in any Android app.
igraph is a high-performance C library for complex network analysis and graph theory. It provides a comprehensive suite of graph algorithms including centrality measures (PageRank, betweenness, eigenvector), community detection (Leiden, Infomap, label propagation, walktrap), isomorphism (VF2, BLISS), shortest paths, flows, cliques, motifs, graph layout (Fruchterman-Reingold, Kamada-Kawai, DRL, Sugiyama), and random graph generation (Erdos-Renyi, Barabasi-Albert, stochastic block models). The library is designed for efficiency and portability, with a stable C API that serves as the backend for higher-level interfaces in R (igraph), Python (python-igraph), and Mathematica (IGraphM). The architecture is a monolithic C library with optional C++ components for performance-critical algorithms (e.g., PRPACK for PageRank, DRL layout, BLISS isomorphism). It includes a rich set of internal data structures (vectors, matrices, sparse matrices, heaps, tries, prefix sum trees) and a robust error handling framework using IGRAPH_CHECK and IGRAPH_FINALLY macros. The library supports multiple graph file formats (GML, GraphML, Pajek, LGL, NCOL, DL, DIMACS) and provides attribute handling for vertex/edge metadata. Key strengths include extensive testing (unit tests, regression tests, benchmarks, fuzzing via OSS-Fuzz), reproducible random number generation (PCG, MT19937), and a well-documented API with semantic versioning. The codebase is actively maintained with frequent releases and a clear deprecation policy. While primarily a C library, it integrates external solvers (ARPACK, LAPACK, GLPK) and bundled vendor code (cliquer, bliss, plfit) for specialized algorithms.
The Infomap repository provides a software implementation of the Infomap algorithm for multi-level network clustering based on the map equation. It offers a standalone command-line interface and language bindings for Python, R, and JavaScript, enabling users to detect community structure in various network types (including weighted, directed, bipartite, multilayer, and higher-order networks). The core algorithm is implemented in C++ with a modular design separating optimization, I/O, configuration, and output handling. The architecture emphasizes reusability and extensibility, featuring a parameter catalog system for managing options, a console utility for formatted terminal output, and safe string conversion utilities. The system supports multiple input and output formats, integrates with popular graph libraries (NetworkX, igraph), and includes advanced features such as entropy correction, teleportation, and regularization. Comprehensive test suites, benchmarking tools, and documentation ensure reliability and ease of use across different platforms and programming languages. While the repository includes example networks and tutorial notebooks for demonstration and validation, its primary focus is delivering a robust, reusable tool for network analysis rather than conducting specific data research. The codebase is actively maintained with continuous integration, regular releases, and cross-language binding consistency checks.
picosnitch is a Linux daemon for per-executable network bandwidth monitoring using eBPF (CO-RE) and fanotify. It tracks which executables make network connections, records bytes sent/received, timestamps, and metadata (SHA256 hashes, optional VirusTotal and GeoIP lookups). The system provides real-time and historical views via multiple interfaces: a curses TUI (both live top and historical browser), a web UI, and a UNIX socket live feed. The daemon uses a multiprocess architecture with privilege-separated subprocesses for monitoring, event processing, database writes, notifications, and external API queries. It supports local SQLite storage and optional remote SQL (MariaDB/MySQL/PostgreSQL) for tamper-evident logging. The repository also includes a comprehensive comparative benchmark suite (bench/) that tests picosnitch against nine other network monitors across 24 scenarios, with a reusable ToolAdapter framework for running controlled experiments.
JA4+ is a multi-protocol passive network fingerprinting suite that generates deterministic fingerprints for TLS, HTTP, SSH, TCP, DHCP, X.509, and latency characteristics. The repository provides four independent implementations: a Python reference library, a Rust CLI tool and library, a Wireshark post-dissector plugin, and a Zeek plugin (C++ BiF + Zeek scripts). All implementations share the same fingerprinting algorithm, test pcap files, and expected output snapshots, enabling cross-validation. The system processes raw packet captures (PCAP/PCAPNG) via tshark or direct packet parsing, extracts protocol handshake parameters (cipher suites, extensions, HTTP headers, SSH key exchange algorithms, TCP flags, DHCP options), normalizes them (GREASE removal, sorting, hashing), and outputs structured JA4+ fingerprints. External dependencies include tshark (for PCAP parsing in Python and Rust), Wireshark/Zeek APIs for plugins, and the ja4db.com database for fingerprint-to-software mapping. The architecture is a collection of standalone tools rather than a unified service, with each implementation optimized for its target environment (CLI, packet analyzer, network monitor). While primarily an application toolkit, the repository also includes research-oriented assets such as a fingerprint-to-software mapping CSV, technical documentation with diagrams, and a large corpus of real-world test traffic, making it a hybrid of production tooling and security research.
CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) is a full-stack web application for collaborative connectomics research. It enables neuroscientists to trace neuron skeletons, annotate synapses, and analyze neural circuits across massive image stacks. The backend is a Django monolith with a REST API, PostgreSQL/PostGIS database, Celery for async tasks, and WebSockets for real-time collaboration. The frontend is a large JavaScript codebase using jQuery, Three.js, Pixi.js, D3, and custom widget system. Key features include skeleton tracing tools, 3D visualization, NBLAST neuron similarity, connectivity matrices, and review workflows. The codebase is well-structured with many reusable client-side utilities (event system, command pattern, caching, settings manager) and demonstrates strong engineering practices with CI/CD, tests, and Docker deployment. However, it carries some technical debt from vendored libraries and complex database migrations.
datafusion-dft (dft) is a batteries-included Rust application that wraps Apache DataFusion into a multi-front-end SQL query and analysis tool. It provides four interfaces sharing one execution core: a scriptable CLI (file/command queries, benchmarking, FlightSQL client mode, CSV/JSON/Parquet/Vortex output), a ratatui terminal UI with SQL editor, paginated results, and history tabs, an Arrow FlightSQL gRPC server with full metadata and prepared-statement support, and an HTTP REST server. The core library (crates/datafusion-app) constructs a DataFusion SessionContext with a trait-based extension plugin system, dedicated CPU/IO executors, statistics collection, and observability hooks.
The chromeHAR repository is a single-page application built with AngularJS (v1.0.8) that serves as a HAR (HTTP Archive) viewer, mimicking the Chrome Developer Tools Network tab. It allows users to load HAR files via drag-and-drop, file input, or URL parameter and displays detailed network request/response data, including timings, sizes, and a waterfall chart. The application follows a modular structure with separate controllers, services, and directives, and uses Grunt for build tasks. Key entry points include index.html (bootstraps the app), js/script.js (initializes drag/drop and app setup), js/controllers.js (contains the main NetworkCtrl for handling HAR data and UI state), and js/libs/HAREntry.js (processes individual HAR entries). The project relies on external dependencies such as AngularJS, jQuery, and various AngularJS modules, along with compatibility libraries like es5-shim and json3.