Fpart is a filesystem partitioner (CLI tool in C) that splits file trees into balanced subsets called 'partitions' based on size, file count, or number of partitions. It uses a bin-packing algorithm to optimize space utilization and supports a live mode that generates partitions during filesystem crawling, with hooks to trigger immediate actions (e.g., rsync transfers). The companion tool fpsync (a POSIX shell script) wraps fpart and rsync/cpio/pax/tar to parallelize data migrations across local or SSH workers, providing job queue management, resume/replay, and progress reporting. The architecture is modular: fpart's C source is organized into separate modules for options, file entries, partitions, dispatch, and utilities, with an embedded FreeBSD fts(3) for filesystem traversal. Fpsync is a well-structured shell script that handles job scheduling, worker coordination, and run lifecycle. The project has been used by NASA, Amazon, Microsoft, and others for large-scale data migration.
This repository is an Android TV application (影視TV) that provides a comprehensive media center experience for streaming Video-on-Demand (VOD) and live television. It features a plugin-based content aggregation system using spiders written in Java, Python (via Chaquopy), and JavaScript (via QuickJS), allowing users to browse and play content from multiple external sources. The application supports multiple player engines (ExoPlayer and mpv), hardware/software decoding, subtitle and danmaku (bullet comment) overlays, DLNA casting (both sender and renderer), and a local HTTP server for remote control and file sharing. Architecturally, the project is a multi-module Gradle build with a shared core library (catvod) providing network utilities, cryptography, and data models. The main app module contains two UI tracks: 'leanback' for TV remote control navigation and 'mobile' for touch-based phone interaction, both sharing common logic under app/src/main. The system uses an event-driven architecture with EventBus, a Room-based SQLite database for persistence, and a ViewModel pattern for reactive UI updates. External content sources are integrated through a standardized Spider interface, with loaders for JAR, Python, and JavaScript plugins. Key features include a sophisticated playback controller with fallback policies, preloading, and history management; extensive audio/video effect pipelines (equalizer, loudness normalization, color adjustment); danmaku search and display; EPG (Electronic Program Guide) with timezone-aware parsing; and a comprehensive settings system covering player, decoding, subtitles, and network configuration. The project also includes a documentation website built with Next.js, providing developer guides for the spider API and local HTTP API.
RuoYi is a production‑ready Java rapid‑development framework for building enterprise back‑office management systems (CMS, CRM, OA, etc.). It is a monolithic Spring Boot application using Apache Shiro for authentication/authorization, MyBatis for persistence, Thymeleaf for server‑side rendering, and a jQuery/Bootstrap frontend with numerous third‑party plugins. The system provides a complete RBAC module (user, role, department, menu management), dictionary and configuration management, operation and login auditing, online user monitoring, Quartz‑based scheduled tasks, and a Velocity‑powered code generator that produces full CRUD modules from database tables. Additional features include dynamic data source switching, XSS/CSRF protection, captcha, file upload/download, Excel import/export with annotation‑driven mapping, and a demo suite for UI components. The architecture follows a modular Maven structure (admin, common, framework, system, generator, quartz) with a shared common module providing utilities, annotations, and exception hierarchy. While the framework is mature and widely adopted, its frontend stack (jQuery, Bootstrap 3‑era plugins) and use of Shiro represent technical drift compared to modern SPA or Spring Security approaches. The codebase is well‑organized and contains several reusable components, but is tightly coupled to its own conventions and lacks microservice readiness.
Keydd is an HTTP(s) proxy tool that performs sensitive information detection and AI-driven API business logic analysis. It acts as a MITM proxy to intercept traffic, matches content against 30+ regex patterns for credentials/API keys, and optionally uses LLMs to analyze API endpoints for business purpose, sensitivity, and penetration priority. The architecture features separate layers for proxy handling, rule-based detection, AI analysis (with rate limiting, token tracking, and metrics), database persistence (SQLite/MySQL/PostgreSQL), and notifications. Key entry points include main.go for startup, proxy/handler.go for traffic processing, and cmd/match_rules.go for sensitive pattern matching.
This repository provides a cross-platform desktop application (GUI and CLI) for debloating Android devices by uninstalling or disabling unwanted system packages. It is built in Rust using the Iced GUI framework for the interface and a shared core library (`uad-core`) that abstracts ADB communication, package list management, backup/restore, and self-update logic. The tool leverages a community-maintained package classification database (embedded JSON) that categorizes packages by vendor (AOSP, Google, OEM, etc.) and removal safety (Recommended, Advanced, Expert, Unsafe). The architecture is modular: `uad-core` handles all ADB operations and data logic, `uad-cli` provides a command-line interface with subcommands for device management, package list filtering, and actions, and `uad-gui` offers a modern graphical interface with search, filter, bulk selection, and settings for cross-user and fallback modes. The codebase is well-organized with clear separation of concerns, custom theming, and a generic modal overlay widget. It demonstrates solid engineering practices for a desktop tool, though some components (like the embedded large JSON and complex fallback logic) introduce maintenance complexity.
Diun is a Docker image update notifier written in Go that monitors container images across various platforms (Docker, Kubernetes, containerd, Nomad, Swarm, and file-based configurations) for updates. It periodically checks image manifests against a stored state (using BoltDB) and sends notifications via multiple channels (Slack, Telegram, email, etc.) when updates are detected. The application features a modular architecture with pluggable providers and notifiers, a gRPC interface for external interaction, Prometheus metrics, and structured logging. The system is designed for self-hosted environments to automate the detection of base image updates, application releases, and security rebuilds. Key components include a CLI for management, a scheduler for periodic checks, a template system for customizable notifications, and secret handling utilities. Diun's strength lies in its extensibility and wide platform support, making it a versatile tool for DevOps workflows.
bae is a PyTorch library for exact second-order optimization, specializing in Bundle Adjustment (BA) and Pose Graph Optimization (PGO) for robotics and 3D vision. It provides a sparse autograd system that automatically computes sparse block Jacobians by tracing operations on tracked parameters, enabling efficient Levenberg-Marquardt and Schur complement optimizers. The library includes CUDA-accelerated sparse matrix operations via cuSPARSE and Warp, ambient gradient implementations for SE(3) operations that are torch.compile compatible, and utilities for loading BAL and PGO datasets. It is designed as a research library with clean abstractions for defining residual graphs and optimizing over Lie groups. The architecture separates autograd tracing (bae/autograd), optimization algorithms (bae/optim), sparse matrix utilities (bae/sparse), and supporting utilities (bae/utils). The sparse autograd system uses a custom Tensor subclass (TrackingTensor) that records index, map, and cat operations via __torch_function__, then computes sparse BSR Jacobians via a backward traversal. The LM and Schur optimizers support trust region and adaptive strategies, with PCG as the default linear solver. The library also provides monkeypatches for PyPose LieTensor to enable torch.compile full-graph capture, and ambient (plain PyTorch) implementations of SE(3) operations for gradient compatibility.
This is the official Supabase SSR library (`@supabase/ssr`), a TypeScript package that provides `createBrowserClient` and `createServerClient` functions for using Supabase authentication in server-side rendering frameworks like Next.js, Remix, SvelteKit, and React Router. The library manages user sessions entirely via cookies, handling cookie chunking (splitting large JSON payloads across multiple cookies to respect browser size limits), base64url encoding for safe cookie values, and PKCE code verifier lifecycle. It exposes a `getAll`/`setAll` cookie interface that framework adapters implement to read and write cookies from request/response objects. The server client uses lazy session initialization and writes updated tokens back to cookies on auth state changes, with cache-control headers to prevent CDN caching of auth responses. The package also includes a `clearAuthCookiesAtScopes` migration helper for cleaning up stale cookies after domain/path changes. The codebase is well-tested, actively maintained, and follows modern TypeScript practices with strict mode and no top-level side effects.
PikaTorrent is a cross-platform BitTorrent client built with Flutter, supporting Windows, macOS, Linux, Android, and iOS. It provides a modern, user-friendly interface for downloading and streaming torrents, with features like sequential downloading, media playback with subtitle/audio track selection, torrent tagging and filtering, and platform-specific integrations (system tray, background service, file association). The application follows a clean architecture with an engine abstraction layer (currently backed by the Transmission library via flutter_libtransmission), provider-based state management (AppModel, SessionModel, TorrentsModel), and separate HTTP servers for streaming content and subtitles. The codebase is generally well-organized, though it contains some hardcoded values and minor code smells. The project is actively maintained with a full CI/CD pipeline and multi-platform release automation.
Intent UI is a production-ready React component library built on React Aria Components and Tailwind CSS v4, following the shadcn/ui copy-paste distribution model. It provides a comprehensive set of accessible UI primitives (buttons, forms, overlays, date pickers, collections, charts, navigation, etc.) that users install via CLI (`npx shadcn@latest add @intentui/button`) and fully own. The repository also includes a Next.js documentation site with MDX content, a registry server for component metadata, pre-built block templates (auth, charts, sidebars, navbars), an icon browser, a color palette tool, and AI agent skills for component composition, migration from v3 to v4, and registry auditing. The architecture is primarily a library of thin wrappers around React Aria primitives with `tailwind-variants` styling, plus a documentation/registry infrastructure that doubles as a marketing site. The codebase is actively maintained with low technical drift, but contains some repetitive example data and minor code smells (e.g., duplicate spreads, unused props).
Plots.jl is a comprehensive, multi-backend plotting library for Julia, organized as a monorepo containing several interdependent packages: PlotsBase (core plotting infrastructure), RecipesBase/RecipesPipeline (a macro-driven recipe system for defining custom plot types), GraphRecipes (graph and tree visualization), StatsPlots (statistical plots like boxplots, violin plots, histograms, and correlation plots), and PlotThemes (color themes). The library provides a unified API over backends including GR, PythonPlot, PlotlyJS, PGFPlotsX, UnicodePlots, and Gaston, allowing users to switch between interactive and static output without changing code. The architecture is centered on a preprocessing pipeline that transforms input data and attributes through alias resolution, magic argument expansion, and recursive recipe application before delegating to the chosen backend. This design enables extensibility: package authors can define new plot types via the lightweight RecipesBase dependency without pulling in the full Plots stack. While primarily a general-purpose visualization library, it includes strong support for scientific and research workflows through statistical plots, graph layouts, unitful quantities, HDF5 persistence, and animations. The codebase is large and macro-heavy, with some backends lagging in feature parity and occasional deprecated API usage, but overall it is actively maintained and well-tested.
Phocid is a full-featured offline Android music player built with Jetpack Compose and Material3, targeting Android 11+. It serves as a modern replacement for Phonograph, offering extensive customization of themes, artwork colors, layouts, and playback behavior. The application manages a local music library by scanning MediaStore and optionally using JAudioTagger and OpusMetadataIo for advanced metadata extraction. It supports playback via Media3 ExoPlayer with custom shuffle, repeat, speed/pitch control, and a sleep timer. The UI includes a tabbed library view (tracks, albums, artists, genres, folders, playlists), a player screen with artwork, synced lyrics (LRC), and a queue, plus home screen widgets and Android Auto integration. Playlist management features M3U import/export with relative paths and bidirectional sync using file timestamps. The codebase is well-structured with clear separation of concerns: data layer (LibraryIndex, Playlist, Preferences), service layer (PlaybackService, PlayerManager), UI components (custom scrollbar, swipe-to-dismiss, carousel, dialogs), and utility modules (AsyncCache, SaveManager, StateFlow extensions). It includes unit tests for core logic (lyrics parsing, playlist I/O, search, sorting, caching) and uses Kotlinx Serialization for persistence with upgrade paths. The architecture follows a single-activity pattern with Compose navigation and a ViewModel-driven state management. While primarily an application, it contains reusable data engineering patterns such as generic persistence, concurrent caching, and locale-aware sorting that could be extracted for other projects.
Trime is an Android input method editor (IME) that serves as a frontend for the Rime input engine, supporting Chinese dialects and both shape-based and phonetic input methods. The system is a single Android application with a native JNI layer (librime, OpenCC, etc.) and a fully theme-driven UI. The architecture is a monolith: the IME service (TrimeInputMethodService) manages keyboard, candidate, popup, and clipboard windows via a BoardWindowManager, while a RimeDaemon/RimeSession abstraction wraps the native engine. Key entry points include the IME service, MainActivity for settings, and SetupActivity for first-run configuration. Data flows involve user keystrokes processed through JNI to librime, which returns candidates and commits; theme YAML files are parsed and resolved into runtime colors and layouts. External dependencies include librime, OpenCC, Boost, LevelDB, marisa-trie, yaml-cpp, and AndroidX libraries. The codebase is well-structured with many reusable patterns (PreferenceDelegate, EventStateMachine, ThemeScope) and extensive unit tests, but has some technical debt from JNI complexity, static mutable state, and deprecated Android API usage.
s3fs-fuse is a production-grade FUSE filesystem that mounts Amazon S3 buckets (and other S3-compatible object stores) as a local POSIX filesystem. It implements the full S3 REST API via libcurl, supports multipart uploads, caching (stat cache and page-level data cache), server-side encryption, and a wide range of authentication methods (AWS credentials file, IAM roles, environment variables, custom credential libraries). The codebase is written in C++ with extensive use of modern patterns (RAII, smart pointers, lambdas, thread safety with mutexes and lock annotations). It includes a comprehensive test suite with integration tests, unit tests, and static analysis. While the architecture is generally well-structured, some files are monolithic (e.g., curl.cpp >3500 lines, s3fs.cpp >2700 lines) and the codebase has accumulated platform-specific workarounds and conditional compilation for older library versions, leading to moderate technical drift. The project is actively maintained with regular commits.
Binance Grid Trader is a cryptocurrency trading bot that implements grid trading strategies for Binance Spot and Futures markets. The application features a modular architecture with separate layers for exchange gateways, event processing, strategy management, and user interface. It provides both a graphical interface built with PyQt5 and script-based execution modes for running trading strategies without UI. The system connects to Binance via REST and WebSocket APIs to receive market data and execute trades based on configurable grid strategies.
DynamoDB-Toolbox is a lightweight, type-safe query builder and abstraction layer for Amazon DynamoDB in TypeScript. It provides a fluent, schema-driven API that sits on top of the AWS SDK v3 DocumentClient, enabling developers to define typed Table, Entity, and Schema primitives and then compose DynamoDB operations (get, put, update, delete, query, scan, batch, transactions) via a tree-shakable Action pattern. The library is designed for single-table designs but supports multiple tables, and it emphasizes compile-time type inference for input validation, transformation, and output formatting. Architecturally, the codebase is organized around three core layers: Schema (attribute-level type definitions with defaults, links, transforms, and validation), Entity (a schema bound to a table, representing one item family with automatic metadata attributes), and Table (a physical DynamoDB table with primary key, indexes, and a document client). Operations are implemented as separate Action classes (e.g., GetItemCommand, QueryCommand, UpdateItemCommand) that are instantiated via a `.build()` method on the corresponding core object. Actions follow a fluent builder pattern with `.key()`, `.item()`, `.options()`, `.params()`, and `.send()` methods, and they can be intercepted by Spy actions for testing. Key entry points include the main exports from `src/index.ts` (Table, Entity, schema builders, commands, update extensions, errors), and subpath imports for individual actions (e.g., `dynamodb-toolbox/entity/actions/get`). The library integrates with Zod (schema conversion and runtime validation), JSON Schema (via JSONSchemer), AWS CDK (generating TableV2 props), and MCP (exposing DynamoDB operations as AI tools). It also provides a DTO serialization system for portability, a transformer pipeline (prefix, suffix, trim, jsonStringify, pipe), and a comprehensive error system with typed blueprints. External dependencies include the AWS SDK v3 packages (`@aws-sdk/client-dynamodb`, `@aws-sdk/lib-dynamodb`) as peer dependencies, and optional integrations with `zod`, `@modelcontextprotocol/sdk`, and `aws-cdk-lib`. The library is published as an npm package with dual CJS/ESM builds and is tested across multiple Node.js and TypeScript versions. It is not a data research project; it is a developer tool for building type-safe DynamoDB applications.
OpenGrok is a production-grade source code search and cross-reference engine, written primarily in Java with supporting Python tooling. It indexes source code repositories from multiple version control systems (Git, Mercurial, Subversion, Perforce, CVS, RCS, SCCS, BitKeeper, etc.) and provides a web-based interface and REST API for searching, browsing, and annotating code. The system is architected as a Lucene-backed indexer (opengrok-indexer) that analyzes source files using language-specific JFlex lexers and ctags for symbol extraction, a web application (opengrok-web) built on Jersey and Jakarta Servlets, a suggester module for autocomplete, and a plugin-based authorization framework supporting LDAP and header-based authentication. Key entry points include the Indexer CLI, the web application's REST controllers (e.g., SearchController, FileController, HistoryController), and Python administration scripts (opengrok-indexer, opengrok-mirror, opengrok-sync). External dependencies include Apache Lucene, JFlex, Universal Ctags, and various VCS binaries. The codebase demonstrates strong engineering practices with extensive unit and integration tests, Docker deployment, and a modular analyzer hierarchy covering over 30 programming languages.
This is a mature PowerShell module (`powershell-yaml`) that provides high-fidelity YAML serialization and deserialization for PowerShell objects. It wraps the popular YamlDotNet library and extends it with a powerful typed class system (`YamlBase`), custom type converters, metadata preservation (comments, tags, scalar styles, mapping styles), case-sensitive key support via `[YamlKey]` attribute, and duplicate key detection to prevent data loss. The architecture is a hybrid: a PowerShell script module (`powershell-yaml.psm1`) that loads a C# class library compiled for netstandard2.0, providing both synchronous cmdlets (`ConvertTo-Yaml`, `ConvertFrom-Yaml`) and public API functions for metadata manipulation. The C# code uses Assembly Load Context (ALC) isolation for YamlDotNet to avoid conflicts with other modules. The module supports both traditional hashtable output and an enhanced PSCustomObject mode with full round-trip metadata preservation, as well as strongly-typed deserialization into PowerShell classes inheriting from `YamlBase`. This is a production-quality library with extensive test coverage, active maintenance, and well-documented examples.
Lumen is an open-source agent framework for chatting with data and building interactive dashboards, built on the HoloViz ecosystem (Panel, hvPlot, Bokeh). It provides a dual interface: a YAML-based declarative dashboard specification system for non-AI use cases, and an AI-powered natural language interface that generates SQL queries and Vega-Lite visualizations from user prompts. The architecture is modular, with clear separation between data sources (files, SQL databases, REST APIs, xarray), data pipelines (filters, transforms, SQL rewrites), views (hvPlot, Vega-Lite, DeckGL, tables), and an AI agent layer (Planner, Coordinator, specialized agents for SQL, visualization, validation, and source ingestion). The AI system supports multiple LLM providers (OpenAI, Anthropic, Google, Ollama, etc.), vector stores for RAG (Numpy, DuckDB, ChromaDB), and a tool system for external API integration. The codebase demonstrates high craftsmanship with reusable patterns like spec-driven component creation (MultiTypeComponent), composable SQL transforms, and a consistent agent/tool abstraction. While primarily an application framework, it includes data pipeline components that could be extracted for data engineering use. The project is actively maintained with extensive test coverage, though its large optional dependency surface and rapid feature velocity introduce some maintenance risk.
This monorepo contains four distinct MCP (Model Context Protocol) servers for Neo4j, built on FastMCP and the Neo4j Python async driver. The servers are: mcp-neo4j-cypher (executes Cypher queries with schema introspection, read-only mode, and token-limit truncation), mcp-neo4j-memory (persistent knowledge graph memory with CRUD for entities, relations, and observations), mcp-neo4j-data-modeling (graph data model design, validation, format conversion between Arrows JSON, OWL Turtle, Pydantic models, and Neo4j GraphRAG schema, plus Cypher ingestion code generation), and mcp-neo4j-cloud-aura-api (manages Neo4j Aura cloud instances via REST API). Each server supports stdio, SSE, and HTTP transports, with security middleware (CORS, TrustedHost) and configurable namespace prefixing for multi-tenant deployments. The architecture follows a consistent pattern: CLI + environment variable configuration, FastMCP decorator-based tool definitions, Pydantic argument validation, and async Neo4j driver usage. External dependencies include the Neo4j database, Neo4j Aura API, FastMCP, Pydantic, neo4j driver, tiktoken, and rdflib. The codebase is actively maintained with CI/CD pipelines, Docker images, and multi-channel distribution (PyPI, Docker Hub, GitHub MCP Registry).