The Dependency Injector is a mature, feature-rich dependency injection framework for Python. It provides core DI patterns through providers (Factory, Singleton, Resource, etc.), containers (Declarative/Dynamic), and wiring mechanisms (@inject decorator). The framework emphasizes explicit dependency declaration, testability, and loose coupling while supporting async/await, configuration management from multiple sources, and runtime provider overriding. It includes framework-specific integrations (Flask, Django, FastAPI, etc.) via its wiring module and extension system, though the core remains framework-agnostic. Architecturally, the codebase separates concerns into distinct modules: providers handle object creation/lifecycle, containers manage provider resolution and hierarchy, wiring enables automatic dependency injection into functions/methods, resources manage lifecycle-dependent objects (e.g., DB connections), and configuration providers unify settings from YAML/INI/JSON/env. The framework uses Cython for performance-critical components while maintaining a pure Python interface. Comprehensive documentation, extensive test suite, and numerous examples demonstrate practical integration patterns across web applications, daemons, and cloud services. The project shows strong maintenance with frequent releases, active issue triage, and broad Python version support (3.8-3.14). Technical drift is low due to proactive dependency updates, removal of deprecated Python versions, and adherence to modern Python practices. The codebase exhibits high craftsmanship in its provider patterns, particularly the Resource provider's context-manager semantics and the wiring module's automatic dependency resolution, making it a reusable dependency injection solution for Python applications.
This repository is a comprehensive catalog of software design pattern implementations in Java, maintained by Ilkka SeppΓ€lΓ€ (iluwatar). It contains over 300 self-contained modules, each demonstrating a classic GoF pattern, architectural pattern, concurrency pattern, or microservices pattern. The code is educational and demonstrative rather than production-ready, with each module providing a runnable App.java entry point, clear class hierarchies, and accompanying unit tests. The project serves as a reference for developers learning design patterns, and the implementations are intentionally simplified to focus on the pattern's mechanics. The architecture is modular: each pattern lives in its own Maven module with a consistent structure (main source, test source, documentation images, and localized READMEs). Some modules integrate with Spring Boot, JPA/Hibernate, Kafka, or MongoDB for realistic context, but the vast majority use in-memory data structures. The codebase spans Java 8 to modern features, with extensive use of Lombok, SLF4J, and JUnit 5. While not a single deployable application, the repository effectively demonstrates how patterns solve common design problems. Notable for both its breadth and craftsmanship, the repository includes patterns from creational (Builder, Factory), structural (Adapter, Proxy), behavioral (Observer, Strategy), and architectural (Hexagonal, Clean Architecture) categories. Many modules implement reusable abstractions (e.g., Converter, Specification, Filterer, EventDispatcher) that can be extracted and used in new projects. The project's high star count and active contributor base reflect its status as a primary learning resource for Java design patterns.
The `faif/python-patterns` repository is a curated collection of Gang of Four (GoF) and other classic software design patterns, implemented in idiomatic Python. Each pattern is contained in a single, self-contained module under organized directories (creational, structural, behavioral, fundamental, other). The code is the ground truth: every module includes a `main()` function that runs doctests demonstrating usage, alongside dedicated unit tests in a parallel `tests/` directory. The implementations are not boilerplate; many demonstrate advanced Python features such as descriptors (strategy.py), metaclasses (registry.py), weak references (flyweight.py), context managers (pool.py), and closures (memento.py). The repo serves as a reference for learning or adopting design patterns in Python, with clear docstrings and examples from the Python ecosystem where applicable. It has no external runtime dependencies, relying solely on the Python standard library, making it universally applicable for study or light reuse.
This repository is an open-source book, 'Rust Design Patterns', built with mdbook and published as a website and PDF. It is a comprehensive reference cataloging Rust idioms, design patterns, anti-patterns, functional programming concepts, and FFI practices. The architecture is a static site generator (mdbook) with a custom theme providing enhanced UI features (playground integration, theme toggling, sidebar, clipboard). The book is community-driven with CI/CD pipelines for building, linting, link checking, and deployment to GitHub Pages. While not a traditional software application, it functions as a living documentation project with high-quality content and reusable infrastructure components. The primary data flow is Markdown source files processed by mdbook into HTML/PDF. External dependencies include mdbook, mdbook plugins, pandoc, TeX Live, and GitHub Actions. The repository also includes internationalization infrastructure (gettext .po files) for translations, though most translations are marked fuzzy and may be outdated.
This repository is a curated, open-source catalog of AI agent design patterns. It functions primarily as a documentation website (built with Astro, deployed to Vercel) that indexes ~200+ patterns across categories like orchestration, memory, security, and tool use. Patterns follow a strict template with YAML frontmatter, evidence grading, and cross-references. The codebase includes a static site generator, automated research pipelines (arXiv scanning, similarity checking), and CI/CD for pattern validation. While the core deliverable is a web application, the repository also contains significant research infrastructure (scripts for paper discovery, reference verification, and pattern quality fixing), making it a hybrid between an application and a research data project. The source of truth is markdown files in `patterns/`; the build pipeline generates JSON, graph data, and site assets. The project is actively maintained with community contribution guidelines and automated PR review workflows.
This repository is a comprehensive collection of iOS demonstration projects covering design patterns, data structures, Swift language features, iOS frameworks, and software engineering principles. Each demo is a self-contained Xcode project or Swift playground that illustrates a specific concept through working code. The collection serves as an educational resource for iOS developers learning various aspects of iOS development, from basic Swift syntax to advanced frameworks like Core Animation and Combine. While individual demos vary in complexity and polish, they collectively provide valuable reference implementations of common iOS development patterns.
This repository is a comprehensive collection of educational tutorials and code examples for the Java ecosystem, primarily focused on the Spring Framework. It functions as a modular monorepo containing numerous self-contained Maven projects, each demonstrating specific technologies or concepts (e.g., Spring Boot, Spring Security, JPA, Kafka). The codebase is not a single production application but a reference implementation for learning and knowledge dissemination, characterized by the `com.baeldung` package naming convention. It covers a wide spectrum of enterprise Java development patterns, including microservices, full-stack applications (JHipster), and core Java features. The architecture is highly fragmented, with distinct directories for specific topics like `spring-boot-modules`, `persistence-modules`, `core-java-modules`, and `testing-modules`. Each module typically contains an `Application.java` entry point and is designed to be runnable in isolation. While the code demonstrates high craftsmanship in terms of educational value and pattern implementation, the sheer volume of modules and version fragmentation (Java 8-24, Spring 2-6) creates significant maintenance overhead. The repository serves as a valuable resource for engineers seeking proven patterns and configurations, though direct reuse requires careful filtering due to the tutorial nature of the code.
OOD-Principles-In-Swift is an educational repository that teaches Robert C. Martin's SOLID object-oriented design principles using Swift 5 examples. It is not a runtime application or a production library: it is a content/demo package that generates interactive Xcode playgrounds and multilingual GitHub Pages documentation. The canonical code lives in source/SOLID/*.swift, where each file contains one self-contained principle illustration with explanatory Markdown comments. Bash scripts named generate-playground-*.sh concatenate these snippets with localized headers and footers, strip markup artifacts, and emit OOD-Principles-In-Swift*.playground/contents.swift plus README-*.md for a dozen languages. When consumed, the primary entry point is OOD-Principles-In-Swift.playground/contents.swift, which renders five SOLID sections in Xcode's playground UI. The repo also acts as a Jekyll site via _config.yml and _layouts/default.html, hosting the translated READMEs on GitHub Pages. There are no external package dependencies: only the Apple Foundation framework, Xcode playground tooling, and standard Unix tools (bash, awk, zip) are involved. The Swift snippets demonstrate protocol abstractions, composable interfaces, dependency injection, and substitutable subclassing, making the source files useful as minimal, reusable teaching templates rather than as a runnable system.
This repository is an educational resource focused on teaching low-level design (LLD) and object-oriented design (OOD) principles through runnable code examples. It contains implementations of the Gang of Four design patterns (creational, structural, behavioral) across multiple programming languages including C++, Java, C#, Python, Go, and JavaScript/TypeScript. Additionally, it provides problem statements and solutions for common LLD interview questions such as parking lot systems, ATM implementations, vending machines, and social networking platforms. The repository serves as both a reference guide and a practical learning tool. Each design pattern is demonstrated with minimal, focused examples that isolate the pattern's core mechanics. The problem/solution sections show how these patterns can be applied in real-world scenarios, complete with class diagrams and explanations. While the code is functional and demonstrative, it prioritizes clarity of concept over production robustness, making it ideal for learning but requiring adaptation for actual deployment. Although primarily educational, the repository contains executable code that could be considered a collection of mini-applications. Each pattern implementation and problem solution can be run independently to observe the behavior. The multi-language approach allows developers to compare how the same pattern is expressed in different ecosystems, highlighting both universal principles and language-specific idioms.
This repository is an educational reference and study guide for system design interviews. It is not a deployable application or data pipeline. It provides a structured index of system design topics, solution sketches for common design problems (e.g., pastebin, Twitter, web crawler), and Python code snippets illustrating key patterns like LRU caching, hash tables, BFS on social graphs, and MapReduce. The material is oriented toward preparing engineers for technical interviews, with back-of-the-envelope calculations, REST API designs, and scalability discussions. The code examples are minimal, self-contained, and demonstrate fundamental computer science patterns, but are not production-grade software. The repository also includes Anki flashcard decks for spaced repetition.
This repository is an educational resource focused on teaching the Gang of Four (GoF) design patterns through comprehensive explanations, code examples, and diagrams. It covers creational, structural, and behavioral patterns, with each pattern accompanied by detailed markdown documentation, visual diagrams, and runnable code samples in both Java and Kotlin. The project structure organizes content by pattern type, making it easy to locate and study specific patterns. The code examples demonstrate practical implementations of each pattern in context-specific scenarios, such as a banking system for the Command pattern, a notification system for the Decorator pattern, and a file system for the Composite pattern. While the build configuration defaults to running a Singleton demo, each pattern includes independent main functions or demo classes that can be executed separately. The repository also features automated workflows for building and deploying the documentation via GitHub Pages. Despite some maintenance challenges like duplicate documentation files (marked for removal in a future version) and the coexistence of Java and Kotlin examples, the project shows active maintenance through contribution guidelines, regular updates, and a clear migration path toward consolidating documentation. The examples are well-crafted, self-contained, and illustrate core object-oriented design principles effectively, making them valuable for both learning and reference purposes.
C++ι£δΊδΊ (CPlusPlusThings) is a comprehensive Chinese C++ learning repository that aggregates code examples, tutorials, and practical exercises covering basic to advanced C++ topics. The repository is structured as a monolithic collection of self-contained C++ source files organized by topic areas including basic language features (const, static, virtual, pointers), modern C++ features (C++11/14/17), design patterns, STL source code analysis, concurrency, and coding idioms. The project uses Bazel as its build system and includes both Chinese and English documentation. The codebase primarily serves as an educational resource rather than a production application. Each topic area contains multiple .cpp files demonstrating specific C++ concepts through minimal, focused examples. The repository includes practical exercises organized as a 10-day practice series, key exercise collections, and analysis of STL internals like vector, list, hashtable, and rb_tree implementations. The design patterns section covers singleton (with multiple thread-safe variants), producer-consumer, and other classic patterns. The architecture follows a flat, topic-based organization where each directory represents a C++ concept or feature area. Most files are standalone examples that can be compiled independently using the provided Bazel BUILD files. The repository demonstrates moderate code quality with clear examples but limited production-grade error handling or comprehensive testing. The content appears to have been actively developed through 2019-2020 based on commit dates, with the last commit in May 2026 suggesting some ongoing maintenance.
This repository is a curated 'awesome list' aggregating external resources about Agent Skills β an open specification for packaging workflows and knowledge into folders consumable by AI agents. It does not contain runnable software, data pipelines, or ML models. The codebase consists of a multilingual README index, three translated adaptation guides (design patterns, practical experience, and a complete build guide), and an Excalidraw MCP integration guide. These documents are high-quality translations of official material from Anthropic and Google, providing actionable design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) and best practices for building Agent Skills. The primary function of this repository is as a reference index and a gateway to the broader Skill ecosystem; there is no executable code to analyze for architecture or data flows. Classified as 'application' due to lack of data research characteristics, but it is accurately described as a curated documentation and resource hub.
This repository is a curated reading list organized as a GitHub Pages site using Jekyll. It contains no application logic, no data pipelines, and no executable code. The entire substantive content is a single README.md file with approximately 1,500 categorized links to external articles, talks, and case studies about scalable system design. The repository's value lies entirely in its curation quality and organization, not in any software it produces. The architecture is a static Jekyll site served via GitHub Pages, configured through _config.yml with the 'jekyll-theme-modernist' theme. The only other files are standard GitHub community templates (CONTRIBUTING.md, SECURITY.md, issue templates, CODE_OF_CONDUCT.md, LICENSE, FUNDING.yml). There are no tests, no CI/CD pipelines, no package manifests, and no source code in any programming language. The last commit was in January 2026, indicating the repository is still receiving updates. The content covers distributed systems patterns across major tech companies (Netflix, Uber, Facebook, Google, LinkedIn, Pinterest, etc.) organized into categories: principles, scalability, availability, stability, performance, intelligence/ML, architecture, interview prep, organization, and talks. The breadth of coverage is impressive but the curation is entirely manual link collection with no original analysis or tooling.
This repository is a community-driven collection of programming exercise solutions, organized as a numbered roadmap (00 to 50) covering fundamental to intermediate topics. Each exercise is solved in multiple programming languages (Python, JavaScript, Java, C++, Go, Kotlin, etc.) by different contributors. The code consists of standalone scripts that demonstrate language features such as recursion, data structures, exception handling, async programming, regular expressions, design patterns (Singleton, Decorator), and SOLID principles. The repository is not a cohesive application or library; it is an educational resource for learning and practicing programming concepts across languages. The quality of solutions varies widely, with some files containing errors or being incomplete. The project relies on community contributions and lacks centralized testing or dependency management.