FreeCAD is a mature, open-source parametric 3D CAD application with a modular workbench architecture. The codebase reveals a well-structured monolithic desktop application written in C++ with extensive Python bindings. The core uses OpenCASCADE as its geometry kernel, Coin3D (Open Inventor) for 3D scene representation, and Qt for the GUI. The architecture separates the application logic (App) from the GUI (Gui), with a Base layer providing fundamental utilities (math, units, persistence, Python integration). Workbenches (PartDesign, Sketcher, Draft, Arch, FEM, CAM, Mesh, etc.) are implemented as loadable modules that extend the document-object model. The parametric engine supports expression-driven constraints, a full dependency graph with topological naming, and a sketcher solver for 2D constraints. The FEM workbench includes a solver framework supporting CalculiX, Elmer, and Z88. The CAM workbench provides CNC toolpath generation with post-processing. The codebase demonstrates strong engineering practices: comprehensive unit tests, CI/CD pipelines, modern CMake, C++17, and clean separation of concerns. With 31k+ stars and active development (last commit June 2026), it's a production-grade CAD system used by hobbyists and professionals alike.
Kratos is a production-grade Go microservices framework designed for cloud-native applications. It provides a unified transport layer supporting both HTTP (gorilla/mux) and gRPC, with a composable middleware chain for cross-cutting concerns like logging, tracing, authentication, rate limiting, and circuit breaking. The framework is protobuf-first: service definitions are written in proto files, and code generators (protoc-gen-go-http, protoc-gen-go-errors) produce HTTP stubs and structured error types. It includes a pluggable configuration system with support for multiple sources (file, etcd, consul, nacos, apollo, kubernetes, polaris), a service registry abstraction with implementations for etcd, consul, nacos, eureka, zookeeper, kubernetes, polaris, and others, and a load balancing selector with algorithms like random, weighted round-robin, and power of two choices (p2c). Observability is integrated via OpenTelemetry (tracing, metrics, logging) and Sentry error tracking. The CLI tool `kratos` scaffolds new projects, manages proto generation, and handles upgrades. The architecture is modular: core packages are minimal and extensible, with contrib packages as separate Go modules. The codebase demonstrates strong engineering practices, clean interfaces, and well-tested core components.
nix-topology is a NixOS-based tool that automatically generates infrastructure and network diagrams as SVGs from NixOS configurations. It uses a global topology module to aggregate information from multiple NixOS hosts, extracting data about interfaces, services, containers, and virtual machines via specialized extractors. The gathered topology is then rendered using ELK for layout and converted to SVG via custom tools (elk-to-svg and html-to-svg), producing both a physical connection diagram (showing nodes as cards with hardware and service details) and a network-centric view (showing logical networks and their connections). The architecture is modular, with clear separation between information gathering, topology aggregation, and rendering.
Boundary is an Elixir library that enables developers to define and enforce architectural boundaries within their applications. By specifying which modules belong to which boundaries and what dependencies are allowed between them, teams can prevent unwanted coupling and maintain clean architectural layers (e.g., keeping the web layer from directly accessing database modules). The library works as a mix compiler that hooks into Elixir's compilation process using compilation tracers. During compilation, it records all module/function calls, then validates these calls against the defined boundary rules. Violations are reported as compiler warnings, allowing developers to identify and fix dependency issues early in the development process. Beyond enforcement, Boundary provides a suite of mix tasks for visualizing and analyzing dependencies. These include tools to generate dependency graphs at both the module and function level, identify external dependencies, and create documentation groupings. The implementation demonstrates advanced Elixir techniques including effective use of GenServer for state management, ETS for efficient data storage during compilation, and clever tricks for incremental analysis to maintain performance in large projects.
This repository is a comprehensive French-language training course for PostgreSQL 18, structured as 22 progressive chapters and 7 reference annexes. It serves as educational material covering PostgreSQL fundamentals, SQL querying, advanced features, administration, performance tuning, extensions, and modern architectures. The content combines theoretical explanations with practical examples, best practices, and version-specific features (like asynchronous I/O, UUIDv7, and native OAuth). While not containing executable code or data pipelines, it provides reusable reference materials such as SQL query templates, shell scripts for backup/monitoring, configuration guides, and checklists that can be adapted across projects. The repository is actively maintained with recent updates and demonstrates low technical drift due to its focus on enduring database principles alongside version-specific annotations.
This repository is the **Wow** framework, a comprehensive, production-grade event-sourcing and CQRS framework for building reactive, cloud-native applications in Kotlin and Java. It provides a full stack: domain modeling with aggregates, commands, events, and sagas; reactive command/event buses with multiple backends (MongoDB, Redis, Elasticsearch, Kafka); a query gateway with rich filter DSL and snapshot/event-stream queries; automatic OpenAPI generation; a compensation subsystem with a React dashboard for managing failed executions and retries; observability via OpenTelemetry; and BI tooling for ClickHouse. The architecture is modular (30+ Gradle modules) but deployable as separate services (e.g., compensation server, example server). It is actively developed with extensive tests (unit, contract, integration, JMH benchmarks) and comprehensive documentation.
This repository is a reference architecture for building production-ready React applications, implemented as a monorepo containing three distinct application templates: Next.js App Router, Next.js Pages Router, and Vite. It serves as a comprehensive guide for scalable project structure, demonstrating best practices in state management (Zustand for client state, TanStack Query for server state), testing (Vitest, Playwright, MSW), and security (JWT, RBAC). The codebase prioritizes strict TypeScript usage, unidirectional data flow, and feature-based colocation. The system functions as a boilerplate and educational tool rather than a standalone product. It provides a complete development environment including UI component libraries (Radix UI + Tailwind), form handling (React Hook Form + Zod), and API abstraction layers. The architecture is designed to be copy-pasted into new projects, with the three app implementations serving as comparative examples for different React rendering strategies. Key operational patterns include a centralized API client with Axios interceptors for authentication and error handling, a mock backend service using MSW and @mswjs/data for local development and testing, and a robust authorization system that enforces role-based access control at the component and route levels. The codebase is heavily instrumented with tests and documentation to ensure maintainability and adherence to the defined architectural standards.
Impress is an enterprise application server for Node.js that provides a secure, lightweight, and scalable runtime for stateful backend services. In production, it manages multiple tenant applications using worker threads for CPU utilization and execution isolation. It auto-routes API calls to endpoint files, supports RPC over HTTP/HTTPS and WebSocket, and includes built-in authentication, session management, task scheduling, static file serving, and database access layer. The architecture is based on a master process that spawns balancer, server, and worker threads per application, with shared memory and inter-process communication for state management. Key entry points include the impress.js master process, which loads applications from .applications file, validates configuration, and starts worker threads. Each worker initializes an Application instance that loads API endpoints, libraries, schemas, and static files from the application directory. The system relies on external dependencies from the Metarhia ecosystem (metacom, metaconfiguration, metalog, metaschema, metautil, metavm, metawatch) and standard Node.js modules. It does not use third-party npm packages for core functionality. Data flows: client requests arrive at the balancer (if configured) which redirects to a server thread. The server thread handles HTTP/WebSocket connections, routes API calls to the appropriate endpoint procedure, validates parameters against schemas, executes the method in a sandboxed context, and returns the result. Static files are served from memory cache with optional compression. Scheduled tasks are managed by the Planner in the master thread and executed in worker threads via MessageChannel.
CodeGraph is a Python command-line tool that performs static analysis on Python source code to generate dependency graphs. It parses Python files to extract functions, classes, and import statements, then builds a graph showing how these entities depend on each other. The tool outputs an interactive HTML visualization (using D3.js by default) or can export the data to CSV or print it to the console. The architecture is modular, consisting of a parser (using Python's tokenize module), a core module that builds the dependency graph, utility functions for file discovery, and a visualizer that supports both D3.js (interactive) and matplotlib (legacy) backends. The tool does not execute the code being analyzed, making it safe and fast for large codebases. Key strengths include a robust parser that handles various import styles and class inheritance, and a flexible visualization system that provides features like search, collapsing/expanding nodes, and detailed tooltips. The project is actively maintained with comprehensive tests and supports multiple Python versions.
This repository provides 'Diagrams', a Python library that lets you define cloud system architecture diagrams as Python code. It renders diagrams using Graphviz, supporting a wide range of cloud providers (AWS, Azure, GCP, Kubernetes, etc.) through auto-generated node classes. The library includes a CLI tool to execute diagram scripts and a web-based playground (React/TypeScript) that runs the library in-browser via Pyodide, allowing interactive editing and export. The architecture is a library with a CLI entry point, plus a separate SPA playground. Key entry points are the `diagrams` CLI command and the `Diagram`/`Node` classes. External dependencies include Graphviz (system binary), Jinja2, and for the playground: Pyodide, @hpcc-js/wasm-graphviz, React, CodeMirror. The codebase is well-structured with a code generation pipeline for provider nodes, comprehensive testing, and a polished user experience.
drawio-skill is a comprehensive toolkit for generating, manipulating, validating, and publishing draw.io architecture diagrams from natural language, codebases, infrastructure-as-code, API specifications, and other structured sources. It provides a unified CLI (`diagramctl.py`) and an optional MCP server (`diagramctl_mcp.py`) that expose workflows for building, syncing, viewing, testing, reviewing, querying, what-if analysis, story generation, and publishing diagrams. The system uses a formal Diagram Intermediate Representation (IR) to separate semantic meaning from geometry, enabling incremental reconciliation, multi-view projection, architecture contract enforcement, and accessible HTML story output. It includes 15+ importers for languages (Python, JS, Go, Rust), IaC (Terraform, Kubernetes, Docker Compose), schemas (SQL, OpenAPI, AsyncAPI, Protobuf, GraphQL), and live infrastructure (Docker, Terraform state). The project is packaged as an AI agent skill compatible with Claude Code, Cursor, Copilot, and other Agent Skills hosts, and ships with GitHub Actions for CI integration. While primarily an application, it also bundles curated icon indices and style presets that could be considered data assets.
eslint-plugin-project-structure is a TypeScript-based ESLint plugin that enforces scalable, consistent project architecture through three configurable rules: folder-structure enforcement, file-composition constraints (e.g., `**/*.consts.ts` may only contain variables, `**/*.types.ts` only interfaces/types), and independent-modules enforcement to prevent massive dependency trees. The plugin parses all import syntaxes — ESM, CJS, dynamic `import()`, `require()`, `jest.mock()`, `jest.requireActual()`, plus `ExportAllDeclaration`/`ExportNamedDeclaration` — and validates file paths against micromatch glob patterns. Configuration is provided via JSON files validated against a bundled JSON schema, enabling IDE autocompletion and early error detection. The codebase is a standard ESLint plugin library: rules receive AST nodes and report violations, with utility modules handling import classification, cross-platform path resolution (Windows/POSIX), and multi-pattern glob matching. It is distributed as compiled CommonJS (`dist/index.js`) with a self-referencing dev dependency for integration testing. The project ships with comprehensive tests covering edge cases like Windows paths, `@types/` scoped packages, and external-import detection, plus CI running type checks, format checks, lint, and tests via Yarn 3.6.3.
PipelinR is a lightweight Java library implementing the mediator/pipeline pattern for command processing. It provides a type-safe way to send commands to handlers through a configurable pipeline of middleware, supporting cross-cutting concerns like logging, validation, and transactions. The library also implements notification (event publishing) with multiple handling strategies including synchronous, asynchronous, and parallel execution modes. With zero dependencies and a focus on simplicity, it aims to help developers follow SOLID principles by replacing monolithic service classes with focused handlers.
Kaspresso is a comprehensive Android UI testing framework built on top of Espresso (via Kakao) and UI Automator (via Kautomator), providing a Kotlin DSL for writing stable, readable tests. It offers extensive built-in reliability mechanisms: flaky safety (automatic retries with configurable timeouts), auto-scroll fallback, system dialog handling, and element reloading. The framework includes a modular interceptor architecture for customizing test behavior, device manipulation APIs (network, location, phone, permissions, logcat, screenshots, video recording), and integration with Allure for reporting and Jetpack Compose for modern UI testing. Additionally, it provides an ADB server for executing shell commands on the device and a command-line tool for generating page objects from UI dumps. The codebase is well-structured with multi-module Gradle projects, convention plugins, and extensive sample apps demonstrating real-world usage.
This repository stores design proposals and technical decisions for the OpenClaw agent platform. It contains RFCs covering architecture, protocols, and implementation plans for features such as localization, computer use, sender authentication, enterprise control plane, and plugin SDKs. While not an application itself, it serves as the authoritative specification source for the OpenClaw project. The RFCs are well-structured with YAML frontmatter, sidecar assets, and implementation plans, demonstrating high-quality design documentation. The repository also includes GitHub Actions workflows for process automation and a pull request template.
This repository is primarily an open-source technical book and architecture knowledge base written in Chinese, rendered as a heavily customized VuePress static site. The core deliverable is the book itself — chapters spanning architecture history, distributed systems foundations, transactions, security, observability, service mesh, containers, and cloud-native infrastructure — rather than a runnable software service. It is best classified as a documentation/application hybrid: the only application code is the VuePress site infrastructure, custom plugins, CI/CD scripts, and an embedded resume slide-deck framework. The repository also links to external companion demo projects (Spring Boot, Spring Cloud, Kubernetes, Istio, serverless) rather than hosting them in-tree.
This repository is the Azure Enterprise-Scale (ALZ) reference implementation, providing infrastructure-as-code artifacts to deploy and govern multi-subscription Azure environments. It is not a runtime application but a deployment toolkit consisting of ARM templates, Bicep files, custom Azure Policy definitions/initiatives, custom role definitions, and PowerShell automation. The architecture follows the Cloud Adoption Framework's landing zone design, with a management group hierarchy, policy-driven governance, and modular networking topologies (hub-spoke, Virtual WAN). Key entry points include the main ARM deployment template (`eslzArm/eslzArm.json`), the portal accelerator (`eslzArm/eslz-portal.json`), and the Bicep-based policy compilation (`src/templates/policies.bicep`). Data flows are primarily declarative: policy definitions and assignments are deployed to management groups, which then enforce compliance on subscriptions and resources. External dependencies are entirely Azure-native: Azure Resource Manager, Azure Policy, Azure RBAC, and Azure networking services. The repository also includes a PowerShell module (`Alz.Tools`) for subscription alias creation, orphaned RBAC cleanup, and artifact conversion, plus a Pester-based test framework for policy validation.
coca is a static code analysis and refactoring CLI tool written in Go, designed to assess code quality, detect bad smells, analyze dependencies, and support migration tasks (specifically Spring MVC to Spring Boot). It operates as a monolithic command-line application using the Clean Architecture pattern, with distinct layers for domain models, application logic, and infrastructure. The tool supports multiple languages including Java, Go, Python, JavaScript, TypeScript, and Groovy, leveraging ANTLR for parsing and generating ASTs for analysis. The system's primary function is to scan codebases for technical debt, architectural issues, and API definitions, outputting results in various formats (JSON, CSV, Graphviz). It includes features for bad smell detection (e.g., Long Method, Large Class), test smell analysis, and dependency usage evaluation. The architecture is modular, allowing individual analysis features to be executed independently, though the tool itself is a single binary. While the codebase demonstrates solid engineering practices with a clean separation of concerns and reusable domain models, it exhibits technical drift in its dependency ecosystem. The Go version specified (1.23) is ahead of current stable releases, and several core dependencies (e.g., spf13/cobra, gomega) are significantly outdated, creating a mismatch between the code's stated requirements and the available ecosystem versions. This suggests the project is either in a transitional state or relies on specific, potentially unstable, dependency versions.
This repository is a curated knowledge base for professional software engineers, not a functional application or data pipeline. It consists of a large README with categorized links to articles, books, talks, and other resources, along with supplementary directories for antipatterns (with small Python code examples), training curricula for front-end and web development, and a glossary. The codebase is purely documentation and illustrative snippets; there are no services, APIs, or executable systems. Its value lies in the curated collection of high-quality references rather than in any software component. The antipatterns section provides well-written explanations of common mistakes, and the training outlines offer structured learning paths. However, there are no reusable software components—the Python files are trivial examples tied to the documentation.
This repository is a curated collection of reusable draw.io shape libraries, editable diagram examples, and a Docusaurus-based guideline website for creating consistent SAP Business Technology Platform (BTP) solution architecture diagrams. It provides standardized icons for SAP BTP services (organized by category and size), connectors, area shapes, text elements, brand logos, and pre-built diagram templates (e.g., SAP Task Center, SAP Cloud Identity Services, SAP Private Link Service). The guideline website documents design principles, atomic components, and best practices for diagram creation. The repository is not a runnable application but a design asset library with a static site for documentation. It serves SAP solution architects and technical writers who need to produce professional, on-brand architecture diagrams.