This repository is the official Spring PetClinic sample application, a classic web application for managing a veterinary clinic's owners, pets, and visits. It is built as a Spring Boot monolith using Spring MVC, Spring Data JPA, Thymeleaf templating, and an H2 in-memory database by default, with optional MySQL and PostgreSQL backends. The application demonstrates a wide range of Spring Boot features including caching (JCache), internationalization (10+ languages), form validation, custom formatters, and comprehensive testing with JUnit 5, Mockito, and Testcontainers. It also includes a JMeter load test plan, Kubernetes deployment manifests, and CI/CD workflows for Maven and Gradle. The architecture follows a standard layered pattern: controllers handle HTTP requests, repositories provide data access via Spring Data JPA, and Thymeleaf templates render the UI. Domain models use a clean inheritance hierarchy (BaseEntity, NamedEntity, Person) to reduce duplication. The codebase is well-structured and serves as a reference for Spring Boot best practices, though it is intentionally a sample and not a production-ready system. It is actively maintained by the Spring team and tracks the latest Spring Boot releases. Key functional areas include owner management (CRUD with search), pet management (with duplicate name detection), visit scheduling (with future-date validation), and a veterinarian listing page. The application also includes a crash controller to demonstrate error handling and a welcome page. The code is clean, well-tested, and follows Spring conventions, making it a valuable learning resource for developers new to Spring Boot.
This repository builds and maintains the `grafana/otel-lgtm` Docker image, an all-in-one OpenTelemetry backend for development, demo, and testing. It bundles Grafana, Prometheus, Tempo, Loki, Pyroscope, and the OpenTelemetry Collector into a single container, providing a complete observability stack with a single `docker run` command. The image supports optional eBPF-based auto-instrumentation (OBI) for zero-code traces and RED metrics, and can export telemetry to external vendors via OTLP. The architecture is a multi-stage Docker build that downloads and verifies each component using checksums or cosign signatures. The container entrypoint (`run-all.sh`) starts all services in parallel, waits for health checks, and handles graceful shutdown. The OpenTelemetry Collector configuration is dynamically extended based on environment variables (debug exporter, external endpoints, custom headers). The repository also includes example applications in Java, Go, Python, .NET, and Node.js that demonstrate OpenTelemetry instrumentation, along with acceptance tests (OATS) that validate traces, metrics, logs, and profiles. CI/CD is comprehensive: automated builds, linting (flint, hadolint, shellcheck, etc.), container scanning with Trivy, CodeQL analysis, and weekly scheduled releases with immutable version tags. Container images are signed with cosign and attested via GitHub Artifact Attestations. The repository is actively maintained with Renovate for dependency updates and a well-defined release process.
This is an example application demonstrating how to set up and run a Parse Server instance using Express. It serves as a reference implementation for the Parse Server open-source backend-as-a-service. The codebase includes a minimal Express server that mounts the Parse Server middleware at a configurable path, serves static assets and a test page, and provides health check endpoints. It uses modern ES6 modules, TypeScript, and the latest Parse Server version (9.x). Cloud code functions (hello, beforeSave) and schema definitions are included to illustrate common patterns. The project also includes comprehensive deployment configurations for various platforms (Heroku, AWS, Google Cloud, etc.), Docker support, CI/CD with GitHub Actions, and automated semantic releases. While simple in scope, the code is well-structured, follows current best practices, and includes a test suite with a reusable test runner utility for programmatically starting/stopping the server during integration tests. This repository is best used as a starter template for new Parse Server projects or as a reference for migration to Parse Server 8+.
This repository contains Dreamhouse, a Salesforce sample application for real estate property management built with Lightning Web Components (LWC) and Apex. It demonstrates key Salesforce platform capabilities including custom objects (Property__c, Broker__c), flexible page layouts, Lightning Message Service for inter-component communication, and mobile features like barcode scanning and geolocation. The application provides a property explorer with filtering, pagination, map visualization (using Leaflet), and a property creation flow with geocoding via OpenStreetMap. It also includes sample data import, accessibility testing, and comprehensive CI/CD pipelines. The codebase is well-structured with separate Apex controllers, reusable LWC components, and Jest unit tests, representing a professional implementation of Salesforce development best practices. While primarily an application, it includes sample data (JSON files) that could be considered a minor data aspect, but the dominant character is application.
This repository is a full-stack marketing demo application powered by Google Cloud generative AI (Vertex AI Gemini, Imagen). It provides an end-to-end workflow for generating campaign content (emails, social media posts, website copy, images), analyzing audiences via natural language to BigQuery, and managing assets across platforms. The frontend is a modern Angular 17 single-page application with Firebase authentication and hosting, while the backend is a FastAPI Python service deployed on Cloud Run. Terraform infrastructure-as-code provisions all GCP resources including BigQuery datasets, Vertex AI Search datastores, Dialogflow CX agents, and Google Drive folder templates for content export. The application showcases marketing use cases like trendspotting via GDELT news summarization, audience insights using structured and unstructured data, and interactive image editing with inpainting/outpainting. While primarily a demo application, the repository also contains several Jupyter notebooks exploring model tuning, text-to-SQL, and clustering explanations, making it a hybrid resource for both application developers and data scientists.
This repository is a comprehensive multi-module Maven project that serves as a learning and demonstration resource for the Java ecosystem, specifically focusing on Spring Boot, microservices, middleware, and AI integration. It contains over 20 independent modules, each showcasing a specific technology or pattern: from core Spring features (AOP, events, lifecycle) to advanced distributed systems (Dubbo, gRPC, Netty, RocketMQ, Kafka, Redis, Elasticsearch, ShardingSphere, Sentinel, Nacos, Curator) and modern AI capabilities (LangChain4j-based RAG, agentic workflows, guardrails, MCP client, image generation, TTS). The architecture is a modular monolith where each module is independently runnable via its own Spring Boot application class, sharing a common utility library (whatsmars-common) for serialization, caching, consistent hashing, encryption, and logging. The AI module is particularly notable for implementing advanced patterns such as query compression, smart routing, re-ranking, structured output, and configurable agentic workflows (sequential, parallel, conditional, loop, supervisor). While the repository is not a single deployable product, it provides high-quality, well-documented examples that demonstrate non-obvious craftsmanship in areas like custom gRPC load balancing, consistent hashing with virtual nodes, and programmatic logger creation. The codebase is professionally written with good separation of concerns, though testing coverage is limited to utility classes. Overall, it is a valuable reference for Java developers exploring modern frameworks and AI integration patterns.
This repository is a Godot 4 addon that integrates the libsm64 library (a reimplementation of Super Mario 64's game engine) into Godot via GDExtension. It provides both a low-level C++ API exposed as a Godot singleton (`LibSM64`) and high-level GDScript nodes (`LibSM64Mario`, `LibSM64StaticSurfaceHandler`, `LibSM64SurfaceObjectsHandler`, `LibSM64AudioStreamPlayer`) for easily adding Mario characters and surface geometry to Godot scenes. The addon handles coordinate system conversion (SM64 to Godot), fixed-rate physics ticking at 30 FPS with interpolation for smooth rendering, and dynamic material switching based on Mario's power-up state. A demo project with multiple playable scenarios (Bob-omb Battlefield, many Marios, flying range) is included, along with a ROM picker dialog for loading the required Super Mario 64 US ROM. The architecture is a classic Godot plugin: a GDExtension shared library (built from C++ source in `extension/`) registers new engine classes and a singleton, while GDScript files in `addons/libsm64_godot/` provide editor integration and higher-level abstractions. The core C++ code wraps all libsm64 functions (global init, Mario creation/tick, surface loading, audio) and exposes them as Godot methods. The GDScript layer adds node lifecycle management, input mapping, interpolation, and surface handling utilities. The demo project in `libsm64_godot_demo/` showcases the addon with custom scenes, a camera rig, HUD, and input configuration. Key external dependencies are the libsm64 C library (included as a Git submodule) and the godot-cpp bindings (also a submodule). The addon requires a specific Super Mario 64 US ROM (SHA-256 verified) to function, as libsm64 uses original game assets. The build system uses SCons and GitHub Actions to compile the GDExtension for Windows, macOS, Linux, and Web platforms. The code is well-structured with clear separation between C++ engine bindings and GDScript gameplay logic, making it a solid reference for integrating complex C libraries into Godot.
This repository is a curated resource catalog for WeChat Mini Program development. It does not build a deployable application in the traditional sense; instead, it operates as a structured data project that generates a searchable static site and API from a YAML data file. The core architecture is a set of TypeScript scripts that read `data/resources.yaml`, validate the data integrity, check external links for liveness, and generate `README.md`, `public/api/resources.json`, and the search interface in `public/index.html`. The data is categorized into official docs, tools, plugins, components, backend SDKs, and demos, each with metadata like language, difficulty, and topics. The repository's primary value lies in its data quality tooling. The validation script (`validate-data.ts`) enforces strict schema rules, checks for duplicate URLs, validates metadata, and provides precise YAML line numbers for errors. The link checker (`check-links.ts`) uses concurrent HTTP requests with retries and graceful handling of servers that block HEAD requests. The import script (`import-readme.ts`) can reconstruct the YAML from the generated README, enabling a bidirectional workflow. The GitHub Actions CI pipeline runs validation, generation checks, and link checks on pull requests, posting automated comments when checks fail. The codebase is well-structured with clean separation of concerns: `catalog.ts` handles all data types and YAML/JSON I/O, `generate.ts` handles README and API generation, and individual scripts handle specific tasks. The TypeScript configuration uses strict mode and modern ES2022 features. Dependencies are minimal (only `yaml` at runtime) and the project requires Node.js >= 20. The last commit was in June 2026, indicating the project is in a stable but not actively developed state.
The W3C Editing repository hosts specifications and examples for web editing APIs, including EditContext, Clipboard API, and Input Events. It provides static HTML documentation and runnable demos that illustrate how to use these specifications. The repository is actively maintained by the Web Editing Working Group and includes a reusable polyfill for the 'intentions' feature that demonstrates non-obvious skill in handling keyboard events.
This repository is the source code for the micro-frontends.org website, an educational resource explaining the Micro Frontends architectural pattern. It contains a Jekyll-based static site with detailed tutorials, along with multiple self-contained example applications (plain HTML, client-side composition, universal composition, and data-fetching) that demonstrate key Micro Frontends concepts. The example code is organized by team (red, blue, green) and uses Custom Elements, Server-Side Includes (SSI), Docker Compose, and Nginx to illustrate how to build independent frontend fragments that can be composed into a cohesive application. The repository serves primarily as a learning tool and reference implementation, not a production-ready framework.
OpenCV Zoo is a curated collection of pre-trained computer vision models optimized for OpenCV's DNN module, accompanied by inference demo scripts (Python and C++), benchmarking harness, evaluation tools, and quantization utilities. The repository serves as a practical resource for deploying models on a wide range of hardware (x86, ARM, RISC-V, NPUs) by providing ready-to-run code for tasks such as face detection/recognition, object detection, segmentation, text detection/recognition, pose estimation, and more. While primarily an application-oriented model zoo, it also includes research-grade evaluation metrics (e.g., LFW, WIDERFace) and quantization workflows, giving it a hybrid character that bridges model deployment and performance characterization. The benchmarking framework and quantization tools are the most architecturally coherent parts; model-specific demos are utilitarian but tightly coupled to their respective ONNX graphs.
This is the official W3C WebTransport specification repository. It defines a JavaScript API for bidirectional, multiplexed network communication over QUIC and HTTP/3, providing reliable streams and unreliable datagrams. The repository contains the spec source in Bikeshed format (index.bs), supporting documents (explainer, use-cases, security questionnaire), and functional sample implementations (echo client/server and a WebCodecs-based video streaming demo). The core deliverable is the specification, while the samples serve as proof-of-concept implementations. The codebase is a hybrid: primarily a standards document, but includes runnable demo applications that illustrate the API usage patterns. The spec is still in early draft stage and references evolving IETF protocols.
This repository is a curated collection of over 30 independent Streamlit in Snowflake (SiS) demo applications, each self-contained in its own folder. The demos span a wide range of use cases including business intelligence dashboards, LLM-powered chatbots (Cortex, DeepSeek-R1, RAG), geospatial H3 visualizations, custom UI components (AgGrid, ECharts, Slickgrid), CI/CD pipelines with Snowflake CLI, and embedding SiS into external portals via Next.js. Each app includes a `streamlit_app.py` entry point, a `data/creation_script.sql` for Snowflake object setup, and sample data. The code is educational, heavily commented, and demonstrates patterns like caching, dynamic page navigation, and local/cloud-aware connections. While the apps are not production-grade (some hardcoded values, duplication), they serve as excellent reference implementations for building SiS applications.
This repository is a small Python demo application that turns a webcam feed into a real-time David Attenborough-style nature documentary narration. It consists of two independent processes: capture.py grabs frames from the webcam every 2 seconds, resizes them to a maximum dimension of 250 pixels, and writes them to frames/frame.jpg; narrator.py polls that file, base64-encodes it, sends it to OpenAI's GPT-4 Vision API with a snarky Attenborough system prompt, appends the response to a running conversation script, synthesizes speech via ElevenLabs, and plays the audio aloud. The two processes communicate through the filesystem, which is a simple but effective decoupling pattern for a demo. The code is functional and readable, with a few thoughtful touches: the image is downscaled before upload to reduce API cost and latency, and the narrator retries reading the frame file when it catches a transient EACCES error caused by the capture process writing concurrently. However, it is a proof-of-concept rather than a production application: there are no tests, no error handling for API failures, hardcoded paths, and the pinned dependencies are from late 2023. The OpenAI model 'gpt-4-vision-preview' and the ElevenLabs 'generate/play' API are both deprecated, so the code would need updates to run against current APIs. Despite a recent commit timestamp in the metadata, the codebase is effectively a frozen 2023 demo. The maintainer has not updated dependencies or addressed the deprecated APIs, and 41 open issues remain. It is best treated as a reference implementation or a starting point for a webcam-to-AI-narration pipeline, not as a maintained library.
This repository is a historical demo application showcasing JWT-based authentication and authorization in a Spring WebFlux reactive stack. It implements a custom security filter chain that first authenticates via HTTP Basic (returning a signed JWT) and then validates Bearer tokens on protected endpoints. The code uses Spring Boot 2.1.1, Java 8, and the Nimbus JOSE library for JWT signing and verification. The architecture is a simple monolith with in-memory user details and hardcoded secrets, intended for educational purposes rather than production use. The README explicitly states the project has been superseded by a modernized version (Spring Boot 4, Java 21, Spring Security 7) and is kept for historical reference. The code demonstrates key patterns: custom ServerAuthenticationSuccessHandler, functional bearer token converter, and a dummy ReactiveAuthenticationManager for stateless JWT authentication. However, it suffers from significant technical drift (outdated dependencies, Java 8, deprecated APIs) and lacks proper error handling, tests, and configurability.
This repository is a Chinese-language chatbot built with Rasa 1.10.18, a now-deprecated version of the Rasa framework. It implements a simple customer service bot for telecom queries (phone bill and data usage) using a BERT-based NLU pipeline (HFTransformersNLP with bert-base-chinese) and a rule-based dialogue management system with story-driven flows. The codebase is minimal: a single custom action for querying consumption, a few intents and entities, and a set of dialogue stories. The project is functional but lacks tests, CI/CD, or modern Rasa practices. The pinned dependencies (rasa==1.10.18, transformers==2.4.1) are severely outdated, and the code relies on deprecated Rasa components. While the repository has accumulated stars (983) due to its accompanying blog series, the actual code is a basic Rasa template with little reusable craftsmanship. It serves as a learning example for building a Chinese chatbot with Rasa 1.x, but is not suitable for production or reuse without significant modernization.
This is a deprecated AngularJS 1.x demo application showcasing the ng-admin library for creating admin interfaces. It simulates an e-commerce poster shop ('Posters Galore') with mock data generated by Node.js scripts. The application uses FakeRest to create a mock REST API in the browser, allowing full CRUD operations on customers, products, orders, reviews, and settings without a real backend. The admin interface includes dashboards, custom directives for shopping cart calculations, currency formatting, and image zoom functionality. The repository is marked as archived in the README with a warning that it's no longer maintained.
This is a Chinese-translated fork of the official Electron API Demos application, an interactive desktop app built with Electron that demonstrates core Electron APIs through runnable code examples. The app is a monolith with a main process (main.js) that creates a BrowserWindow and loads all main-process demo scripts, and a renderer process that handles navigation, demo toggling, and code display. It uses IPC for communication between processes, electron-settings for persisting user state, and highlight.js for syntax highlighting. The codebase is heavily tied to the demo structure and is not designed for reuse. It relies on outdated dependencies (Electron 7, Node 8.9.3) and uses deprecated patterns like nodeIntegration and the remote module. The primary value is educational, providing a hands-on way to learn Electron APIs, especially for Chinese-speaking developers. The repository has not been actively maintained since 2018, and the technical drift is high.
This repository is a meta-project for a Spring Cloud microservices demo, now superseded by the Opensabre framework. It contains no actual source code; instead, it aggregates several external submodules (opensabre-framework, base-authorization, base-organization, base-gateway, etc.) via Git submodules. The root pom.xml defines dependencies for Spring Boot 2.1.10, Spring Cloud Greenwich, and Alibaba Cloud 2.1.0, all of which are outdated. The README explicitly states that the project has been replaced by a newer version. As a standalone repository, it provides no reusable code or components; it serves only as a historical reference or a pointer to the submodules. The repository is effectively deprecated and should not be used for new projects.