This repository is a comprehensive, Chinese-language technical knowledge base and online book focused on Flutter and Dart development. It contains over 300 Markdown articles covering everything from basic Flutter widgets and state management to deep dives into rendering engines (Impeller, Skia), platform-specific issues (iOS 26 keyboard, Android Vulkan), Dart language features, and ecosystem trends. The content is published as a VitePress static site with offline search (Pagefind) and is also compiled into a downloadable PDF. While the repository is primarily a documentation site (not an application or data pipeline), it includes a well-engineered build system with automated CI/CD deployment, PDF generation via Puppeteer, and a custom search component. The articles are high-quality technical writing, often referencing official Flutter issues and PRs, making it a valuable resource for Flutter developers. However, the codebase itself is limited to build scripts and theme configuration, with no executable application code.
The repository is a Rust CLI application for managing a todo list. It allows users to create and list todo items, with data persisted in a JSON file. The application is structured into modules: `core` (defining the data structure and command enum), `create` (handling item creation), `list` (handling listing and filtering), and `storage` (handling file I/O). It uses the `clap` crate for command-line argument parsing and `serde` for JSON serialization. The code demonstrates good Rust practices such as error handling with `Result`, modularity, and unit testing. However, it includes several unused dependencies, indicating some technical drift, and the last commit is over six months old, suggesting the project is not actively maintained.
This repository implements a full-stack AI-powered food recommendation application called 'δ»ε€©εδ»δΉ' (What to eat today). The system combines a Next.js frontend with a Python Flask backend that uses a GraphRAG (Retrieval-Augmented Generation) architecture leveraging Neo4j for graph-based recipe relationships and Milvus for vector similarity search. Users interact via a chat interface to receive personalized recipe recommendations with step-by-step cooking instructions. The application includes a comprehensive recipe knowledge base in markdown format, Docker-based deployment orchestration, and a modular RAG pipeline with intelligent query routing and session management.
This repository is the official Spring Boot getting started guide (gs-spring-boot). It provides a minimal web application that demonstrates Spring Boot's auto-configuration, embedded Tomcat, and actuator endpoints. The codebase consists of a single REST controller returning a static greeting string, a Spring Boot application class with a CommandLineRunner that lists all beans, and corresponding unit and integration tests. The complete variant includes Spring Boot Actuator for production-ready features. The repo serves purely as a tutorial scaffold, not a production-grade application. The architecture is a trivial monolith with no external services, database, or complex data flow. All source files are framework boilerplate generated or inspired by Spring Initializr. There are no reusable components or non-obvious patterns. The value lies in its documentation and as a starting point for learning Spring Boot. Despite its simplicity, the repository is actively maintained with up-to-date dependencies (Spring Boot 4.0.x, Kotlin 2.2.x, Gradle 9.3.x) and CI pipelines. It includes both Java and Kotlin implementations, and both Maven and Gradle build systems.
This repository contains the public information and learning resources for DigitalPlat FreeDomain, a free domain registration service. It includes a comprehensive book-length tutorial covering domain registration, DNS, website building, HTTPS, email records, operations, and security. The actual application source code is maintained separately in DigitalPlatDev/Domain-OSS. This repo is purely documentation and educational material, with no executable code or data pipelines. It serves as a guide for users to register and manage domains using DigitalPlat's service and external DNS providers. The documentation is well-structured, with clear separation between product-specific instructions and general textbook content, and includes practical exercises, checklists, and troubleshooting guides.
This repository is a Chinese-language book about front-end engineering, published as a VuePress static site. It covers the full lifecycle of front-end development: tech selection, coding standards, componentization, testing, build tools, CI/CD, monitoring, performance optimization, refactoring, microservices, and serverless. The codebase consists solely of markdown documentation files, VuePress configuration, a small advertisement component, and a GitHub Actions deployment workflow. It is not a software application or data pipeline; it is an educational resource. The repository is well-organized and actively maintained, but contains no reusable software components beyond standard VuePress boilerplate.
This repository is an educational tutorial for learning LangChain, LangGraph, and Deep Agents frameworks. It contains two tracks: 101 (fundamentals) and 201 (production patterns) with Jupyter notebooks and standalone agents runnable via `langgraph dev`. The code demonstrates multi-agent supervision, human-in-the-loop interrupts, middleware hooks, MCP tool integration, and a deep research agent with skills and memory. All agents use mock or simulated data (Chinook SQLite, Tavily search) and are designed for learning rather than production. The architecture centers on a centralized model configuration (`utils/models.py`) that supports multiple LLM providers, and reusable patterns like subgraph composition, dynamic prompts, and composite backends. While the code is educational and lacks production hardening (error handling, testing, security), it provides clear, well-commented examples of advanced agent patterns.
This repository is a collection of educational tutorials demonstrating various Python web frameworks and data analysis techniques. The majority of content consists of self-contained Streamlit applications showcasing features like QR code generation, form handling, session state, file-based routing, and multipage apps. It also includes examples of other frameworks such as FastAPI (with JWT authentication), Reflex, Marimo, NiceGUI, H2O Wave, Dash, and Fasthtml. A notable data research component is the Jupyter notebook analyzing pandas usage patterns across 1.2 million notebooks from UCSD Library archives. Architecture-wise, each tutorial is independent with no overarching system. Streamlit apps range from single-file scripts to multipage applications using st.navigation or custom file-based routing. Backend services like the FastAPI server in the authentication tutorial are paired with Streamlit frontends. The pandas analysis notebook follows an end-to-end pipeline: extracting notebooks from archives, parsing code via AST, filtering results, and visualizing API usage statistics. Key entry points are streamlit_app.py files in each tutorial directory, framework-specific server files (e.g., fastapi_server.py), and the pandasmostused.ipynb notebook. The repository exhibits moderate craftsmanship with several reusable patterns: a file-based routing system for Streamlit, a JWT authentication mechanism securing Streamlit via FastAPI backend, and parallel AST processing for bulk notebook analysis. However, it shows signs of technical drift including mixed use of deprecated and current Streamlit caching methods, hardcoded secrets in plaintext, and inconsistent dependency management. Maintenance is active based on recent commits, but the tutorial nature means some examples prioritize clarity over production best practices.
This repository is a comprehensive MySQL tutorial written in Chinese (with some English terms). It covers everything from installation and configuration to advanced topics like stored procedures, triggers, views, performance optimization, and transaction/locking basics. The content is structured as a series of Markdown files organized into chapters, plus a quick reference guide ('21 minutes') and a curated list of MySQL resources ('awesome-mysql'). There is no executable code, data pipeline, or application logic; it is purely a documentation and learning resource. The repository uses the 'idoc' static site generator to produce a website, but the primary value is the tutorial content. It is well-maintained with recent updates and has a high star count (4751).
This repository is a comprehensive collection of Go language learning notes, organized into multiple directories covering basic syntax, concurrency, project management, web programming, popular frameworks (e.g., Gin), microservices, standard library, and Go runtime internals. The content is presented as Markdown files with embedded code snippets and diagrams, serving as a structured tutorial for Go developers. It does not contain any executable Go code or reusable software components; it is purely educational material. The notes are well-structured and cover a wide range of topics from beginner to advanced, making it a valuable reference for learning Go.
This repository is an educational Docker tutorial centered on a functional Django REST API application. The core implementation uses Docker Compose to orchestrate two services: a Django web container (DRF) and a PostgreSQL container, with explicit health checks, JSON-file logging limitations, named volumes, and user-defined networks. The Django API itself is a basic music CRUD service with endpoints under /api/musics, secured behind authentication, and housed in a straightforward project structure that demonstrates the typical pattern of a containerized Python microservice. Beyond the main tutorial, the repo provides stand-alone Docker Compose examples covering advanced patterns: YAML anchors to reduce configuration repetition, docker-compose override to batch operate multiple databases, environment variable substitution with .env files, profiles for selective service boot, and cAdvisor for container resource monitoring. Each subfolder includes a README and ready-to-run configuration, making the entire repository a one-stop teaching resource for Docker Compose patterns and best practices. The code is not intended as a production tool but rather as a self-contained, reproducible learning blueprint that shows how to build and manage containerized stacks with modern features. While the application layer is deliberately simple, the Docker-compose layer embodies several good practices around health and logging, and the advanced usage of YAML anchors and overrides exemplifies high-quality configuration craftsmanship that is directly reusable in other projects.
This repository is a comprehensive knowledge base and research platform for quantitative finance, primarily focused on Chinese A-share markets. It contains hundreds of blog posts, course materials, and technical articles covering factor investing, machine learning strategies, backtesting frameworks, and data engineering. The codebase includes reusable data pipelines (ParquetUnifiedStorage, CalendarModel), a custom backtesting framework (Moonshot), and numerous factor analysis scripts. It also hosts a sophisticated set of AI-assisted academic research skills (paper writing, review, deep research) defined as Claude skill files. The repository serves as both an educational resource and a personal research lab, with content spanning from beginner Python tutorials to advanced factor construction and model training. While the code is functional and demonstrates solid data engineering patterns, it is tightly coupled to Chinese financial data APIs (Tushare, akshare) and contains hardcoded paths, limiting portability. The repository is actively maintained but shows signs of technical drift in some dependencies and content duplication.
This repository is the source code for an Elasticsearch tutorial e-book, built with VuePress to generate a static website. The content covers Elasticsearch 7.9.3, including installation, basic and advanced operations, architecture, and practical examples. The site is deployed via a simple shell script that builds the VuePress site and pushes to GitHub Pages. While the repository primarily serves as educational material, it also includes standard VuePress configuration and deployment scripts typical of documentation sites.
This repository is a large collection of tutorial lessons (numbered 006 to 281) focused on DevOps, cloud infrastructure, and backend development. It contains example applications in multiple languages (Go, Rust, Python, Node.js, Java, etc.) demonstrating common patterns like CRUD APIs, database integration, caching, and monitoring. The infrastructure-as-code examples cover Kubernetes (deployments, RBAC, ingress, service meshes), Terraform (AWS, GCP, Azure), Ansible, Helm, and CI/CD with ArgoCD. A significant portion is dedicated to observability with Prometheus, Grafana, and custom exporters. The repository serves as a companion to video tutorials and is not a single production application but a learning resource. It exhibits high duplication across lessons and some pseudo-code, but contains several reusable components for monitoring, infrastructure provisioning, and performance benchmarking.
This repository is a personal technical blog by the Chinese developer 'ε΄ηΎ½' (yayujs), containing a large collection of in-depth JavaScript articles and accompanying demo code. The articles cover core JavaScript concepts such as prototype chains, scope, execution context, closures, 'this' binding, call/apply/bind, new, inheritance, and many utility implementations like debounce, throttle, deep copy, array flattening, currying, partial application, memoization, type detection, and object equality. The demos are standalone HTML/JS files that progressively build up implementations (e.g., 8 versions of a template engine, 7 versions of debounce), making them excellent educational resources. The repository also includes a small VuePress plugin for code copy functionality and examples of module systems (ES6, CommonJS, RequireJS, SeaJS). It is not a production application but a curated set of learning materials. The code is well-structured for teaching purposes, though it lacks modern tooling, automated tests, and CI/CD. Some examples use older JavaScript patterns (var, ES5) and outdated library copies (Underscore 1.8.3, RequireJS 2.3.5), but the core concepts remain relevant.
This repository is a Jupyter Book titled 'Learning Machine', providing concise, intuitive explanations of machine learning and deep learning concepts. It is organized into sections covering basics, common tasks, layers, generative models, reinforcement learning, and more. The content is primarily textual with illustrative code snippets using numpy, scipy, and PyTorch. No real datasets are used; all examples are synthetic. The book is designed as a quick-reference handbook for learners, accompanying a university course. The repository is effectively archived by the author, with no planned updates.
This repository is a comprehensive educational resource for learning and building AI agents using the LangGraph framework. It consists of numerous tutorials and examples organized by chapter, covering fundamental concepts such as state graphs and tool usage, as well as advanced patterns including Retrieval-Augmented Generation (RAG), multi-agent supervisor systems, and human-in-the-loop workflows. Each lesson is designed as a runnable script that demonstrates specific LangGraph integrations with various APIs (e.g., OpenAI, Tavily, OpenWeatherMap) and techniques like vector stores with Chroma. While the repository serves its purpose as a learning tool, it exhibits some maintenance challenges due to code duplication (e.g., multiple copies of display_graph.py across chapters) and contains at least one known flawed implementation (the audit data monitor in chapter13) that suffers from schema mismatches and inefficient API usage, as indicated by log files.
This repository is an educational cookbook from Upstage, centered on their Solar LLM and Document AI APIs. It contains a large collection of Jupyter notebooks (Solar-Fullstack-LLM-101) that teach prompt engineering, RAG, cache-augmented generation, tool calling, and fact-checking with knowledge graphs, plus a Flask sample backend (Bremen) and a Flutter mobile app. The dominant character is a notebook-driven ML/research collection, but it is hybrid because it includes runnable application code (Flask API, Flutter app) and static data assets (OCR JSON, CSV knowledge bases).
This repository is a step-by-step tutorial for building a minimal text editor called 'kilo' in C, based on antirez's original kilo. It is not a runnable application itself but a set of documentation and a diff file (steps.diff) containing the incremental code changes. The tutorial guides the user through 184 steps to create a terminal-based text editor with features like raw mode input, cursor movement, file I/O, search, and syntax highlighting. The code is a single C file with no external dependencies, targeting POSIX terminals. The repository is primarily educational, with the actual source code embedded in the diff file rather than as a standalone project. It has not been updated since 2017, making it stale, but the code remains functional on modern systems due to its simplicity and use of standard C99.
This repository is a tutorial and research exploration of Graph RAG (Retrieval-Augmented Generation) combining vector databases (Weaviate) and knowledge graphs (RDF/SPARQL). It contains Jupyter notebooks that demonstrate building knowledge graphs from PubMed articles, World Bank documents, and Netflix movies, comparing vector search and structured query performance for retrieval-augmented generation. A Streamlit app provides an interactive demo for medical article search with MeSH term refinement and LLM summarization. The code is primarily educational, with reusable query modules for Weaviate, RDF, and Databricks, but lacks dependency pinning and has hardcoded credentials, limiting reproducibility. The repository is hybrid, with both application (Streamlit app) and data research (notebooks, datasets) elements, but the dominant character is data research as the core value lies in the experimental notebooks and data pipelines.