Anduin2017/HowToCook 是一个由社区驱动的中文菜谱仓库，旨在为程序员提供形式化、可量化的烹饪指南。该仓库包含超过 700 个 Markdown 菜谱文件，按照菜系（素菜、荤菜、水产、早餐、主食等）组织，每个菜谱都遵循严格的模板格式，包含精确的计量、步骤和卡路里预估。仓库还包含辅助工具如自动 README 生成器（readme-generate.js）、Markdown 质量检查脚本（manual_lint.js）以及 GitHub Actions CI 流程，确保菜谱的一致性和可读性。虽然本质上是一个文档/数据仓库，但其组织方式、质量控制工具和社区协作模式使其成为一个高质量的开源数据项目，方便被其他应用（如菜谱API、小程序）消费。
PDFMathTranslate is a production-grade CLI and GUI application for translating PDF documents while preserving scientific formatting (formulas, charts, tables, and fonts). It uses an ONNX-based layout detection model (DocLayout-YOLO) to understand document structure, supports OCR for scanned PDFs, and integrates with numerous translation services (Google, DeepL, OpenAI, Ollama, etc.). The system operates as a monolithic package with multiple entry points: a command-line tool, a Gradio web interface, a Flask+ Celery HTTP API, and an MCP server for AI assistant integration. Internally, it follows a pipeline architecture: input PDF → optional OCR → layout analysis → text extraction → translation (with caching) → typesetting → bilingual output PDF. Notable engineering patterns include a pluggable translation backend via a kernel registry, a configurable translator base class with retry and caching, and a singleton configuration manager. The code is well-structured with test coverage and CI/CD pipelines, though the dependency on a Git submodule for the 'precise' kernel introduces minor coupling risk.
OpenCC (Open Chinese Convert) is a deterministic, dictionary-based Chinese text conversion library and CLI tool that converts between Simplified Chinese, Traditional Chinese, and regional variants (Taiwan, Hong Kong, Japan), including experimental Small Seal Script (Unicode 18.0). It is used in production by input method engines (RIME, fcitx), dictionary tools (GoldenDict), and NLP pipelines for text normalization and preprocessing. The core is a C++ library with a plugin-based segmentation system (built-in mmseg, optional jieba). Conversion is config-driven via JSON files that define normalization, segmentation, and a chain of dictionary groups with match policies (short_circuit, union). Dictionaries are compiled from text files into binary formats (marisa-trie .ocd2, legacy .ocd). The library exposes C, C++, Python (pybind11), and Node.js (N-API) APIs. A CLI tool provides conversion, inspection, ambiguity detection, and dictionary management. Data flows: text input is segmented, then each segment is looked up in a chain of dictionaries (character, phrase, regional variants). The first match is used (short_circuit) or all matches are unioned. The result is reassembled. The system supports streaming conversion with bounded memory via chunked processing. External dependencies include marisa-trie, darts-clone, rapidjson, pybind11, Google Test, and Google Benchmark; optional jieba (C++ fork) for segmentation plugin.
The repository implements Trime, a fully functional Android input method (IME) based on the Rime input engine. It provides a highly customizable keyboard with YAML-based themes, color schemes, key layouts, and gesture support. The core architecture is a Kotlin/JNI bridge to the native librime library, with a service-based IME lifecycle (`TrimeInputMethodService`), a daemon for Rime session management, and a rich UI layer for candidate display, clipboard, popups, and liquid keyboards. The application also includes a settings interface for schema management, user dictionaries, sync via Storage Access Framework, and clipboard history. The codebase is modular, with clear separation between Rime interaction, theme management, UI delegates, and data persistence, using modern Android patterns (Room, Paging, WorkManager, Coroutines, Kodein DI). It is actively maintained with frequent commits and extensive test coverage for core components. The YAML theme system is particularly sophisticated, featuring typed color resolution with fallback chains and a custom parser that handles anchors and aliases.
Read Frog is a browser extension for AI-assisted language learning. It provides in-page translation, video subtitle translation, text-to-speech, and a selection toolbar with custom AI actions. The extension supports over 30 language model and translation providers, configurable via a unified provider interface, with features like automatic page translation, hover translation, and AI-powered note suggestions. The architecture is built on the WXT framework (MV3) for cross-browser compatibility, with a background service worker orchestrating translation queues, caching, and state management. The frontend uses React 19, Jotai for state, TanStack Query for data fetching, and a custom Base UI component library. The codebase is highly modular with extensive test coverage, a versioned configuration migration system (100+ schema versions), and sophisticated DOM manipulation for in-place translation.
This repository is a bilingual (Chinese/English) open-content book titled 'Life Level-up Guide' (人生进阶指南), focused on lifelong learning, English acquisition, AI collaboration, personal development, and evidence-based growth. It is published as a VitePress static documentation site with deterministic EPUB and PDF exports. The codebase includes a sophisticated build toolchain with content validation, navigation synchronization, accessibility enhancements, and extensive Playwright end-to-end tests. While not a traditional software application, it demonstrates high craftsmanship in content management, automated publishing, and quality assurance. The repository is actively maintained with CI/CD pipelines, link auditing, and bilingual parity enforcement.
This repository is a comprehensive Chinese-language Rust tutorial titled 'Rust Course' (Rust 语言圣经). It is primarily an educational documentation project built with mdBook, producing a static website and a PDF. The content covers Rust from basic syntax to advanced topics like async, unsafe, concurrency, and performance optimization, with over 170 chapters and 1.1 million characters. It also includes a forked and translated version of the rustlings interactive exercise system (rustlings-zh), which provides a CLI tool for hands-on practice. The repository is not a production application but a learning resource; however, the rustlings-zh component is a functional CLI application. The architecture is a static site generator (mdBook) plus a standalone Rust CLI tool. Key entry points are the mdBook configuration (book.toml) and the rustlings-zh main.rs. The project is well-maintained, tracks Rust versions up to 1.89, and has active community contributions.
This is a mature PHP library for converting Chinese characters (simplified and traditional) to pinyin romanization. It provides a clean facade (`Pinyin` class) with static methods for common conversions (sentence, phrase, name, heteronym, permalink, abbreviation) and supports multiple output tone styles (symbol, number, none). The architecture employs the strategy pattern via a `ConverterFactory` that selects among three converter implementations: `MemoryOptimizedConverter` (~400KB memory), `CachedConverter` (~4MB, faster), and `SmartConverter` (adaptive). An abstract base class (`AbstractConverter`) handles shared preprocessing, tone formatting, and dictionary loading. The library includes a CLI tool (`bin/pinyin`) for command-line use, comprehensive test coverage, static analysis at PHPStan level 9, and a data generation pipeline (`bin/build`) that downloads and patches dictionary data from external sources. The dictionary is split across multiple PHP array files in `data/`, covering characters, surnames, and multi-word phrases with polyphone disambiguation. The codebase is well-structured, follows modern PHP practices (8.1+ enums, typed properties), and is actively maintained with CI and dependabot.
ChatTTS is a generative speech model application designed specifically for dialogue scenarios, functioning as a Text-to-Speech (TTS) library with integrated serving infrastructure. The core logic resides in the ChatTTS package, where `ChatTTS/core.py` defines the main inference interface. The architecture combines a GPT-based language model (`ChatTTS/model/gpt.py`) for token generation, a DVAE (`ChatTTS/model/dvae.py`) for audio compression, and Vocos as a vocoder. The system supports multiple speakers, prosodic control (laughter, pauses), and text normalization for Chinese and English. Deployment is handled through multiple entry points: a FastAPI-based API server (`examples/api/main.py`), a Gradio WebUI (`examples/web/webui.py`), and CLI tools (`examples/cmd/run.py`). A significant engineering highlight is the integration of vLLM (`ChatTTS/model/velocity/`) to optimize inference throughput for the GPT component. The repository includes robust asset management (`ChatTTS/utils/dl.py`) for model weights with SHA256 verification and ONNX export capabilities (`examples/onnx/exporter.py`) for cross-platform deployment. While the underlying model is research-grade (trained on 100k+ hours of audio), the repository structure prioritizes application delivery over experimental research. It provides a stable library interface with clear separation between model logic, audio processing (`tools/audio/`), and serving layers. The codebase demonstrates high craftsmanship in handling stochastic generation reproducibility (`tools/seeder/ctx.py`) and speaker embedding management (`ChatTTS/model/speaker.py`), making it a production-ready TTS solution rather than a raw research notebook collection.
chubbyskills is a personal knowledge management CLI toolkit that ingests content from over a dozen platforms (YouTube, Bilibili, X, Xiaohongshu, WeChat, Douyin, Podcasts, etc.), transcribes audio/video locally or via cloud providers, and outputs structured Markdown notes with YAML frontmatter (schema v1). The system is architected as a collection of modular "skills" (one per platform) orchestrated by a unified CLI (`tools/chubby.py`), with shared utilities in `chubby_common/`. It includes an incremental knowledge base indexer using SQLite FTS5 and optional semantic embeddings, an MCP server exposing six tools for AI agent integration, and a deterministic evidence brief generator with integrity validation. Operation is local-first: transcription uses SenseVoice and faster-whisper, video downloading uses yt-dlp, and all processing is designed to work offline. Cloud transcription (Atlas/MuAPI) and LLM enrichment (DeepSeek) are optional extensions. The repository also provides a skill installer, platform smoke testing, output validation, and retry mechanisms with credential redaction. Despite the breadth, the codebase maintains clean separation of concerns, with the shared library standing out as a reusable zero-dependency core.
LessAI is a Tauri 2 desktop application for AI-assisted document rewriting. It supports importing documents in plain text, Markdown, LaTeX, DOCX, and PDF formats, segmenting them into rewrite units, and sending them to an OpenAI-compatible LLM for rewriting. The system provides a workbench view with a side-by-side editor and review panel, enabling users to view diff hunks, accept/reject suggestions, and finalize writeback to the original file. For complex formats like DOCX and PDF, a slot-based model preserves locked placeholders (e.g., tables, images, hyperlinks) while allowing editable text regions to be rewritten. The application includes a web demo mode for plain text only, a full settings UI for provider configuration, and an in-app update mechanism. The architecture is a monolith split between a Rust backend (Tauri commands, document adapters, rewrite pipeline) and a React/TypeScript frontend with custom hooks, progressive rendering, and a slot-based structured editor. The codebase is well-structured with extensive unit tests, roundtrip test fixtures, and CI/CD pipelines for cross-platform bundles.
This repository is the personal blog and knowledge base of digoal, a prominent PostgreSQL DBA and database expert. It contains over a decade of blog posts (2010–2026) covering PostgreSQL internals, performance tuning, database architecture, and related topics, along with a large collection of images, PDFs, and other attachments. The blog is organized by year and month, with each post as a markdown file. The repository also includes a 'skills' directory that defines reusable AI agent skill definitions (SKILL.md files) for various tasks such as fact-checking, stock prediction, bug hunting, and course writing. These skills are structured with agent prompts, reference materials, and supporting scripts, making them extractable and reusable in AI-assisted workflows. The repo is actively maintained and serves as both a reference for database professionals and a library of AI agent capabilities.
This repository is a documentation-focused resource providing security guidelines for SaaS startups organized by company growth phases. It contains no executable code or data processing pipelines, but rather offers educational content in Markdown format covering topics from basic security hygiene (password management, encryption) to advanced compliance and incident response strategies. The material is structured as a practical guide referencing real-world scenarios and external resources, with accompanying images illustrating key concepts. While not a software project, it functions as a reusable knowledge base that engineers can reference when implementing security practices in early-stage startups.
This repository is a static website for a Chinese-language educational course titled 'Deep Agents 实战' (Deep Agents in Action). It provides 16 chapters of technical content on building AI agents using LangChain and LangGraph, including text, video links, and downloadable PDFs. The site is built with Astro 6, Tailwind CSS 4, and TypeScript, and is deployed to GitHub Pages via GitHub Actions. The codebase is well-structured: content is authored in Markdown under `content/`, metadata is managed in `scripts/chapters.json`, and a preprocessing script injects frontmatter into the Astro content collection. Custom Astro components handle layout, navigation, theme toggling, and a responsive table of contents. The repository also includes CI/CD workflows for asset validation and cross-platform installation testing. While the primary purpose is educational content delivery, the build and content pipelines demonstrate reusable patterns for static site generation with Astro.
This repository provides curated Chinese character sets (3500 common, 7000 common, and a comprehensive set) combined with symbols (Latin letters, digits, punctuation, full-width variants). The data is sourced from official Chinese character frequency lists and community-maintained character tables. The repository includes simple Node.js scripts for building the final text files from source files, merging new characters, and removing duplicates. The primary use case is generating character sets for Unity TextMeshPro font creation, but the data can be used in any application requiring a comprehensive Chinese character inventory. The codebase is minimal and well-documented in AGENTS.md, with clear instructions for editing source files and regenerating outputs. The repository is actively maintained with no open issues and a recent commit.
This is the largest structured Chinese classical poetry dataset on GitHub, containing ~55,000 Tang poems, ~260,000 Song poems, ~21,000 Song ci (lyric poetry), and additional classical texts (Four Books and Five Classics, Book of Songs, Chu Ci, Yuan opera, etc.). The data is distributed as JSON files organized by dynasty and genre, with each poem containing author, title, and paragraph fields. The repository serves as a foundational dataset for Chinese NLP, digital humanities, and cultural heritage projects. The codebase is minimal — essentially a data repository with a JSON validation test (test_poetry.py), a simple data loader (loader/data_loader.py), and a web scraper for Song ci (宋词/UpdateCi.py). The data itself is the product of web scraping from sources like ctext.org and other online repositories, with manual curation and error correction over time. The repository includes an 'error' directory containing records with data quality issues (corrupted characters, missing content, etc.), which demonstrates transparency about data limitations. The data loader provides a basic abstraction for extracting poem bodies across different datasets, but the overall engineering is straightforward. The value lies entirely in the data breadth and accessibility — no complex transformations, feature engineering, or analytical pipelines are present. The repository is widely used (52K+ stars) as a reference dataset for Chinese poetry generation, analysis, and educational applications.
This repository provides Simplified Chinese language packs for WHMCS (Web Host Manager Complete Solution), a web hosting billing and automation platform. It contains PHP array files mapping English interface strings to Chinese translations for multiple versions of WHMCS (from 7.x to 9.0.4). The translations are generated using AI-assisted machine translation followed by human proofreading and optimization, as described in the accompanying documentation. The repository is actively maintained, with updates aligned to WHMCS releases, and uses the override mechanism for compatibility with newer WHMCS versions. While the repository does not contain reusable data processing components, it serves as a stable, versioned resource for deploying WHMCS in Chinese-speaking environments.
EhViewer_CN_SXJ is an Android application for browsing, searching, and downloading content from E-Hentai (a manga/doujinshi platform). It provides a full-featured gallery viewer with offline download management, image caching, and archive extraction. The app integrates native C/C++ libraries (libjpeg-turbo, libpng, libwebp, giflib) via JNI for high-performance image decoding, and uses a custom OpenGL-based rendering framework (GLView) for smooth gallery navigation. The architecture is a monolith with a scene-based UI navigation system, a SQLite-backed persistence layer (greenDAO), and a custom HTTP client built on OkHttp with proxy and cookie management. The codebase is a mix of Java and Kotlin, with extensive native code for image processing and SIMD optimizations (SSE, NEON, MIPS). While the app is functional and actively maintained, it exhibits significant technical debt: tight coupling to E-Hentai's HTML structure, deprecated Android API usage, a forked Android-like environment, and heavy vendored native libraries that require ongoing security updates. The project demonstrates some reusable patterns (Conaco caching, UniFile abstraction, custom collections) but overall is a domain-specific application with limited generalizability.
This is a research repository from the SCIR-HI lab at Harbin Institute of Technology, focused on instruction-tuning large language models (LLaMA, Alpaca, Bloom, Huozi) with Chinese medical knowledge. The core contribution is the construction of Chinese medical instruction datasets using structured knowledge bases (cMeKG) and medical literature via GPT3.5, followed by LoRA-based fine-tuning. The repository provides scripts for fine-tuning, inference, and model export, along with pre-trained LoRA weights. While it demonstrates a complete research pipeline (data generation, training, evaluation), it is primarily a research artifact rather than a production application. The code is functional and includes a clean, reusable Prompter class for template management, but lacks software engineering rigor (no tests, no CI). Reproducibility is moderate: dependencies are pinned, but the data generation process is not fully automated, and external base models and LoRA weights must be obtained separately.
This repository is a curated collection of Chinese vocabulary lists for standardized tests (HSK 3.0, BCT, YCT), enriched with pinyin, zhuyin, part-of-speech tags, frequency data, and English definitions. It includes Python and JavaScript scripts for data cleaning, format conversion (TXT/TSV/XLSX), POS tag correction using SUBTLEX frequency corpus, and pinyin tone conversion. The data is organized by test level and version (2021 and 2025 HSK standards), with multiple output formats suitable for flashcard apps (Anki, Pleco) and language learning tools. The repository is primarily a data asset with supporting ETL scripts, not a production application. It demonstrates solid data engineering practices for language resource curation, though it suffers from some file duplication and hardcoded paths.