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.
Kagome is a self-contained Japanese morphological analyzer written in pure Go. It provides tokenization with part-of-speech tagging, multiple segmentation modes (normal, search, extended), and dictionaries embedded in the binary for easy deployment. The system includes a CLI tool with subcommands (tokenize, server, lattice, sentence), a RESTful API server mode, WebAssembly support for browser use, and C library API for FFI integration with languages like Python and PHP. The architecture centers around a lattice-based Viterbi algorithm for tokenization, with clean separation between tokenizer logic, dictionary handling, filtering utilities, and interface layers.
Patina is a deterministic, pattern-based AI text detection and humanization tool, primarily targeting Korean but supporting English, Chinese, and Japanese. It operates as a Node.js CLI (`patina-cli`), a web API (Vercel serverless), and an agent skill (Claude Code, Codex, Cursor). The core workflow: analyze source text for AI-typical patterns (stylometry, lexicon, discourse tells), build a structured prompt, invoke an LLM backend (OpenAI, Claude, Gemini, Kimi, etc.), verify meaning preservation (MPS/fidelity), and output a humanized rewrite with a deterministic audit trail. It enforces strict meaning guards (number safety, polarity, causation) and provides scoring, diff, and verification modes. The architecture is modular: a shared deterministic analysis core (`src/features/`) runs identically in Node and browser; a prompt builder assembles context from pattern packs, document types, and personas; multiple backend adapters abstract LLM invocation; and a verification pipeline ensures fidelity. The project includes extensive research infrastructure (benchmarking, human evaluation panels, corpus management) and a commercial Pro tier with Polar license validation. It is well-tested with over 200 unit/e2e tests, CI/CD, and security hardening (SSRF guards, input fencing, atomic writes).
This repository is the Korean translation of the Modern JavaScript Tutorial (https://javascript.info). It contains a comprehensive collection of educational articles, tasks, solutions, and interactive code examples covering core JavaScript, browser APIs, networking, animations, web components, and regular expressions. The content is structured as static markdown files with embedded code snippets and self-contained HTML demos. It is not a production software application but a static educational resource. The repository demonstrates consistent translation conventions and includes reusable teaching patterns such as decorators, custom events, drag-and-drop algorithms, and event delegation. Some content references older browser behaviors and deprecated APIs (e.g., `__proto__`, XMLHttpRequest), but also covers modern features like async/await, Promises, and modules. The code examples are clean and well-documented, making it a valuable learning resource for Korean-speaking developers.
This repository is a Jekyll-based static site serving as an open-source Korean textbook on convex optimization, titled 'Convex Optimization for All'. It contains 25 chapters covering theory (convex sets, functions, duality) and algorithms (gradient descent, subgradient, Newton, ADMM, etc.), with mathematical notation and practical examples. The site is built for GitHub Pages, uses the Lanyon theme, and includes comment functionality via Utterances. While the content is comprehensive and well-structured, the repository is primarily a content delivery platform rather than a software application. There is a small Python utility script for GitHub issue management, but the core value is the educational material. The project appears to be in a maintenance stalemate, with most content last updated in 2021 and no recent commits. The architecture is standard Jekyll with no novel software engineering patterns.