This repository is a curated list of noteworthy Java frameworks, libraries, tools, and resources (an 'awesome list'). It is not a software application in the traditional sense; rather, it is a documentation project that uses a Java-based generator script (GenerateReadme.java) to produce the README.md from a source file (README_SOURCE.md). The generator fetches GitHub statistics (stars, push dates, licenses) via the GitHub API and renders a structured Markdown document with categories, project entries, and resource groups. The repository also includes a GitHub Actions workflow that validates contributions and regenerates the README on pushes and weekly schedules. The codebase is well-crafted, with modern Java features, self-tests, and atomic file writes, but it is tightly coupled to the specific awesome-list format and not reusable as a general-purpose library.
BettaFish (åūŪč) is a multi-agent public opinion analysis system that automates the end-to-end process of gathering, analyzing, and reporting on public sentiment across 30+ Chinese social media platforms. The system is built around four specialized agents: QueryEngine (news breadth search via Tavily), MediaEngine (multimodal search via Bocha), InsightEngine (database mining via SQLAlchemy), and ReportEngine (template-based report generation with intermediate representation and HTML/PDF/Markdown rendering). A ForumEngine coordinates agent collaboration through log monitoring and LLM-generated moderator comments. The architecture is a monolithic Flask application that manages multiple Streamlit sub-applications and CLI tools, with a separate MindSpider crawler subsystem for data collection. While primarily an application, it includes significant data/research components such as sentiment analysis model training scripts (BERT, GPT-2, Qwen3, traditional ML), a Weibo sentiment dataset, and data pipeline scripts. The system relies heavily on external LLM APIs (OpenAI-compatible), search APIs (Tavily, Bocha, Anspire), and system dependencies (Playwright, WeasyPrint). The codebase demonstrates solid engineering with reusable patterns like robust JSON parsing, chart validation/repair, and a hardened GitHub Actions star-history script, but suffers from significant code duplication across engines and tight coupling to external APIs.