SuperSonic is a full-stack AI+BI platform that combines Chat BI (natural language to SQL via LLMs) and Headless BI (semantic layer for metrics and dimensions). The backend is a modular Java Spring Boot application with modules for chat, headless query, authentication, and common utilities. It supports multiple databases (MySQL, PostgreSQL, ClickHouse, etc.) and integrates with various LLM providers (OpenAI, Ollama, Dify) and embedding stores (Milvus, Chroma, PGVector, InMemory). The frontend is a React application built with UmiJS and Ant Design, providing a semantic model editor, agent configuration, plugin management, and an embeddable chat SDK. The core NL2SQL pipeline involves schema mapping (rule-based and embedding-based), semantic parsing (rule and LLM), SQL correction (chain of correctors), translation to physical SQL, and execution. The system also includes chat memory for few-shot learning, plugin recognition, and post-query recommendations. While primarily an application, it contains data research elements such as evaluation scripts (benchmark.py, evaluation.py) and custom HanLP dictionaries for domain-specific entity recognition. The architecture is extensible via SPI-based component factories for parsers, correctors, mappers, and DB adaptors. The codebase is well-structured but shows signs of complexity with many modules, some commented-out code, hardcoded configurations, and a dependency on external LLM services that may introduce drift.

PageEyes Agent is a production-grade Python framework for natural-language-driven UI automation, published on PyPI as `page-eyes` (v1.3.9). It provides LLM agents that perceive screens and execute actions (click, input, swipe, assert) across Web (Playwright), Android (ADB), iOS (WebDriverAgent), HarmonyOS (hdc), and Electron (CDP). The core architecture separates a PlanningAgent that decomposes user instructions into atomic steps from per-platform execution agents that call tools through Pydantic AI's tool-calling framework. Screen perception is delegated either to a vision-language model in lightweight mode or to the bundled OmniParser2 service in full mode. Each run produces a self-contained HTML report with step logs, screenshots, element bounding-box overlays, and video, which can be uploaded to COS or MinIO. The repo is hybrid: it is primarily an application framework but embeds a significant ML-serving sub-project, OmniParser2, which is a FastAPI microservice running PaddleOCR for text detection, a fine-tuned YOLOv11n for icon detection, a LoRA-fine-tuned Florence-2 model for icon captioning, and a YOLO overlay detector for popups/loading states, with optional Milvus-based image caching. Model weights and tokenizer artifacts (including a ~96k-token BPE merges file) are committed to the repository, and the docs include detailed articles on Florence-2 LoRA fine-tuning and YOLO overlay detection, giving the repo a data-research flavor alongside its application character. The codebase shows genuine engineering skill: a generic Device abstraction with factory methods, a storage strategy pattern (COS/MinIO/Base64), a reusable skills system (e.g., gui-popup-handler), and an agent loop with planning/execution/reflection separation. CI/CD, tests, a changelog, and mkdocs documentation are present. Weaknesses include heavy ML dependencies with override conflicts, a committed binary blob (yadb), code duplication between the main package and OmniParser2, and coupling to Tencent-internal infrastructure (mirror URLs, email addresses, platform names), which limits external portability.