This is the ZJU ICICLES (浙江大学课程攻略共享计划) repository - a massive, student-maintained collection of course materials from Zhejiang University spanning 20+ academic disciplines. The repository contains lecture slides (PPT/PDF), lecture notes (Markdown/LaTeX/Typst), exam papers with solutions, homework assignments with code implementations, lab reports, textbook PDFs, and study guides. While classified as 'data_research' due to the presence of code artifacts in lab directories (compiler construction, data structures, operating systems, computer networks, digital logic), the dominant content is educational courseware - not a data pipeline or ML project. The repository serves as a knowledge base for ZJU students and demonstrates exceptional community curation. Key technical components include: a complete C compiler implementation (lex/yacc + codegen) in 编译原理/实验/, data structure implementations (red-black trees, graph algorithms, B-trees) in 数据结构基础/作业/dsHomework/, RISC-V OS lab code in 操作系统/ and 计算机组成与系统结构/, socket programming labs in 计算机网络/Lab/, and Verilog/FPGA code in 逻辑与计算机设计基础/ and 汇编与接口/. The repository is actively maintained with recent commits, but reproducibility of code artifacts is low due to course-specific dependencies, missing build configurations, and environment assumptions.
This repository is a Spring Boot training/courseware package rather than a production service. It consists of five independent, runnable Spring Boot applications under com.kousenit (demo, restclient, persistence, shopping, springai), accompanied by lab workbooks (labs.md, advanced-labs.md), course slides (slides.md), and CI workflows. The code demonstrates current-generation Spring and Spring Boot patterns: MVC and REST controllers, Java records for DTOs, RestClient and HTTP interfaces, JdbcClient and JPA data access, AOP aspects, RFC 7807 exception handling, and Spring AI chat clients. Although the dominant character is application code, it is educational example code, not a deployed system.
This is a Chinese-language educational companion to DeepLearning.AI's 'Agentic AI' course by Andrew Ng, maintained by the Datawhale community. The repository is a hybrid learning resource: ~60 translated markdown files across 5 modules (agentic workflow intro, reflection design patterns, tool use, practical building tips, highly autonomous agents) plus executable Jupyter notebooks and supporting Python modules that implement the lab exercises. It is not a production application or service — it is courseware with real, runnable agent code demonstrating current LLM agent patterns.