Java backend learner and builder. I like making projects run first, then turning them into clear, maintainable systems.
I am currently focusing on Java backend development and practical AI-assisted engineering.
- Core stack: Java, Spring Boot, MyBatis, MySQL, Redis
- Backend interests: REST APIs, database design, transaction boundaries, message reliability
- Current learning line: Canal, Kafka, outbox pattern, local AI agents, tool calling, RAG
- Working habit: verify the real runtime, write down the flow, and make repositories easier to understand
| Project | Direction | What it shows |
|---|---|---|
| Table-backend | Java backend, timetable and AI recognition | Backend API design, upload flow thinking, model-readable resource handling |
| out_box | Outbox, Canal, Kafka | Reliable message delivery, binlog-driven flow, transaction boundary learning |
| MCP_Tool | Python local tools and agent workflow | Tool calling, local automation, staged path toward search and RAG |
| gas-analysis-business | Java business module | Reading and organizing business-heavy backend code |
| MiniPay | Vue/course project | Frontend practice and full workflow thinking |
| my-first-extension | VS Code extension | TypeScript tooling and developer-experience experiments |
I am trying to connect these topics into one practical engineering path:
- Build Spring Boot services that can run locally and be debugged clearly.
- Understand how data changes become reliable events with MySQL, Canal, Kafka, and outbox-style design.
- Use AI tools as engineering assistants with explicit boundaries and verification.
- Keep learning notes, runtime checks, and README files close to the code.
For each serious repository, I try to make the README answer:
- What problem does this project solve?
- How do I run it locally?
- Where is the main business flow?
- What did I learn from building it?
- What should be improved next?