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Hi there 👋

I'm Steven-Zhl, a passionate fullstack developer from China. I enjoy creating web applications and exploring new technologies in the field of AI. I'm always eager to learn and grow as a developer, and I'm excited to share my projects and experiences with you!

Info

name: Steven-Zhl
pronouns: he/him
born: 2002
education:
  major: Computer Science (AI)
  degree: Bachelor
language: [Chinese, English]
job: Fullstack Developer (Vue.js & FastAPI)

GitHub Stats

⚠️ Work keeps me busy, so my activity might be low. More active during holidays!

Rest assured, I’ll respond to all messages as quickly as I can.

My GitHub Stats My GitHub Streak My Top Languages

🛠 My Skills & Scenarios

I firmly believe that tech stacks should be chosen based on actual needs, not just trends. Below is a breakdown of my expertise, categorized by the core scenarios I specialize in.

🤖 AI Agent Infrastructure

  • Service:
    • FastAPI FastAPI, I like asynchronous programming for better performance and scalability.
    • Pydantic Pydantic, it helps me build reliable services.
    • MongoDB MongoDB, it is better suited for JSON-based memory.
    • Redis Redis, useful for caching hot data, request rate limiting, simple message queue (based on Arq), and many other use cases.
  • Agent-related:
    • LangChain LangChain (v1.0), Despite many criticizing its over-engineering, I still think it's very insightful.
    • Qdrant, a practical vector DB with a concise Python API. I use it to build RAG pipelines, semantic-based search, and recommendation.
    • Dify, to build isolated and simple requirements and integrate them into the Agent.
  • Frontend:
    • Vue 3 Vue 3 (Composition API), Vite Vite, Tailwind CSS Tailwind CSS, XMarkdown

🕸 Distributed Web Scraping

  • Task Queue: RabbitMQ RabbitMQ (Distributed Message Broker)
  • Basic Scraping: httpx, BeautifulSoup4, re
  • Dynamic Rendering: playwright (Prioritize protocol-level scraping for performance; use Playwright as fallback.)
  • Related libraries for stealth features, proxy pool management, captcha solving, etc.
  • Data Pipeline: MongoDB MongoDB, Pandas Pandas

🐧 DevOps & Deployment

  • Containerization: Docker Docker & Docker Compose
  • System Admin: Linux Linux (CentOS/Debian), Systemd (Service Orchestration)
  • Automation: Bash Shell Scripting

📂 Future learning plans

  • Spring Spring and Spring Boot Spring Boot

    The engineering experience and system design accumulated from past Java development practices are invaluable assets for learning.

  • Flutter Flutter

    Expanding our business into mobile application development and exploring cross-platform solutions.

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