Zhanbo Chen
Zhanbo Chen avatar
@2AnbloBackend / AI

Technical notebook / resume / systems

I build backend systems and AI applications, then write down the tradeoffs behind them.

Zhanbo is a computer science student focused on Java backend engineering, microservices, AI agents, RAG, and infrastructure. This site is a working notebook for the systems, essays, and resume signals behind that work.

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Articles / Notes

Writing

Practical notes on Java backend engineering, Spring systems, AI agents, RAG, and the implementation details that are hard to preserve in a resume.

AI Agent/10 min read/2026-10-18

Zanbot Dev Diary, Pt. 1: Long-Term Memory and a Better Command Line

Today I did two main things: gave zanbot long-term memory, and replaced the `input()` loop with a proper interactive terminal. I also fixed two small issues along the way. Below is a record in the order things happened, with the focus on the problems I ran into.

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Algorithm/Learning Notes/27 min

CS546 Learning Notes: From Convolutional Kernels to LSTM’s Memory Conveyor Belt — Reviewing Sequence Modeling

I worked through Lecture 5 on Sequence Modeling from the beginning. The CNN section has plenty of diagrams that seem clear at a glance, but when I ask myself what the second diagram actually shows or what a filter is, I struggle to explain. This review focuses on understanding the slide diagrams and matching each symbol to its role in the formulas. The notes follow the order of the questions I asked. The numerical examples are for illustration: Section 5 uses the cs231n example cited in the slides; the others are not experimental results.

Algorithm/Learning Notes/19 min

CS546 Learning Notes: From TagLM and ELMo to Seq2Seq Decoding — Reviewing Contextual Embeddings

Lecture 6 has a long title: Contextual Embeddings, Model Architectures, and Decoding. It really covers three threads: how to give a word a representation that changes with its context, how an encoder-decoder turns one sequence into another, and how to pick a word at each step during generation. While reviewing, I asked a lot of "what is this figure actually showing" questions, and got a few things wrong along the way. This post follows the order of my questions, mistakes included. All numeric examples are made up to aid understanding; none of them are experimental results.

Learning Notes/Algorithm/21 min

CS546 Learning Notes: From Co-occurrence Matrix to Word2Vec, Connecting Negative Sampling and Language Models

Reviewing Lesson 4 on Language Models & Word Embeddings, I found that some concepts seem familiar at first glance, but when asked questions like "what is one dimension of this matrix" or "what probability does this actually predict", I still get stuck. This post organizes problems I encountered during review: first understanding concepts in my own words, then matching them with small examples and formulas to see if I've confused two things together. The numerical examples in this article are meant to help explain concepts, not experimental results.

All writing

Profile

Master's student in Computer Science at the University of Illinois Urbana-Champaign, bachelor's graduate in Computer Science and Technology from Capital Normal University (Rank 5%). Focused on AI application development and Java backend engineering, with practical experience in microservices architecture, Docker sandboxing, and LLM integration. Participated in the autonomous driving simulation project at NEVC, and led the development of an online programming training platform and an AI music creation platform.

Education

University of Illinois Urbana-Champaign

2026-08 / 2028-05

Master, Computer Science

Rank: N/A

Capital Normal University

2022-09 / 2026-07

Bachelor, Computer Science and Technology

Rank: 5%

Focus

AI Application Development

Building intelligent applications based on large language models, exploring cutting-edge AI engineering practices such as RAG and Agents.

Java Backend Development

Building high-performance, scalable enterprise backend services, proficient in the Spring ecosystem and microservices architecture.

GITHUB ACTIVITY

GitHub contribution activity and LeetCode progress

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Selected Systems

Projects as technical records.

A smaller set of systems framed by architecture, implementation scope, and tradeoffs instead of product-marketing cards.

01
Microservices/Java + Spring Cloud Alibaba

Online Programming Training Platform

A microservices-based coding practice system with large language model support

An online judge and intelligent Q&A system built on Spring Cloud Alibaba, with a multilingual code sandbox and extensible judging strategies.

JavaSpring Cloud AlibabaSpring BootNacosDockerMySQLRedis

Implementation notes

  • Built a microservices architecture with Spring Cloud Alibaba, separating user, problem, judging, and intelligent Q&A services.
  • Used Nacos for service registration and discovery to support reliable calls between services.
  • Designed the online judging module and a Docker-based execution sandbox to isolate submitted code, evaluate results, and record execution details.
Case studyGitHub
02
AI application/Spring Boot + FastAPI

MusicLens AI Music Creation and Analysis Platform

AI-powered music generation, sentiment analysis, and work management

A decoupled AI music platform offering music generation, work management, sentiment analysis, AI cover art, and text recognition in images.

Spring BootFastAPIPythonAI APIVue.jsMySQL

Implementation notes

  • Designed a decoupled architecture and built back-end services for users and administrators with Spring Boot.
  • Built a separate FastAPI service around Python AI capabilities, including music sentiment analysis.
  • Integrated third-party AI services for music generation from prompts and lyrics, AI cover generation, and OCR.
Case studyGitHub
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