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laolvfan/README.md

LAOLVFAN / BACKEND × AI RELIABILITY

Build systems. Break assumptions.

Nanjing University undergraduate · Java backend developer · TensorFlow/XLA bug investigator

NJUer focused on reliable Java backend systems and correctness in AI frameworks.

$ ./profile --status

identity    NJU undergraduate
building    Java / Spring backend systems
exploring   TensorFlow & XLA correctness
approach    ship the system · reproduce the edge case · verify the result

Selected work

Git-Guild

An RPG-inspired collaboration platform that turns real repository work into quests—from publishing an issue to submitting, reviewing, and merging a pull request.

Architecture · Backend Engineering · Team Project

  • Worked across system architecture, authentication, quest/review workflows, Gitea integration, and CI-backed verification.
  • Built with Java 17, Spring Boot 3, Spring Security, JPA, MySQL, Docker Compose, and Vue 3.
  • Designed as a working end-to-end system rather than a static course demo.

→ Explore the repository

TensorFlow / XLA bug investigations

I investigate observable differences between TensorFlow eager execution, graphs, and XLA, then reduce them to reproducible bug reports.

Selected reports:

→ View all TensorFlow reports

Working set

Java Spring Boot MySQL Docker TensorFlow 

backend     Java · Spring Boot · Spring Security · JPA · REST APIs
data        MySQL · Redis
infra       Docker Compose · GitHub Actions · Gitea
ai systems  TensorFlow · XLA · numerical and compiler edge cases

Current direction

  • Building backend systems whose behavior is explicit, testable, and observable.
  • Learning from real framework failures instead of treating abstractions as black boxes.
  • Turning investigations into small, reproducible artifacts that other engineers can verify.

READ THE CODE · REPRODUCE THE BUG · LEAVE THE SYSTEM BETTER

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