Eric — Full-Stack & AI Vision Developer

// ABOUT ME

Building intelligent systems
with code, decoding the visual world

Studying Software Engineering at South China University of Technology. Focused on vision model development and AI application. From frontend interfaces to backend services, from model training to edge deployment — I thrive on creating value across the entire tech stack. Experienced with YOLO, SAM, LLM, and Stable Diffusion in real-world projects.

Projects

Years Study

OSS Repos

GitHub Stars

eric@dev ~ zsh

❯

// EXPERIENCE

My Journey

From classroom to practice, constantly evolving

2022 — 2024

Programming Foundations · SE

@ South China University of Technology

Entered Software Engineering program, mastered C++ and data structures & algorithms, started exploring Web development and AI, building a solid CS foundation.

C++ Data Structures Algorithms HTML/CSS

2024 — 2025

Big Data & Backend Learning

@ South China University of Technology

Systematically studied big data stack (Flink, Kafka) and backend development (Java, Python), completed multiple course projects, gained deep understanding of distributed systems.

Flink Kafka Java Python

2025

Emerging Industries Intern

@ MIIT-affiliated Government Dept.

Conducted industry research and policy planning for emerging sectors (embodied intelligence, large models, computing power, chips), assisted in completing industry analysis reports and policy recommendations, gained deep insight into national emerging industry strategies.

Industry Research Policy Planning Embodied AI Large Models

2025 — 2026

Full-Stack Development Training

@ SCUT Research Lab

Responsible for full-stack web development using Vue + Spring Boot, completing multiple business modules and participating in system architecture optimization and API design.

Vue Spring Boot MySQL Redis

2026

Vision Model Dev Intern

@ Leading local tech company

Participated in R&D and delivery of visual detection models, designing and optimizing algorithms with YOLO, SAM, and similar models for industrial vision; validated and shipped engineering deployments of project models, driving visual models into real project production settings.

YOLO SAM PyTorch Docker

2026 - Present

MSc Student in Artificial Intelligence

@ Hong Kong University of Science and Technology

Pursuing a master's degree in Artificial Intelligence, studying machine learning, deep learning, natural language processing and computer vision. Coursework and projects connect model fundamentals and training methods with AI application development.

Machine Learning Deep Learning NLP Computer Vision

// TECH STACK

Tech Stack

From AI models to full-stack apps — multi-domain capabilities

AI / ML

Vision modeling: PyTorch · YOLO · SAM · DETR
Language & generation: LLM · NLP · Stable Diffusion
Inference & deployment: ONNX · C++

Exploring detection, segmentation, language and image generation, with model training, format conversion and edge deployment to bring AI into applications.

NeuralVision Pro PixelMind SDK

Select a field to trace its connections and explore a project

// SKILLS

Skills Matrix

Always learning, always improving

⚡

Frontend

Vue.js 90%

HTML / CSS / JavaScript 92%

WeChat Mini Program (WXML/WXSS) 80%

🔧

Backend

Java / Spring Boot 88%

Python / FastAPI 90%

PHP / RESTful API 82%

MySQL / openGauss / Redis 85%

🧠

AI / ML

PyTorch 90%

YOLO / SAM / DETR 88%

LLM / NLP / Stable Diffusion 78%

Model Design & Research 82%

☁️

Big Data / DevOps

Flink / Kafka 82%

Docker / MyBatis-Plus 85%

JWT / Redis / C++ 80%

Linux / Shell 78%

// FEATURED CODE

Good code is poetry written for machines

Training a neural network is like the human journey — from ignorance to perception, from mistakes to growth. Every backpropagation is an iteration of self-awareness.

PyTorch Neural Network Backpropagation

train.py

import torch, torch.nn as nn, torch.optim as optim
model = nn.Sequential(
    nn.Linear(784, 256), nn.BatchNorm1d(256), nn.ReLU(),
    nn.Linear(256, 128), nn.BatchNorm1d(128), nn.ReLU(),
    nn.Linear(128, 10)
)
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
# Initialize weights — Born with certain talents (random init)
for epoch in range(100):
    # Forward pass — Understanding the world with existing knowledge
    predictions = model(train_images)
    # Compute loss — Recognizing our shortcomings
    loss = criterion(predictions, train_labels)
    # Backpropagation — Learning from mistakes, computing gradients
    loss.backward()
    # Update weights — Growth and change, fine-tuning along gradients
    optimizer.step()
    optimizer.zero_grad()
# Ultimately, we learn not to replicate the past, but to generalize to the future

// TESTIMONIALS

What People Say

Real feedback from collaborators and classmates

Eric is one of the most engineering-intuitive classmates I've ever met. He quickly grasps complex technical problems and delivers elegant, efficient solutions, especially impressive in vision detection projects.

Li Wei

Mentor @ AI Tech Company

Working with Eric on course projects was a great experience. His dedication to practical technologies and turning ideas into working solutions benefited our whole group, especially in hands-on work around big-data coursework.

Zhang Hao

Project Lead @ SCUT

Eric's full-stack capabilities are impressive — from Vue frontends to Python/Java backends to AI model training, he handles it all with ease. Highly recommended!

Wang Xin

Intern Colleague @ Internet Company

// NEXT CHAPTER

System Upgrade

Currently pursuing an MSc in AI at HKUST

COMPLETED

🎓

SCUT

华南理工大学

Software Engineering · BSc

Full-Stack Dev & AI Vision Applications

PythonPyTorchVueYOLOSAM

▶

CURRENTLY ENROLLED

🚀

HKUST

香港科技大学

Artificial Intelligence · MSc

2026 — Present · Deep Learning & AI Research

Deep LearningNLPComputer VisionResearch

SYSTEM UPGRADE 0%

✓ BSc Completed ✓ Project Experience ✓ Enrolled at HKUST ◉ MSc in AI in Progress

2026

Enrollment Year

SCUT → HKUST

Current University

Research Focus

Eric.

Building intelligent systems
with code, decoding the visual world

My Journey

Tech Stack

Frontend