AI Full Stack Development Course

AI Full Stack Development Course | Original

Home 2026.07

A series of AI full stack development courses, covering AI + Android, AI + iOS, AI + Frontend, AI + Backend, AI + Full Stack, and Cloud Platforms. 50 CNY per session (30 minutes). Pay per session, no package required. I’m happy to share what I’ve learned.

My Background

Male, born in 1995, Chinese citizen, resides in Guangzhou. Achieved top 300 in Guangdong NOIP 2011 and solved around 1000 algorithm problems online.
Previous experience includes LeanCloud, Beijing Square Root, Beyondsoft (outsourced to a Singapore bank), Farben Information (outsourced to a UK-based international bank) and a global IT outsourcing company (outsourced to a UK-based international bank).
Accumulated 12 years of professional experience. Specializes in mobile (Android, iOS) and full-stack engineering, with 1 year in machine learning/big data.
Good understanding of HTML, CSS, JavaScript, Vue, React, Angular, iOS/Android, Java, Spring, MySQL, Redis, distributed systems, and cloud platforms (Alibaba Cloud, AWS, Azure).
Fun Live — GitHub, a WeChat-powered technical live streaming platform for developers focusing on iOS, Android, backend, and AI content. Operated from 2016 to 2017 with around 30k users.
Personal blog — lzwjava.github.io, an AI-driven one-stop blog system with AI-powered translation and beyond. Operated from 2021 to present, ~60k monthly views (Cloudflare).
Native in Chinese, proficient in English (IELTS 6.5). Read over 320 books.
AI Enthusiast — trained small LLMs (nanoGPT) on H200, AMD MI300X and RTX 4070 GPUs, and built projects using OpenRouter and Claude Code (~3B tokens used).
More details: Resume · Software Portfolio · AI Portfolio · Life Portfolio

Course Philosophy

AI is now a practical skill. You don’t need to understand neural network math to be effective. What you need is:

Know how to prompt well
Know which tool for which task
Know how to chain AI tools into real workflows
Know how to build things without writing everything yourself

The best way to learn is by building something real.

Who Is This For

Program managers — want to understand what AI can do for your team, how to spec AI features, how to evaluate AI tool outputs
University students — any major (arts, science, engineering). AI is now a basic literacy skill
Middle school students — curious about AI and want to learn with a real practitioner
Non-coders — you don’t need to write code to use AI effectively. Many tools let you build with natural language
Anyone who wants practical AI skills, not academic theory

Course Format

50 CNY per session, 30 minutes each. Pay as you go — no upfront package.
One-to-one via Zoom or Tencent Meeting. Sessions are recorded.
Between sessions, quick text follow-ups (under 15 minutes) available outside sleep time.
Each session is self-contained — no homework required unless you want it.
You pick the topic each time. It’s your course.
Join a WeChat community of 3k+ engineers in China for networking and discussions. Contact via WeChat: lzwjava.

To enroll, send an email to lzwjava@gmail.com or add me on WeChat: lzwjava (same ID). Please include a brief introduction about yourself and what you’d like to learn.

Sample Topics

Pick any. Or suggest your own.

AI + Android Development

Build an Android app from scratch using AI coding tools
Integrate LLM APIs (OpenAI, Claude) into Android apps
Build AI-powered features: chat, image recognition, text-to-speech
Use AI to generate UI layouts, ViewModels, and data layer code
Debug Android apps with AI — let the agent find your NullPointerException
Optimize APK size and performance with AI suggestions

AI + iOS Development

Build an iOS app using AI (SwiftUI + AI pair programming)
Integrate ChatGPT/Claude API into iOS apps
Use Core ML and Apple’s on-device AI frameworks
Generate Swift code, Core Data models, and network layers with AI
Debug iOS crashes and memory leaks with AI assistance
Build and submit to App Store with AI-guided workflows

AI + Frontend Development

Build responsive websites with AI (HTML, CSS, JavaScript, React, Vue)
Create beautiful UIs with AI-generated Tailwind CSS
Build interactive prototypes in minutes — no design skills needed
Use AI to write unit tests and integration tests
Debug frontend issues (layout, API calls, state management) with AI
Performance optimization: lazy loading, code splitting, bundle analysis

AI + Backend Development

Build REST APIs and GraphQL servers with AI assistance
Design database schemas and write SQL with AI
Build serverless functions with AI (Cloudflare Workers, AWS Lambda)
Set up authentication, authorization, and data validation
Debug server errors, performance bottlenecks, and race conditions
Write unit tests, integration tests, and API documentation

AI + Full Stack

Build a complete web app from frontend to database
Connect React/Vue frontend with Node.js/Python backend
Deploy full stack apps (Vercel, Railway, Cloudflare, self-hosted)
Build real-time features: WebSockets, SSE, live updates
Full stack debugging — trace an issue from browser to database
CI/CD pipeline setup with AI assistance

Cloud Platforms

Covers deployment, infrastructure, and operations across major cloud providers:

Google Cloud Platform (GCP) — Compute Engine, Cloud Storage, Cloud SQL, Cloud Run, GKE, Cloud Functions, IAM, VPC networking
Amazon Web Services (AWS) — EC2, S3, RDS, Lambda, ECS/EKS, API Gateway, CloudFront, IAM, VPC, CloudWatch, auto-scaling
Microsoft Azure — Azure App Service, Azure Functions, Azure SQL Database, AKS, Event Hubs, DevOps, Active Directory, Blob Storage
Alibaba Cloud — ECS, OSS, RDS, ACK, Serverless, CDN, DNS — the dominant cloud in China
DigitalOcean — Droplets, App Platform, Spaces, managed databases — simple, predictable pricing for side projects and startups
Vercel & Cloudflare — frontend deployment (Vercel + Next.js), edge computing (Cloudflare Workers, Pages, R2, D1), CDN, DDoS protection
Multi-cloud strategies — when to use which provider, cost optimization, avoiding vendor lock-in
Cloud migration — moving apps from on-premise to cloud, database migration, traffic shifting

DevOps & Infrastructure

Docker — containerize applications (write Dockerfiles, multi-stage builds, docker-compose for local development)
Kubernetes (K8s) — pods, deployments, services, ingress, configmaps, secrets. Deploy with AI-generated YAML
CI/CD pipelines — GitHub Actions, Jenkins, Azure DevOps, GitLab CI. Automate build, test, and deploy
Infrastructure as Code — Terraform for cloud resources, Ansible for configuration management
Monitoring & observability — Prometheus + Grafana for metrics, ELK stack (Elasticsearch, Logstash, Kibana) for logs, structured logging
Reverse proxies — Nginx configuration, SSL/TLS termination, rate limiting, load balancing
Linux fundamentals — SSH, file permissions, process management, systemd, journalctl, network troubleshooting (curl, ping, tcpdump, traceroute)

Databases

Relational — MySQL, PostgreSQL, SQLite, IBM Db2. Schema design, indexing, query optimization
NoSQL — Redis (caching, queues, sessions), MongoDB (document store), object storage (S3, OSS, R2)
Cloud databases — Amazon RDS, Cloud SQL, Azure SQL Database, managed databases on DigitalOcean, Supabase
Working with AI — let AI generate complex SQL queries, explain query plans, suggest indexes, write migration scripts

Programming Fundamentals

Core concepts — variables, data types, functions/methods, conditionals, loops, arrays/lists, maps/dictionaries, classes and objects, inheritance, interfaces
OOP principles — encapsulation, inheritance, polymorphism, composition over inheritance
Common patterns — MVC, singleton, factory, observer, repository. When to use them and when not to
Data structures — arrays, linked lists, stacks, queues, hash tables, trees, graphs. Enough to understand what your code does
Algorithms — sorting, searching, recursion, dynamic programming basics. AI can write them, you need to know when to use which
API design — RESTful conventions, HTTP methods and status codes, request/response structure, pagination, rate limiting, OpenAPI/Swagger documentation
Concurrency — threads, async/await, callbacks, race conditions, deadlocks, thread safety basics

Software Principles & Debugging

Goal-driven development — define what you’re building before you write a line of code. Write a spec, draw a diagram, then code
Software principles — DRY (Don’t Repeat Yourself), KISS (Keep It Simple, Stupid), SOLID (Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion), YAGNI (You Ain’t Gonna Need It), separation of concerns. How these apply in the age of AI-assisted coding
Debugging methodology — systematic approach: reproduce the bug, isolate the root cause, fix it, verify the fix. How to read stack traces and error messages. Using breakpoints and watch variables. The art of narrowing down — binary search through code
Adding logs effectively — what to log (entry/exit, key decisions, errors), when to log (before throwing, after catching, at state transitions), log levels (error, warn, info, debug, trace). Log-driven debugging patterns: adding temporary logs to trace data flow
Testing — unit tests, integration tests, end-to-end tests. Why tests matter. TDD basics: red-green-refactor. How to generate tests with AI. Mocking and test fixtures
Long-running tasks — background jobs, async processing, task queues (Redis Bull, SQS, RabbitMQ, Celery), progress tracking, retry strategies, idempotency
Error handling — try/catch, error propagation, graceful degradation, user-facing error messages

Version Control & Collaboration

Git basics — init, add, commit, push, pull, branch, merge, rebase, resolving merge conflicts
Working with AI — AI can write the code, you need to manage the code. Code review workflows, branching strategies (GitHub Flow, Git Flow)
Pull requests — opening, reviewing, addressing feedback. Writing good PR descriptions
Git hooks — pre-commit linting, automated formatting, running tests before push

Coding with AI

Write a website using AI — no coding experience needed
Build a small program step by step with AI help
How to use Claude Code to build real projects
How to use Kimi Code (Moonshot AI’s coding tool)
How to use Trae (ByteDance’s AI coding tool)
Debugging with AI — let the agent find your bugs

Using Chat Models Effectively

How to use ChatGPT — prompting, custom GPTs, advanced features
How to use Claude — long context, artifacts, projects
How to use Gemini — Google’s multimodal capabilities
When to use which model — picking the right tool
Writing good prompts — the one skill that matters most

Practical AI Workflows

AI for document processing and summarization
AI for research and fact-checking
AI for writing, translating, and editing
AI for data analysis (no code)
Building a personal AI assistant

For Program Managers

How to spec features for AI-powered products
How to evaluate AI tool outputs — what’s good, what’s not
Understanding AI capabilities vs. hype
Budgeting for AI in your projects
Leading teams that use AI tools

Hands-On Demos

I’ll share my screen and build something live
You share your screen and I guide you through it
We debug a real problem together using AI tools
We compare how different AI tools handle the same task

Why 50 CNY / 30 min?

I want this to be accessible. 50 CNY is less than a bubble tea. One session is short enough to fit into a busy day but long enough to learn something concrete. You don’t commit to 10 lessons — just show up for what you need.

This is not a course. It’s coaching. You bring what you’re working on, and I help you use AI to get it done.

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