GitHub Stars License: MIT PRs Welcome
6 production-ready AI tutorials — from zero to autonomous agents in one weekend.
I spent months reading AI papers and getting nowhere. Then I just started building. This repo is everything I wish existed when I started.
No PhD required. No math prerequisites. No 10,000ドル bootcamp. Just code that runs.
| # | Tutorial | What You Learn | Code |
|---|---|---|---|
| 1 | AI Chatbot | GPT-4 integration, conversation memory, system prompts | 📁 examples · 📖 tutorial |
| 2 | RAG System | Vector search, knowledge retrieval, embeddings | 📁 examples · 📖 tutorial |
| 3 | Multimodal AI | Text + image processing, vision models | 📖 tutorial |
| 4 | AI Agent | Tool calling, function execution, autonomous decisions | 📖 tutorial |
| 5 | Multi-Agent System | Agent orchestration, parallel workflows, handoffs | 📖 tutorial |
| 6 | MCP Server | Connect Claude to any API, database, or tool | 📖 tutorial |
git clone https://github.com/Alexhr414/ai-dev-no-phd
cd ai-dev-no-phd/examples/part1-chatbot
pip install -r requirements.txt
OPENAI_API_KEY=your_key python chatbot.pyThat's it. You're running a production-quality AI chatbot.
- Backend devs adding AI to existing apps and not sure where to start
- Indie hackers building AI-powered tools that actually ship
- Product engineers prototyping AI features without reading research papers
- Anyone who learns by doing, not by watching 10-hour YouTube series
Most AI tutorials show you a 50-line toy example and call it done.
This course covers:
- ✅ Production patterns — error handling, streaming, rate limits, cost optimization
- ✅ Real use cases — customer support bots, document search, autonomous agents
- ✅ Decision frameworks — when to use RAG vs fine-tuning vs prompting
- ✅ Multi-provider — examples work with OpenAI, Anthropic Claude, and local models
- ✅ From basics to advanced — linear path from Part 1 to Part 6, or jump in anywhere
Build a chatbot with persistent memory, custom personality, and streaming responses. ~50 lines of code. Understand the OpenAI API from first principles.
Add a knowledge base your chatbot can search. Connect it to PDFs, docs, websites. Never hallucinate facts that are in your data again.
Go beyond text. Process images, generate descriptions, build visual Q&A systems. One API call.
Give your AI hands. Let it call APIs, execute code, search the web, take actions. This is where AI gets useful.
Multiple AIs working together. Researcher + Analyst + Writer agents that collaborate automatically. The architecture behind most AI products you've heard of.
Build a Model Context Protocol server. Connect Claude Desktop to your own tools, databases, and APIs. The new standard for AI tool integration.
Python 3.9+
OpenAI API (GPT-4, embeddings, vision)
ChromaDB (vector database)
OpenAI Swarm (multi-agent)
Anthropic MCP SDK
Found a bug? Have a better approach? PRs welcome.
- Fix typos, improve explanations
- Add examples for other LLM providers (Anthropic, Gemini, Ollama)
- Translate tutorials to other languages
- Add production deployment guides (Docker, Railway, Fly.io)
If this helped you ship something, consider:
- Star this repo — helps other developers find it
- GitHub Sponsors — support ongoing tutorials
- Share with a friend who's trying to learn AI without the academic overhead
Open an issue. I reply to all of them.
Built by a developer who got tired of AI tutorials that never shipped.