Hands-on AI Technology
Welcome to Hands-On AI Technology, where you master AI fundamentals the way they should be learned: by building real projects that solve actual problems, then understanding the technology that makes them work.
If you’ve ever wondered what’s really happening when you use ChatGPT, what embeddings actually are, or how RAG systems work under the hood… you’re in the right place.
What You’ll Find Here:
Step-by-step project guides that reveal how AI technology works from the inside out
Clear explanations of embeddings, transformers, semantic search, and multi-modal AI through practical implementation
Real challenges and solutions from actual builds (including the 2am debugging sessions)
Learning paths from simple image search to complex RAG systems and MCP implementations
Hi, I’m Jenny 👋
I learned AI technology backwards. I built projects first, then discovered the concepts. No CS degree, just curiosity and a willingness to ship real applications. Each project taught me something fundamental about how AI actually works. Now I share these learning paths so you can understand AI technology without drowning in academic papers. If you’re ready to move beyond just using AI tools to actually understanding them, welcome!
Start With Fundamentals
Module 1: Understanding Semantic Search & Embeddings
Build This: Content-based image search that finds photos by meaning, not filenames
What You’ll Learn: What embeddings actually are, how CLIP bridges vision and language, why cosine similarity works for semantic search
Module 2: Language Models & Text Generation
Build This: Text generation systems that understand context and respond intelligently
What You’ll Learn: How transformers generate text token by token, what temperature and sampling strategies actually control
Build Advanced Systems
RAG Systems: Making AI Remember Your Context
What You’ll Learn: The retrieve → understand → respond pattern, chunking strategies, vector databases vs JSON storage
Multi-Modal AI: Bridging Vision and Language
What You’ll Learn: How vision-language models bridge images and text, BLIP vs CLIP architectures
MCP & Connected Intelligence: AI That Accesses Real Data
What You’ll Learn: How AI models call external tools and APIs, building custom MCPs for your data
Autonomous Research Systems: AI That Investigates Independently
What You’ll Learn: Multi-agent orchestration, research workflow design, tool calling patterns
Content Generation Systems: AI That Creates at Scale
Specialized Applications
Voice & Audio AI
Accessibility & Inclusive Design
The Real Learning Journey
What Actually Happens When You Build AI:
The Unexpected Lessons Behind My First App Launch - Building the MVP is 10% of the work.
Why I Started Building Instead of Just Reading:
Learning by Doing: My Daring GenAI Challenge - Reading about AI filled me with excitement. But I felt aloof and disconnected because I wasn’t actively doing anything. Building projects changed everything.
Updated March 2026