Teaching Philosophy: From Complex to Clear
My approach to teaching complex AI concepts, refined through years in the classroom and now applied to cutting-edge technology.
Start with the 'why' - understand the problem before diving into solutions
Break complex systems into manageable, logical components
Learn by building - every concept is paired with hands-on implementation
Connect new concepts to existing knowledge you already have
Practice makes permanent - repetition builds understanding
Learning Paths
Structured learning journeys that take you from beginner to expert. Each path is designed with clear progression and hands-on projects.
AI Systems: An Introduction
A beginner-friendly introduction to AI Systems, covering workflow engines, AI agents, LLMs, and MCP servers. Learn how these technologies work together to solve real-world problems.
You'll Learn:
Outcomes:
- •Understand what AI Systems are and their core components
- •Visualize how workflow engines, agents, LLMs, and MCP servers work together
- •Identify real-world applications for small businesses and personal use
- +2 more outcomes
MCP Server Fundamentals
Build your first Model Context Protocol server from scratch. Learn the core concepts, architecture patterns, and best practices.
You'll Learn:
Outcomes:
- •Understand MCP protocol fundamentals
- •Build a basic MCP server from scratch
- •Implement resource and tool providers
- +1 more outcomes
Agentic AI Workflows
Design intelligent systems that can reason, plan, and execute complex tasks autonomously with human oversight.
You'll Learn:
Prerequisites:
Outcomes:
- •Design agent architectures for complex tasks
- •Implement planning and reasoning systems
- •Build human-in-the-loop workflows
- +1 more outcomes
Production AI Systems
Deploy, monitor, and scale AI systems in production. Learn enterprise patterns for reliable AI applications.
You'll Learn:
Prerequisites:
Outcomes:
- •Deploy AI systems to production environments
- •Implement comprehensive monitoring and observability
- •Design systems that scale with demand
- +1 more outcomes
12-Factor Agent Development
Master the patterns and principles for building reliable, production-ready LLM applications based on Dex Horthy's 12-Factor Agent framework.
You'll Learn:
Prerequisites:
Outcomes:
- •Understand agents as reliable software systems, not magical AI
- •Master JSON-based control flow and stateless agent design
- •Build micro-agents that do one thing well
- +2 more outcomes
Mastering Claude Code: From Terminal to Custom Integrations
Learn to leverage Claude Code's unopinionated approach to AI-assisted development. From understanding the evolution of programming tools to building custom integrations with the SDK.
You'll Learn:
Prerequisites:
Outcomes:
- •Understand the evolution of programming tools and where AI fits
- •Master Claude Code's terminal, IDE, and GitHub integrations
- •Implement advanced workflows like TDD and parallel sessions
- +2 more outcomes
Building Intelligent AI Agents with Memory
Master the architecture and implementation of memory systems for AI agents. Learn 10+ memory types, management patterns, and production deployment strategies inspired by neuroscience.
You'll Learn:
Prerequisites:
Outcomes:
- •Understand 10+ types of agent memory and their use cases
- •Build comprehensive memory management systems
- •Implement neuroscience-inspired memory patterns
- +2 more outcomes
Key Concepts
Essential concepts explained clearly. Each topic breaks down complex ideas into understandable components with real-world examples.
Model Context Protocol (MCP)
A standardized way for AI applications to connect with external data sources and tools, enabling more powerful and flexible AI systems.
Related Concepts:
Agentic AI Systems
AI systems that can autonomously plan, reason, and execute complex tasks while maintaining human oversight and control.
Related Concepts:
Tool-Using AI
AI models enhanced with the ability to use external tools, APIs, and services to accomplish tasks beyond pure text generation.
Related Concepts:
Human-in-the-Loop
Design patterns that keep humans involved in AI decision-making processes while leveraging AI for efficiency and scale.
Related Concepts:
AI Planning Systems
Systems that enable AI agents to break down complex goals into actionable steps and execute them systematically.
Related Concepts:
Why Learn Agentic AI & MCP?
The Future is Agentic
AI systems are evolving from simple chatbots to intelligent agents that can plan, reason, and execute complex tasks. Companies are racing to build these systems, and demand for engineers who understand agentic architectures is exploding.
MCP (Model Context Protocol) is becoming the standard for connecting AI systems to external tools and data sources, making it an essential skill for modern AI engineering.
Competitive Advantage
While most developers are still learning basic prompt engineering, you'll master the architectural patterns that power next-generation AI applications. This knowledge translates directly to higher-impact roles and better compensation.
My teaching approach ensures you don't just follow tutorials - you understand the underlying principles so you can architect your own solutions.