Power User Territory
Welcome to the advanced path. If you're here, you're ready to move beyond using AI tools to building with them. This is where theory meets practice, and where you gain programmatic control over AI systems.
At this level, we're talking about extending AI capabilities through custom integrations, running models on your own hardware, and building autonomous agents that can accomplish complex tasks. These aren't just consumer use cases. This is infrastructure.
Technical requirements: You'll need programming experience, comfort with the command line, and familiarity with concepts like APIs, environment variables, and configuration files. If terms like "localhost," "port," and "API endpoint" are new to you, spend more time at the intermediate level first.
MCP (Model Context Protocol) is one of the most powerful recent developments for extending AI. It lets you connect AI assistants to external data sources, tools, and services. Think of it as giving AI access to your file system, databases, APIs, or any custom functionality you build.
Self-hosted models give you complete control. No rate limits, no usage tracking, and your data never leaves your machine. The tradeoff is hardware requirements and slightly lower quality compared to frontier models. But for many use cases, especially privacy-sensitive work, it's worth it.
Agentic development is where AI stops being a chatbot and starts being a system. Agents can plan, use tools, handle errors, and iterate toward goals. This is cutting-edge territory. The patterns are still emerging, and you'll be learning alongside the industry.
Prerequisites for this path:
- ✓Solid understanding of prompt engineering
- ✓Programming experience (Python or JavaScript preferred)
- ✓Comfortable with command line and package managers
- ✓Familiar with APIs and authentication
- ✓Ready to read documentation and troubleshoot
These exercises assume you can debug issues, read error messages, and search documentation. The advanced path isn't hand-holding. It's showing you what's possible and giving you the foundation to explore further.
Lessons
Deep dives into advanced AI development and deployment
MCP Servers and Protocols
Dive into Model Context Protocol (MCP) servers. Learn how to extend AI capabilities with custom tools, data sources, and integrations.
Topics covered:
- Understanding MCP architecture
- Installing and configuring MCP servers
- Creating custom MCP tools
- Integration patterns and best practices
Self-Hosted LLM Models
Run AI models locally on your own hardware. Explore open-source models, quantization techniques, and deployment strategies.
Topics covered:
- Open-source model ecosystem (Llama, Mistral, etc.)
- Hardware requirements and optimization
- Quantization and model compression
- Local deployment with Ollama and LM Studio
Agentic Development Patterns
Build autonomous AI agents that can plan, execute, and iterate. Learn agent architectures, tool use, and orchestration patterns.
Topics covered:
- Agent architectures (ReAct, Plan-and-Execute)
- Tool use and function calling
- Multi-agent orchestration
- Error handling and guardrails
Exercises
Hands-on implementation of advanced AI concepts
Exercise 1: Set Up Your First MCP Server
Install and configure an MCP server to extend AI capabilities. Connect it to Claude Desktop and test custom tool integration.
Prerequisites:
- Basic command line skills
- Node.js familiarity
- Claude Desktop installed
Tasks:
- Install Node.js and required dependencies
- Set up a basic MCP server (filesystem or weather)
- Configure Claude Desktop to use the server
- Test custom tools and verify functionality
Exercise 2: Run a Local LLM
Deploy a local language model using Ollama. Compare performance, privacy, and capabilities with cloud-based solutions.
Prerequisites:
- Sufficient RAM (16GB+ recommended)
- GPU optional but helpful
- Terminal comfort
Tasks:
- Install Ollama or LM Studio
- Download and run a quantized model (Llama 3, Mistral)
- Test various model sizes and configurations
- Compare response quality and speed with cloud LLMs
Exercise 3: Build a Simple AI Agent
Create an autonomous agent that can use tools, make decisions, and accomplish multi-step tasks without constant human guidance.
Prerequisites:
- Programming experience (Python/JavaScript)
- API access (OpenAI/Anthropic)
- Prompt engineering knowledge
Tasks:
- Define an agent task (research assistant, code reviewer)
- Implement tool calling with function definitions
- Add planning and decision-making logic
- Test with complex, multi-step scenarios