AI #

Most writing about AI is either breathless hype or dense research jargon. This section is neither. It is plain-language explanations of how the tools everyone is suddenly using actually work under the hood, written for someone who is technical enough to be curious but does not want to wade through a paper.

Nothing here is tied to one company or product. These explain the ideas (how the models work, how agents work, how to use them well) so they stay useful no matter which tool you pick up.

Foundations: how the models work #

• How Large Language Models Actually Work: the prediction engine behind all of it.
• What Is a Token, and Why It Shows Up on Your Bill: the unit AI runs on.
• Why LLMs Hallucinate, and How to Reduce It: why AI confidently makes things up.
• Context Windows, and Why AI Agents Forget: the invisible memory limit.

Agents: how AI takes actions #

• What Is an AI Agent?: the difference between a chatbot and an agent.
• The Agent Loop: How AI Agents Actually Work: the single mechanism behind every agent.
• How Coding Agents Work Under the Hood: tools, navigation, and self-verification.
• Tool Use and Function Calling, Explained: how a text-only model runs real code.
• What Is MCP (Model Context Protocol)?: the standard that plugs tools into any AI app.

Using AI well #

• Prompt Engineering: How to Actually Get Good Answers: the practical basics.
• What Is RAG (Retrieval-Augmented Generation)?: making AI answer from your own documents.
• Reasoning Models: What “Thinking” and “Effort” Actually Mean: models that work it out first.
• Running AI Models Locally: free, private, offline AI on your own machine.

These pair naturally with the apps I build, which lean heavily on running AI locally and cheaply, and with the cheap-AI frugality guide.