The fundamentals of prompt engineering — clarity, context, and structure — and how it sits inside the broader discipline of context engineering.

How Model Context Protocol (MCP) connectors work — the architecture, integration patterns, and practices for linking AI agents to real-world data and tools.

Five practical patterns for connecting a local LLM to external tools via MCP — natural-language SQL, autonomous research, note management, offline smart-home control, and sandboxed file operations.

A practical playbook for deploying autonomous AI agents safely — agent-specific risk drivers, pre-deployment governance, IAM and guardrails, inter-agent security, traceability, contingency planning, and a deployment gate.

Claude comes in three tiers: Opus for power, Sonnet for balance, Haiku for speed. Here is how they differ and a simple rule for choosing between them.