Tutorial Part 5: When Configuration Becomes Architecture Part 5 — the final part — of the Command-Line LLM Tools tutorial series. Previously: Part 4 — When the Agent Doesn't Know Enough — extending capability with skills and sub-agents. Every fix in this tutorial series scales until it doesn't. The instructions
Tutorial Part 4: When the Agent Doesn't Know Enough Part 4 of the Command-Line LLM Tools tutorial series. Previously: Part 3 — When the Agent Goes Off the Rails — git as time machine, approval modes, hooks for hard guarantees. Parts 1, 2, and 3 made the agent safe and
Tutorial Part 3: When the Agent Goes Off the Rails Part 3 of the Command-Line LLM Tools tutorial series. Previously: Part 2 — When the Agent Forgets — managing context, compaction patterns, files-as-memory. Part 2 solved memory. The agent stays sharp now — context doesn't rot, instructions don't get
Tutorial Part 2: When the Agent Forgets Part 2 of the Command-Line LLM Tools tutorial series. Previously: Part 1 — Getting Started with Claude Code & Codex CLI — install, instructions file, the five rules that prevent day-one mistakes. In Part 1, we set up a working CLI agent with an instructions
How I Choose Between Claude, ChatGPT, and Gemini There's no "best" model. There's "right model for this kind of work, given the current state of the field." This is a practical guide to how I actually route tasks across the three frontier
Why Use More Than One LLM I started with one model. It was good. I assumed the right answer was "find the best model and stick with it" — like picking a code editor or a phone OS. Switching is overhead. Lock-in is fine if the lock is worth
Part 1 of the Command-Line LLM Tools tutorial series. Last reviewed: April 2026. You're here because you're ready to try Claude Code or Codex CLI but don't know how to start. If you don't know what these are or why you should
Why Use Command Line LLM Tools The browser AI assistant is fine for asking questions. It is not fine for getting work done. The three problems that make browser AI fragile — confidently wrong answers, no persistent memory, and unconstrained execution — get worse the moment you start using it for serious
Hello World For the past few months, I've been using LLM agents to help run real work — legal research, board governance, household coordination, college planning, writing, and the everyday mess that doesn't fit neatly into a productivity app. I'd built what I thought was