Most people are still trying to get better AI output from better chats.
I get why. That is where most of us started.
You open ChatGPT, Claude, Gemini, or whatever tool you like. You ask a better question, add more background, paste in a few examples, and try to explain what good output looks like.
Sometimes it works really well.
But then the next chat starts from zero again.
You re-explain the project, re-upload the files, paste the same rules, correct the same mistakes, move the output into Google Docs, Notion, email, Substack, Slack, or wherever the real work happens.
At some point, the problem is no longer the prompt.
The problem is that the AI has no real connection with your work.
That was the main idea I tried to show in a past session with the Cozora Community. I walked through how I run AI Maker from inside Claude Code, including my newsletter folder, my project structure, my CLAUDE.md, my skills, my slash commands, my memory rules, and the way I use agents to help with research, writing, planning, and operations.
This was a live tour of the system I actually use, and now I’m sharing it with you.
When people hear “Claude Code,” they often assume this is for programmers. Margaret, one of the people in the session, asked the obvious question right away:
“So is Claude Code like about coding?”
That is the question most normal people have.
And the answer is: it started there, but that is not where it ends.
Claude Code is useful because it lets an AI agent work inside a real project folder. It can read files, follow rules, create outputs, run repeatable workflows, store a large amount of information, use tools, remember corrections through files and instructions, and help you move from “answer this question” to “help me run this part of my work.”
That is a different relationship with AI.
The Shift I Was Trying To Show
For the last few years, most AI work has looked like chat.
You ask a question, get an answer, copy the answer, paste it somewhere else, open another app, bring back more context, ask again. Then you rinse and repeat.
That is still useful. I still use normal chat sometimes, especially on mobile.
But for my day-to-day work, the center of gravity has moved.
My newsletter now lives in a folder Claude Code can read, including the drafts, archive, writing rules, audience notes, paid-versus-free rules, and performance data.
So when I ask for new post ideas, Claude does not have to guess from the prompt alone. It can inspect the actual project, read the files that matter, see what I have already published, and follow the rules I wrote for the newsletter.
That’s the transformation you can expect when you go full AI‑agent mode.
💡 Quick note: This is also the shift Michael Simmons and I are teaching in Agentic Academy for Knowledge Work.
AI chat has a ceiling. You re-explain the project, re-upload the files, repeat the same corrections. Agentic Academy is 10 live weeks where you build one AI system that runs your real work. No coding. Starts June 22. Enroll now.
Watch’s Inside of The Video
This replay is a walkthrough of the system behind my newsletter and project management setup.
I would not watch it like a normal webinar where you try to remember every tool name. I would watch it with one question in mind:
What would my first agent folder need to understand about my work?
That is the useful lens.
Because the specific tools will change. But the underlying pattern is going to matter for a while:
Give the agent a folder.
Put the right source material inside.
Write the rules it should follow.
Create repeatable workflows.
Add guardrails.
Improve the setup every time the agent gets something wrong.
That is the pattern I kept coming back to during the session.
What We Covered
The session started with a question from Julia about managing multiple systems together.
She was already deep in the work, cleaning up a bunch of Claude projects, trying to build a better master project, and thinking through how memory and project status could work across different kinds of work.
That was useful because it surfaced one of the real problems with AI agents:
They can do a lot, but they can also do too much.
Julia mentioned that Claude Code can sometimes “go nuts” and do far more than she asked for. I have felt that too. Sometimes the agent understands the intent and runs with it in a useful way. Sometimes it does a long chain of work and gives you something that is technically impressive but not what you wanted.
That is why the folder matters.
You need a place where the agent can learn:
What this project is
What counts as good work
Which files matter for which tasks
What actions are allowed
What mistakes it should not repeat
When it should ask before moving
That is the job of the system around the agent.
In my setup, the most important file is CLAUDE.md.
I described it in the session as the brain of the project. It is the file Claude reads first. It tells Claude what the project is, what folders exist, what rules matter, what reference files to use, and what standards to follow.
For AI Maker, that means Claude knows things like:
Newsletter drafts live in one place.
LinkedIn posts have a different style than newsletter posts.
Paid posts need implementation detail.
Free posts should be complete on their own.
My writing should avoid polished corporate language.
If a task involves audience data, it should read the audience files first.
The more I use it, the more I think most people should start with the file that teaches the agent how to understand the project. The complicated automation can come later.
The Six Pieces I Walked Through
During the replay, I broke Claude Code into six pieces.
You do not need all of them on day one. I definitely did not build all of this at once.
But once you see the pieces, the whole thing feels less mysterious.
1. CLAUDE.md
This is the main instruction file for the project.
It tells the agent:
