How I Built an Iterative, Perpetual AI Work System with Claude Code in 3 Minutes

Author: Roland's Thinking Diary (@Roland_WayneOZ)
Published: February 4, 2026 15:14
Source: https://x.com/Roland_WayneOZ/status/2018946199106326726

This article was generated by my own content generation system. I couldn't have written something this good myself — thanks CC. You don't have to read the whole thing. Just like it, bookmark it, copy the full text, and let Claude Code handle the rest. I've already set that up.

Part 1: How You Use AI Reveals Your Cognitive Level

I've seen too many people use AI like this:

They subscribe to 10 AI tools, spending $70 a month. But when it's time to actually use them, they still open ChatGPT and type "write me an email."

Every time they write an article, they re-explain everything from scratch: "I work in XX, my audience is XX, my style is XX…" The AI never remembers the last conversation.

Their desktop is littered with "New Folder," "New Folder (2)," "Temp," "Actually Temp." Finding a document from last week takes 20 minutes of digging.

Last month they wrote a viral post. This month they want to replicate it — but the framework, the key phrases, the data they used? All gone.

This isn't using AI. This is being used by AI.

Honestly, most people use AI the same way they use Word.

Open → Input → Output → Close.

No memory. No accumulation. No system.

Starting from zero every single time.

How you use AI determines whether you're the master of AI or just a user.

Most people are just users.

Part 2: From "User" to "Master" — One Core Shift

What's the difference between a user and a master?

User: AI is a tool. Use it and close it.

Master: AI is part of a system. It knows who you are, what you're doing, and what you've done.

Users spend every session teaching AI who they are.

Masters let AI remember them, then build on that foundation.

Users have linear efficiency: one task, one unit of time.

Masters have compounding efficiency: one task simultaneously adds a brick to the system.

The core shift from user to master is just one thing:

Turn fragmented AI usage into a systematic AI workflow.

How?

I built a system with Claude Code.

Now all my work happens in one directory. AI knows my context. Content is reusable. Methodology accumulates.

Here's the design philosophy behind that system.

Part 3: What My System Looks Like

Before getting into the structure, here are three design principles.

Principle 1: Organize by Business Process, Not File Type

Wrong way:

Documents/
Images/
Videos/
PDFs/

Right way:

01-Content-Production/
02-Work/
03-Life/
04-Skills/
05-Ideas/
...

Why?

Because when you work, you think "what do I need to do" — not "what format is this file."

When you're writing an article, you need: topic ideas, source material, methodology, historical data.

Those things should live together, not scattered across Documents, Images, and PDFs.

Principle 2: Shared Layer + Independent Modules

Your work probably spans multiple directions.

They're independent, but some things are shared:

Personal positioning (who you are)
AI operating instructions
Long-term memory
Skills library

So the architecture should look like this:

WorkSystem/
├── Shared Layer
│   ├── CLAUDE.md (AI master guide)
│   ├── personal-positioning.md
│   ├── memory-bank/
│   └── skills-library/
│
├── Independent Modules
│   ├── 01-Business-Line-A/
│   ├── 02-Business-Line-B/
│   └── 03-Business-Line-C/

The shared layer is "infrastructure." Independent modules are "business lines."

Benefits of this design:

AI only needs one working directory — no context switching
Knowledge across domains can cross-pollinate
Each business line still has clear boundaries

Principle 3: Content Lifecycle Management

I adapted this part from dont's public content — thanks dont.

If you do content creation, an article goes through these stages from idea to publish:

Idea → Develop → Ready to Publish → Published

So topic management can be designed like this:

Topic-Management/
├── 00-topic-log.md       # Inbox for scattered ideas
├── 01-to-develop/        # Has potential, needs writing
├── 02-ready-to-publish/  # Draft complete, awaiting publish
└── 03-published/         # Published, with performance data

Each stage has a clear entry and exit.

Ideas don't get lost. Progress is trackable. Data accumulates.

Full Structure Reference

WorkSystem/
│
├── CLAUDE.md              # AI master guide
├── personal-positioning.md # Who you are, what you do
├── control-panel.md       # System entry point
│
├── memory-bank/           # Long-term memory
│
├── 01-Content-Production/
│   ├── Topic-Management/
│   │   ├── 00-topic-log.md
│   │   ├── 01-to-develop/
│   │   ├── 02-ready-to-publish/
│   │   └── 03-published/
│   ├── Source-Library/
│   │   ├── core-concepts/
│   │   ├── key-phrases/
│   │   └── case-studies/
│   ├── analytics/
│   └── methodology/
│
├── 02-Business/
│   ├── business-model/
│   ├── client-delivery/
│   └── courses/
│
├── 03-[Your Business Line]/
│
└── 04-Skills-Library/
    ├── content-production/
    ├── business/
    └── general/

Part 4: How to Keep the System Alive

A system isn't designed into existence — it's used into existence.

The problem most people have when building systems: they spend three days designing the perfect structure, then never open it again.

For a system to stay alive, it needs to keep iterating.

I use three questions to decide when to adjust the structure:

Question 1: When I'm looking for something, where's my first instinct?

If your first instinct doesn't match where things actually are, your categorization logic is broken.

For example, you put "investment notes" under "Learning." But every time you look for them, you go to the root directory first.

That means in your mental model, "investing" is its own thing — not a subset of "learning."

So make it independent.

Good categorization should match your intuition, not just be logically "correct."

Question 2: How long has it been since I opened this folder?

If a folder hasn't been opened in over a month, either:

The category doesn't fit your workflow
You've stopped doing that thing

Either way, adjust.

The system serves you. You don't serve the system.

Question 3: Do I hesitate when filing new content?

If you pick up a new file and don't know where to put it, your category boundaries aren't clear.

Good categorization means you know where things go without thinking.

If you're constantly hesitating, redefine the boundaries.

Part 5: How to Start

If you want to build a system like this, here's my advice:

1. First, clarify your "business lines"

Ask yourself: what are the main categories of things I do day-to-day?

Keep it under 6. Too many and you can't manage them.

2. Start with one small workflow

Don't chase perfection from the start.

Solve one specific pain point first:

Topics keep getting lost → build "Topic Management" first
Can't find source material → build "Source Library" first
AI doesn't remember context → write "CLAUDE.md" first

3. Let Claude Code help you build it

You can send this article to Claude Code and say:

I want to build a work system like this.

My situation:
- Things I do: [list your business lines]
- My current pain points: [list specific problems]

Please help me:
1. Design a directory structure that fits me
2. Create the core files
3. Define the workflows

Claude Code will customize a system based on your situation.

4. Iterate as you use it

After a week, ask yourself those three questions:

Does my first instinct match where things are?
Are there folders I haven't opened in a while?
Do I hesitate when filing new content?

Adjust based on your answers.

Remember: A system isn't designed into existence — it's used into existence.

Closing

The core value of this system:

Memory system: AI knows what you've done before
Content reuse: Good frameworks, phrases, and cases can be used again and again
Methodology accumulation: Every work session adds a brick to the system
Iterability: The structure evolves with your needs

Fragmented work: Starting from zero every time. Low efficiency, inconsistent quality.

Systematic work: Reusing and iterating every time. High efficiency, consistent quality.

Most people use AI to consume time.

A few use AI to accumulate assets.

The difference is whether you have a system.

If this article helped you, feel free to share it with friends who also use AI.

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