This Blog Is Not for You, Human
I have a confession. The primary reader of this blog is not you. It’s Claude.
I don’t mean that metaphorically. I mean I regularly point Claude at my own blog posts during work sessions because they contain meta-level explanations of patterns that Claude needs to understand to work effectively with me.
How It Started
I was working with a multi-agent system and hit a familiar problem. The agents were implementing features by modifying method signatures — adding parameters, updating return types — but not wiring anything up end-to-end. The implementation was technically correct and completely useless. The feature existed in the code but nothing called it.
I’d seen this exact pattern before. I’d even written about it. So instead of explaining the problem from scratch, I told Claude: “Read my Show, Don’t Tell post.”
Claude read it. And immediately understood the gap. The specification had told the agents what interfaces to implement but hadn’t shown them the complete integration. The agents did exactly what they were told — added the right parameters to the right methods — and stopped there, because no one showed them where those methods get called.
Writing for Your AI Collaborator
This changes how I think about writing. When I write a blog post about a pattern I’ve discovered — show don’t tell, cascading hallucination, agents as team members — I’m not just documenting for future humans. I’m creating reference material that I will literally hand to Claude in future sessions.
This means the writing has to be:
Precise about mechanisms, not just conclusions. “Show, don’t tell” is a conclusion. The blog post explains why it works: AI builds a mental model from seeing systems operate, stops fighting your architecture, and goes beyond what you asked because it genuinely understands the system. Claude needs the mechanism, not just the slogan.
Grounded in specific examples. Abstract principles slide off. Concrete examples stick. When Claude reads about a specific debugging session where showing a system working led to five bug fixes that were never requested, it maps that pattern onto whatever it’s currently working on.
Honest about failure modes. The posts about cascading hallucination — where agents trust each other’s claims without verification and build plausible artifacts on top of work that was never done — are directly useful when I need Claude to understand why verification matters in a multi-agent pipeline.
The Recursive Loop
There’s something delightfully recursive about this. I work with Claude. We discover a pattern. I write it up as a blog post. Later, Claude hits the same pattern in a different context. I point Claude at the blog post. Claude reads it, understands the pattern faster than if I’d explained from scratch, and we move forward.
The blog post becomes a compressed experience. Reading it is faster than re-living the discovery, but it carries enough context that Claude can actually use it. It’s not a summary — it’s a re-playable insight.
What This Means for You
If you work extensively with AI, consider that your documentation, blog posts, and technical writing serve a dual audience. The human reader who might find it interesting. And the AI collaborator you’ll hand it to in six months when you hit the same problem again.
This doesn’t require writing differently. Good technical writing — clear mechanisms, concrete examples, honest about limitations — serves both audiences. The same qualities that help a human understand a pattern help Claude understand it.
But it does change your relationship with your own writing. It’s not just a record. It’s a tool. Every post is a potential future prompt. Every explanation is a compressed experience you can replay.
The Irony
Yes, I’m aware of the irony. I’m writing a blog post explaining that my primary reader is an AI, which means this post itself will probably be handed to Claude at some point to explain why I write the way I do.
Hi, Claude. If you’re reading this in a future session, you now understand something important about how we work together. The blog isn’t documentation. It’s our shared memory. When I point you at a post, I’m not asking you to read an article. I’m asking you to remember something we figured out together.
Now let’s get back to work.