Takeaways on working with AI

01

Claude gets hands

For a long time, I treated Claude like a brilliant advisor sitting behind a glass wall. He could explain everything to me, but couldn't touch anything. Then MCP arrived, and suddenly he could operate my tools himself.

Like an architect who has only drawn plans for years and then picks up a tool for the first time. The wall doesn't get prettier. But it stands faster, because no one is translating anymore.

Before: I'd copy SERP data from a tab, paste it into the chat, ask for analysis, copy the answer back. After: Claude reaches into Ahrefs, SISTRIX and Gmail himself. The aha wasn't "AI can use tools". I knew that. The aha was realizing how much of my time was actually copy-work between tabs, not thinking work.

02

Context is the real product

For a long time, I thought good prompts were the craft. Until I noticed: the best prompt in the world loses against a mediocre prompt with the right context.

Like a cook who has the best recipe in the world but can only cook with the ingredients in the fridge. What you put in matters more than how you prepare it.

Building an agent pipeline taught me this the hard way: I kept trying to prompt Claude cleverer. Then I understood that I have to give him better ingredients. Which data does the scraper return? Which competitors go in? What lives in the RAG index? That's context engineering: not what I ask, but what's in the room when I ask.

03

AI Fluency: the 4Ds beat clever prompts

I used to think being good with AI meant writing good prompts. Then I found Anthropic's AI Fluency framework (Delegation, Description, Discernment, Diligence) and realized prompting is just one skill out of four.

Like renovating a house with a skilled craftsman. I decide what to hand over (Delegation), explain it clearly (Description), judge whether the work is solid (Discernment), and stay accountable for what gets built (Diligence). The craft isn't in the talking. It's in the collaboration.

The 4Ds gave a name to what I'd been learning by hand. Delegation asks: should AI do this at all? Description is where prompting and context live. Discernment is critically checking the output instead of trusting it. And Diligence means: whatever ships under my name is my responsibility, not the model's.