Notes from the work

You tried it and it didn't click. Here's why, what it's actually good at, the one task to test it on, and where it's not worth your time.

Where it actually fits for agents, the one task to start with, where to never trust it, and what real agents say changed.

When you wear every hat, this is the admin to hand off first. Where it fits, where to start, and where to be careful.

Use AI on your own work without crossing your company's line. Where it fits, and the data rule that matters most.

Get ahead of the shift instead of caught by it, without becoming technical or chasing every new tool. The moves that actually matter.

A plain-English answer from someone who builds with it, not a data scientist. What it actually is, what it is not, and why it feels like magic.

LLM, prompt, token, hallucination, agent, RAG. The terms everyone uses and nobody explains, broken down in plain English. Slightly oversimplified, on purpose.

Everyone sells what AI can do. The more useful question is what it can't. Judgment, taste, trust, care. That line is where your value lives.

The red car theory of finding where AI fits. The opportunities were always there. One real win is what lets you finally see them.

Electricity, the spreadsheet, the internet. Jobs changed, the net gain was huge, and here's the honest part nobody mentions.

How I went from using tools to building them. A Nissan engine, an underwriting bot, and a dice game now in classrooms.

A real system designed around how agents actually work, demoed live at a conference. What it does, how it shows up, and what it changed.