No math and no jargon. Short lessons that take you from "what is AI" to how agents work, written for people who make decisions, not people who write code.
Tap a lesson to open it. New lessons are added here as I write them.
Normal software follows rules a person wrote. AI learns patterns from examples instead. Show it a million photos of cats and it learns what a cat looks like without anyone defining one.
Think of a new analyst who reads every past deal memo until they can spot a good deal on their own.
A large language model reads a huge amount of text and learns to predict the next word. It writes by making that guess over and over. That is why it can sound confident and still be wrong.
It is fluent autocomplete trained on most of the internet.
The model has no memory of your company, your goals or your last conversation unless you put them in front of it. Better context beats clever wording almost every time.
Brief it like a smart intern on day one.
Give a model tools, like web search, a calendar or the ability to run code, and a goal. Now it can plan steps, use the tools and check its own work. That is an agent.
A chatbot drafts the email. An agent finds the time, writes the invite and sends it.
Every answer runs on specialized chips in data centers that draw enormous amounts of electricity. Compute, capital and energy set the limits on what gets built, which is what I track on AI Radar.
The bottleneck is rarely the idea. It is who controls the scarce input.
Models make things up when they lack context. The bigger failure is human. In my research, companies bought AI at the top and the people doing the work never used it.
Good AI products are designed around how people actually work.