Tutorials, real-world applications, and plain-English explanations — for people who use AI at work but don't want to read a research paper to understand it.
The report AI helped analyze, the email it drafted, the message it reworded. When does honesty require you to say so, and when is it just using a tool? A practical test.
What can AI agents do? In plain English: carry out multi-step tasks across your apps instead of just answering questions. Here's what that covers today, what it still can't do, and how to start.
Employers say they'll reskill workers for AI, 77% of them, but only about 13% of employees have actually received any training. A practical, do-it-yourself path to real AI literacy while you wait.
Most AI users fear falling behind if they don't keep up, and new grads worry AI will swallow entry-level jobs, yet most active users say they're producing work they couldn't have a year ago. Making sense of the contradiction.
Only about one in five corporate AI investments delivers a measurable return, and just one in fifty is transformational, yet AI tops the CEO priority list. Here's what separates the rollouts that work.
Most workplace AI policies are either nonexistent or a twelve-page legal document nobody reads. Here's the short version that covers what actually matters.
A hands-on companion to the commands-vs-skills-vs-plugins guide: build one genuinely useful thing in Claude Code, starting at the simplest rung and climbing exactly one step when you feel the ceiling.
Three words that get thrown around whenever people customize Claude Code, and a plain-English guide to what each one is, its trade-offs, and when to reach for it.
A simple filter for choosing where to start with AI, one that steers you toward quick wins and away from the tasks that create real risk.
How to get AI action items from a meeting: a copy-the-prompt tutorial that turns a raw transcript into decisions, owners and due dates you can send out before the room has cooled.