I'll be honest about something. I use AI every single day. I've built systems around it. I teach a course on it. And about twice a week, I still get output that makes me want to close my laptop and go outside. Yesterday I spent 20 minutes trying to get Claude to write a simple client email. Twenty minutes. For an email. I could have written it myself in three. The problem wasn't the tool. The problem was that I was being lazy about context. I was rushing. I gave it a vague ask and expected a specific result. And every time it gave me something generic, I got more frustrated instead of stopping to think about what I was actually asking for. Here's what I've learned: AI frustration is almost always a mirror. When I'm frustrated with the output, it's usually because I haven't done the thinking work. I haven't been clear about what I want, who it's for, or what "good" looks like. That doesn't make the frustration less real. It just makes it useful information. What's your most recent AI frustration? And in hindsight, was the problem the tool -- or was it something about how you were using it?

I want to share a resource that shifted my perspective on working with AI. Anthropic (the company behind Claude) publishes their prompt engineering documentation publicly. It's not marketing material. It's their actual technical guidance on how to get better results from language models. Link: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview Here's why I think it's worth your time even if you primarily use ChatGPT or another tool: 1. It explains WHY certain approaches work, not just what to do. Understanding the "why" means you can adapt the principles to any platform. 2. It's honest about limitations. They don't pretend their AI can do everything. That kind of transparency helps you calibrate your expectations. 3. The examples are practical. Not academic. Real tasks, real prompts, real output comparisons. The single most useful thing I took from it: Being specific about what you want is more important than being clever about how you ask. Most prompt advice focuses on phrasing tricks. The actual documentation focuses on clarity and context. That maps directly to what we talk about here: the thinking is the work. Clear thinking leads to clear prompts. Clear prompts lead to useful output. Try it on your next prompt and see what happens. Have you found any AI resources that changed your approach? Share them below -- the more practical, the better!

I want to try something with this community. I'm going to describe a real work scenario, and I want you to tell me how you'd categorize it. The scenario: Your boss asks you to create a presentation for the quarterly board meeting. The presentation needs to include: - Revenue numbers from last quarter (pulled from your internal finance system) - - A competitive analysis of 3 key competitors - - Strategic recommendations for next quarter - - An appendix with employee satisfaction survey results One task. Four very different components. Here's my take -- but I want to hear yours first: Some parts of this are clearly Green (let AI handle it). Some are probably Yellow (AI assists, you verify). And at least one might be Red (keep AI away entirely). How would you break this down? Which parts would you hand to AI, which would you verify carefully, and which would you keep AI away from entirely? And why? Drop your thinking below. There's no single right answer -- that's what makes this interesting. The way YOU think about it depends on your industry, your company, and your risk tolerance. I'll share my breakdown in the comments tomorrow.

New YouTube Video is up! 80% of professionals quit AI tools within 3 weeks — not because the tools are bad, but because using them well forces an identity crisis most people aren't ready for. In this video, I break down the three psychological barriers keeping smart professionals from thriving with AI: the identity crisis nobody sees coming, the delegation gap that makes AI outputs feel generic, and efficient mediocrity — when AI makes bad thinking look polished.

Quick gut check. Just posted on LinkedIn about the 4 tiers of AI stack. Curious what the breakdown looks like in here. 1. Web-only (browser tabs)? 2. Mixed (web plus some custom GPTs or API)? 3. Custom apps (Projects, NotebookLM, n8n)? 4. CLI / harness (Claude Code or similar)? No judgment on the answer. The thing I'm actually trying to learn: if you're not at the tier you want to be at, what's the actual blocker? Confidence, time, money, "not sure where to start," or something else? Be honest. I want to know what to help with first.