Has anyone here been using Buzz yet? The idea of having humans and AI agents working together in shared channels (instead of jumping between Slack, GitHub, and separate chat windows) seems really interesting. I’m curious about real-world experience though: - Is it actually useful? - What workflows are you using? - Any surprises, limitations, or things you wish you knew before trying it? Would love to hear from anyone who’s spent some time with it.

Most free AI courses are a teaser. A few videos. Then a paywall. Anthropic’s Academy is not that. Thirteen courses. Listed at $0. Built by the team that ships Claude. No subscription. No upsell after the last lesson. That is worth an evening. Not because a certificate will hire you. Because the work teaches how the model fails, not just how to chat with it. If you just landed in Clief Notes and you still treat Claude like a smarter search box, start here. You do not need to be a developer. You do need one loop you can finish. Three tracks - AI Fluency. How to think with the model. Where it lies. Where it helps. Everyone starts here. - Developer. API, Claude Code, MCP. User to builder. - Specialized. Students, educators, nonprofits, AWS, Google Cloud. Only after 101. The instinct to kill: skipping Fluency and jumping to agents. That instinct is wrong. You will ship slop faster. Where to start tonight New to AI: Claude 101, then AI Fluency: Framework and Foundations. Want to build: Building with the Claude API, then Introduction to MCP. Education or nonprofit: Claude 101, then your specialized course. Most courses are 1 to 4 hours. Two in a weekend is enough. How you get in 1. Open Anthropic Academy on Skilljar. 2. Make a free account. 3. Pick a course marked $0. 4. Finish the lessons. 5. Download the certificate if you want the paper. https://anthropic.skilljar.com/ The certificate is a bonus. The useful part is a working sense of when Claude is wrong. That is the skill that compounds. Do Claude 101 this weekend. Bring back one thing that broke your current prompt habit. Drop it under this post. I want the boring version, not the certificate screenshot.

I don't want a progress percentage today. I want to know what's actually changing. If you've been working through Building Your Stack, what is one decision you've made because of it? - Maybe you added something. - Maybe you removed something. - Maybe you realized part of your current stack makes absolutely no sense. 😂 Whatever it is, post it below. What did Building Your Stack make you rethink? And if you've been flying through the lessons but skipping the quizzes or accountability posts... Go back. Those are there to make sure you can actually explain and apply what you're learning rather than just consuming it. Drop your biggest realization below. 👇 And keep going. New roadmap next Friday, September 25th.

We all know AI as a great assistant, one that can help you brainstorm and generate creative results from a wide array of data it's been trained on. But here's what we often miss: it's not always going to give you the best results. And that makes sense, actually, because AI doesn't have judgment. It can only provide possible solutions based on your input (context, data, prompt). The amazing results we get are usually slightly above average, but they appear great, especially if you know very little in the area you're working on. This in itself is not a bad thing. I'm not trying to point out a flaw in AI. I'm trying to highlight an important insight. YOUR EXPERTISE ALWAYS MAKES THE DIFFERENCE If you've ever worked with AI on a subject you're an expert in, you probably know what I'm talking about. When your expertise is actively influencing your work with AI, the results are directly proportional to your level of expertise. This is where you witness the full power of AI. When you help it navigate the right trained data based on your judgment and expertise, you create unique results others can't replicate. This is why I always say: focus primarily on your industry. There's a lot your years of experience can unlock, and it's a win-win, because those with no plans to switch careers get to deepen their work in the field they know best. I believe everyone should work with AI across three layers: 1. Primary AI work, where you use your expertise and experience to create unique work and improve your industry. 2. Secondary AI work, the auxiliary functions needed for your primary AI work, business, projects, and other important tasks. 3. Tertiary AI work, which powers your hobbies, entertainment, and other luxuries. This is how industries maintain their relevance, prosperity becomes attainable, and the quality of the human experience is preserved in these times. What kind of work are you using AI for?

I have been mapping what the business does instead of who does it, so the folder describes functions and decision limits rather than names. On paper it is clean. Every decision has an owner, every owner has a limit, and nothing depends on one particular person being around. Then it meets the actual team. One person holds four of those roles. So when the folder says role A escalates to role B, what happens is the same human approving their own call, and the escalation was never a check. It was a formality with two names on it. I could write the folder against reality instead, but then it breaks the week someone is hired or leaves, and I am back to rebuilding the system around people. For anyone who has run into this: do you keep the role chart ideal and accept that today it collapses onto three people, or do you record who currently wears which hat somewhere the agent can read? And if you record it, what keeps that file from quietly turning into the org chart nobody updates?