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Welcome to Clief Notes. Here's where to start.
1. Go check out 📚Navigating The Course to see how to get around and what's here. 2. Start with The Foundation. Concepts, folder architecture, prompting framework. Everything else builds on this. 3. Check in at the bottom of each lesson. Polls, discussion posts, other members working through the same stuff. Use them. 4. When you're ready to build real things join in on our Biweekly competitions and win some real cash. ⭐ Competitions Mega Thread 5. If you are wanting to dive into the masterminds, grab all the past templates, artifacts and resources. Upgrade and head into the The Vault for Premium and The Drawing Room (VIP) for VIP 6. Post your work. Ask questions. Help others when you can. What are you here to build?
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What do I read?
Despite all the ai hubbub and second brains someone asked what I read and IF I read at all anymore. The answer is most certainly YES. You can make connections that might be missed by a million questions with AI by diving into books and reading your self. Some books may not even have most of their pages on the web especially older ones and those are often my favorite. Here are 4 of my current favorite readings to prepare me for the future (by looking at the past) 1. Mythical Man Month (1972 by Fred Brooks) 2. Psychology of Computer Programing (1971 by Gerald M Weinberg) 3. Augmenting Human Intellect (1962 Douglas Engelbart) 4. The Creative Act: A Way of Being (2023 by Rick Ruben) Rick Rubens book is just great in general for business and creative acts.
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Community Guidelines
This community is large and it moves fast. That's the good part and it's also the problem: valuable posts get buried, the same questions get re-asked instead of found, and spammers show up wherever there's an audience. These guidelines are what keeps the room worth showing up to. Read them once. You won't need them again, because most of this is what you'd do anyway. New here? Start with Jake's welcome post and the Foundation course. This post is about how we behave, not where to begin. 1. Build in public. Post the thing while it's half working. A half-finished build is more useful to everyone else than the polished writeup you'll never get around to, and you'll get corrected before you've spent a week going the wrong way. 2. Teach what you learn. The day you figure something out is the day you're best at explaining it, because you still remember exactly what confused you. A month later you've forgotten the hard part and your explanation gets worse. If you cracked something this week, that's a post. 3. Ask good questions. Specific beats polite. "How should I structure this?" gets three vague answers. "I have a 40 file client folder, the model keeps loading the wrong context file, here's my CLAUDE.md" gets a real one. Say what you tried, what happened, and what you expected instead. A more in depth guide: https://dontasktoask.com 4. Give credit. If you built on someone's skill, template, folder structure or comment, tag them. It costs you nothing and it's the reason people keep publishing their work here instead of keeping it. A lot of the best material in this community started as somebody's reply on somebody else's post. 5. No spam, no pitching. Sharing a tool you actually use and explaining why you use it is fine. Dropping a link with no context, cold DMing members, or treating the feed as a lead list is not. Networking is genuinely welcome (there's a Connection Hub for exactly that: https://www.skool.com/cliefnotes/new-the-connection-hub-is-live) but networking and pitching are not the same thing, and everyone can tell the difference immediately.
The Heavy Lift
Here's a link to my most recent work, an article produced by a few of my agents of 80! https://www.linkedin.com/posts/etan-ayen_the-heavy-lift-an-agentic-expose-on-aerospace-ugcPost-7487476844481609728-nxPS/?utm_source=share&utm_medium=member_desktop&rcm=ACoAADb0vVkBFFCNnwLLOD3YE6bstFtUTw-ccX8
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The Heavy Lift
🔁 Loop Engineering: The Next Evolution of AI Workflows
Most people still use AI like a chatbot. Write a prompt. Get an answer. Rewrite the prompt. Repeat. Loop engineering changes that. Instead of manually reviewing every response, you create a workflow where AI evaluates its own output, follows predefined rules and continuously improves until it meets your standards. This approach isn't about replacing human judgment—it's about reducing repetitive work and building more reliable AI systems. In this guide, you'll learn how to: - Build your first review loop with Codex - Structure projects using AGENTS.md - Create repeatable AI review workflows - Automate quality checks for code and content - Develop systems that improve through iteration The most effective AI systems are not built from a single perfect prompt. They are built from simple, reliable loops that consistently solve one problem at a time. How do you see loop engineering changing the way we build with AI over the next few years? I'd love to hear your thoughts.
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🔁 Loop Engineering: The Next Evolution of AI Workflows
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