This has been one question i have always asked and kept trying to understand until recently. I've seen it asked and discussed multiple times here... So i decided to combine, study and comprehend the various perspective of those who have shared how they are using it @Curtis Hays and many others, to come up with a simple way for me to implement. Here is my personal understanding: Say we are four people in the marketing department, and i am the only one who has built an content pipeline using the ICM framework, now i want onboard the other 3 coworkers and you're one of them... lol 😀 We have one master system, the original pipeline i'm using is now hosted on GitHub (with the same agent.md and context.md, rules and stages). Step 1: Getting the System on Your Computer You copy/clone the entire workspace from the internet (GitHub) to your PC. This gives you the same folders I have: 01-ideas 02-drafts 03-formats etc Step 2: Creating Content Open your copy of the workspace. Talk to ai agent while in the folder normally. Example conversations: “Give me ideas for LinkedIn posts about productivity” “Turn idea number 4 into a full draft” “Format this draft for Instagram carousel and Substack” The agent uses the shared system I built, so all our content has the same style and quality. Step 3: Sharing Improvements If you create a better prompt or improve one of the stages: Tell the agent: “I improved the carousel format, make a Pull Request” I (or the content lead) will check it. Once approved, everyone gets the improvement automatically. Step 4: Where Files Live Your ideas and drafts: Stay on your computer (or save final versions to the department Google Drive, which we all have access to). The framework (how we create content): Lives in the shared system (Github). - What You Need to Do Install Git (one time, easy). Clone (copy) the workspace. Use AI agent within the folder workspace.

What do you wish you understood better? 👇 I want to hear from everyone. Every level. Day one or day one thousand. There's no question too small, too basic, or too "I feel like I should already know this." If you've ever wondered how something works, what something means, or how to do it, that's exactly what I want. Two quick things 👇 1️⃣ Vote in the poll for the big topics you want more of. 2️⃣ Drop your specific question in the comments. The more specific, the better. The comments can serve as building a list from your answers. The creators in this group are incredible, and once we know what you're asking for, we can make the content that answers it. (This is a post aimed at helping all of us make more engaging content) Even if you mostly read and rarely post, this is an opportunity to have someone write about something you may not have an answer for. One question from you might be the exact thing ten other people were too shy to ask. 😊 We learn together, we grow together, we win together! 🤓💪🏆Bas Question: What do you want us to make more content on?

One thing I’m learning in AI is that you can create something genuinely useful even if it isn't polished or fully vetted. Honestly, I'm creating things I don't even fully understand. ha ha We see other folks showing off their latest success and eyes-wide-open stare at the screen thinking "Sheesh. They are so impossibly smart." Maybe I'm the only one doing that. But there are a few things to understand. First, not everything we think is “perfect” is actually perfect. Sometimes it is just the best version we can see from where we are standing. Second, we don't all have the same background, so what I think is a "Win" is a win for me. It might not be a win for you. What I struggle with sometimes is wanting my work to be as perfect as I can make it BEFORE I share it with anyone. That's why I didn't enter any competitions until Comp 7. I really didn't want to be embarrassed. But, I entered anyway thinking I'll just do my best and learn from the process of building. I'm SO GLAD I DID. A few things I learned: -Your first version can be messy. -Your folder structure can change. -Your workflow can have gaps. -Your system can grow. That does not mean it failed. It means it is becoming something. I think sometimes we wait to build until we can make the “perfect” version, but the perfect version usually comes after the rough one teaches us what we actually need. AI gives us so many new ways to create, test, and improve. I don’t want to miss that because I’m overthinking every piece before I begin. So here’s my little reminder, mostly to myself, but perhaps also to you: Make the thing. Let it be imperfect. Pay attention to what it teaches you. Then make it better. I appreciate each one of you and the way you encourage me and one another.