This was the first community I joined, and my first real go at AI beyond just talking to a chat window. I found it pretty much by chance, lurked for a few months, and High Tea 6 was my first call. I didn't realise at the time how much that would shape things. The picture I got here was a healthy one. Build the thing, watch how it actually behaves, be honest about the bits that don't work. I've spoken to enough people since who came in through paid courses to know that isn't the norm. Nobody here sold me a shortcut, and I got pretty lucky landing here first rather than somewhere else. Something happened recently that made me take a step back. I met someone at an open networking event who runs corporate training and is writing a textbook alongside it. What she needs is her course turned into per-module walkthrough videos - her own slides on screen, voiceover over the top, one video per module. I built it as ICM, because that's just how I build now. Numbered stage folders, one agent reading the right files at the right moment, markdown carrying the context, and local scripts doing all the mechanical work that never needed a model in the first place. Her voice cloned from a module she'd already recorded, the deck walked and exported as frames, one script and one audio file per slide. The durations of those audio files become the cue sheet that stitches the whole thing together, so there's no separate timing step to get wrong. One module's through it end to end. 21 slides, 21 scripts, 21 audio files, one finished MP4. And there's around ten hours of modules to put through the same pipeline. As an 18-year-old that's a really good opportunity, and it's going to generate me some serious income. I wouldn't have built any of it without what I picked up in here first, so thank you to everyone who's answered a question of mine along the way. Nothing's finished yet. I've still got a lot to come and plenty I haven't cracked. It just felt worth stopping on rather than quietly getting on with the next thing.

As some of you know I have been working on a few modules for the classroom WELCOME TO THE WISE ARCHITECTS CORNER - THE LEGENDS · Clief Notes For a while now the sign on the door read 👷 Coming Soon 🚧 The First Rung Is Now Open Welcome. This is where we will demonstrate how AI works, where we build human-AI systems that last, and where the person at the keyboard stays the author of the whole thing. You do not need a technical background for any of it. You bring the curiosity, I bring the blueprints, and we build the understanding together. 🪜 Here is the climb This whole corner is one ladder, five rungs, and each rung raises what you can do with the machine: (1) Prompt engineering, talking to the model (2) Context engineering, giving it what it needs (3) Memory engineering, making it remember (4) Loop engineering, making it check its own work and then repeat (5) Graph engineering, running a whole system of them, with you in the chair You walk in asking "how do I even talk to this thing?" and you walk out running a system of them. That is the journey, and the top of this ladder is a system of machines, the bottom is one sentence, and the bottom is open today. 📚 What is open today The whole first rung, Prompt Engineering, all six posts: the Series 1 Overview, three classes, the payoff with its quick card, and my perspective on the rung everybody says is dead. 📆 A few things before you start One series a week. Rung two lands next week, rung three the week after, and this corner is going to grow fast, five weeks to the top. Follow these modules in order. You will find that each rung stands on the one below it, the second assumes you took the first, the third assumes you took the second, and the path only works if YOU climb from the bottom. Start at the map. The Start Here post shows you how every series in this corner runs and points you to the first door, the Series 1 Overview. Do not skip it (I know, you want the techniques, they are one door further and they are not going anywhere, the map is a short read, and it saves you a whole series of wondering where you are).

A founder asked me a question this week that I couldn't answer on the spot: do other businesses have a system like this? So I went and looked. Short version: outside this room, I can't find them. Everyone here runs some version of it already, a folder of markdown that Claude reads and maintains, and I'd stopped noticing how unusual that is. I'd like the room to tell me where the others are hiding, or whether we're further ahead than we think. The system in question is the one I've been installing at a three-person shop since August. The part that's now real: a knowledge base in a git repo that their own Claude reads before it does any work, a file register that watches their Drive and logs every arrival and every move, and a pitch engine that runs a brief through four human approval gates with the gaps written on the slide. Every filing carries who approved it or which rule filed it. Every page carries where it came from. When something ends up in the wrong place, the record says whether a person put it there or our rule did. None of the pieces are exotic. Markdown, git, a spreadsheet, an Apps Script, skills. What's different is that the knowledge is compiled and maintained, not retrieved. The agent doesn't search a pile of PDFs every time. It reads pages it helped write, with the provenance on them, and the rules for what it may and may not do live in the same repo. Here's what I found when I went looking for who else does this. The big holding companies in that industry have "operating systems" now. Real ones, sold to clients as the reason to hire the group. That's a data platform with agents on it, top down, owned by the group. Not a thing three people could own. Everyone in the middle has AI on top of files. The numbers from this year's surveys: about two thirds of agencies run brief generation in production, a third draft client reports with it, and the single biggest problem the independents report is getting AI into their actual workflows. Every "AI knowledge base" guide I read describes the same thing. Search over the shared drive. A memory server. Chat with your documents. Useful. Also not a record of anything.

One insight from the lesson that stuck with me: what we call an AI "agent" is usually not a single autonomous entity. It's a series of prompts orchestrated by traditional code. The model itself—when you call it—is just a function: prompt in, text out. The orchestration layer (the regular software wrapping those calls) is what evaluates the output and decides what prompt to send next based on human-defined conditions. This matters because it changes how we think about responsibility, testing, and what's actually "happening" in the system. When I understand that the model isn't choosing the next step, I see the control differently. It shifts where I'd look to audit or improve the system. What does this distinction change for you?

Simon Sinek's Golden Circle asks why you do what you do. You probably already have an answer for your own system. It's in an ICM somewhere. A values doc, a mission file, the folder that says what it's for. A COO I work with asked a different question on a call last month. He wasn't asking why his team ran performance reviews. He was asking what would happen if they stopped. Nobody in the room could answer. Those are two different tests. Sinek's question checks whether you can articulate a belief. The COO's question checks whether that belief is load-bearing. You can pass the first one in a conference room with nobody watching. You can only pass the second one by pointing at what actually breaks. That COO had a month of build-versus-buy behind him. Platforms compared, discovery calls stacked up. Then he asked one question, and the room went quiet. Nobody had ever tested whether their reasons for running reviews would hold up under questioning. Take whatever you've documented about why a process exists and ask what specifically breaks if you stop doing it. Name a consequence you can check: a number that moves, a decision someone makes differently, a person who acts differently on Monday. If you can name it, you have a claim to test. Then look for evidence that the process produces that result. If you can't, you haven't established what the process does in practice. @Jake Van Clief's ratio describes database queries, logic, and AI calls. I apply it as an analogy to people and process. The ratio is his. A client forced the load-bearing test on me, not the other way around. If you want to run it against your own 60%, I'm happy to talk it through. The full article, The Tool Is Ten Percent, is in David's classroom.