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?

This might not be sustainable since these days we want every thing to be a workflow we never have to return to. But I thought to share because someone might need this. These days I tend to write down thoughts as they come and this practice has helped me write more. It’s a refreshing experience and I’m grateful for that. I remember the early days of grammarly. I used grammarly to structure my sentences and correct any typos or grammatical errors. But now everything goes through Claude. So if you can follow through with writing your first draft, here’s the hack. AI is great at refining drafts to make it clearer and preserve your idea without drifting. But it might introduce words and phrases outside of your voice. It’s even worse if it’s the AI voice that we don’t like. 💥HACK: When refining your draft, tell it to retain your voice and choice of words as much as possible except in times where there’s an unclear message, grammar issue or wrong choice of words. And for every change made, it has to give a summary of what changed and why so that we can apply judgment. If we think about, this doesn’t have to be a one-off exercise. You can begin to train your Copy agent based on these types of sessions and in no time you have a system that writes in your voice.

🙋 A little about me: I'm a video editor based in Argentina. Besides business/work, I'm very big on fitness and am currently obsessed with tennis 🎯 My current goal: Learn the fundamentals of AI and incorporate it into my freelance career for more and better clients. Maybe I'll want to create an agency or network of editors to give the work to when I am overbooked. 💪 What I'm currently building/working on: Once I go through the classes and need to start implementing to follow along, I'll build a folder structure to create carousel posts for one of my clients 🤔 My biggest struggle or question right now: No struggle yet; I need to start building. It's been only 24hs and watched a ton of community posts and gone through the onboarding very thoroughly. I'm anxious to build, but I want to be sure I go through everything very intentionally. Let's get it! 🚀

I’ve done a master’s thesis before: genetic algorithms, XGBoost, Monte Carlo simulations, graph optimization. Lots of ML, long before modern AI could meaningfully help me. Back then, the hard part wasn’t having ideas. It was turning those ideas into structured experiments, reliable evidence, useful visualizations and clear conclusions, without losing weeks to setup, documentation and context switching. ICM changed the game. In one week, I’ve done more research than I managed in six months of my master’s thesis. The difference is not just speed. It’s the number of ideas I can now explore, test and refine: - Turn a vague idea into a concrete research question. - Convert that question into an experiment or proof obligation. - Generate the code, tests and evaluation structure. - Produce visualizations and evidence automatically. - Inspect the results and use them to guide the next idea. - Keep the reasoning, context and decisions connected throughout. - Do all of the above concurrently. The ICM becomes a research operating system. It helps separate what the code proves, what the experiment observes, what the data supports and what still requires human interpretation. That distinction matters. A test can show that the implementation works. It cannot prove that the model is useful. A result can show that one approach performed better. It cannot automatically explain why. A paper can inspire an architecture. It cannot validate that architecture on your dataset. ICM gives each of these things a place—and connects them into a traceable research loop: question → hypothesis → experiment → evidence → interpretation → next question The most powerful part is that ICM does not replace the researcher. It amplifies the researcher’s ability to think. The human provides judgment, curiosity and scientific direction. The ICM provides structure, memory, continuity and execution. That is why it feels so transformative for ML research. It turns research from a sequence of disconnected tasks into a living system that can continuously generate, test and refine knowledge.

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).