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’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.

This is a follow-up to my previous post about using ICM for AI-driven machine learning research. In one week of ICM-assisted research, I've moved further than I managed in roughly six months of my master's thesis. This is what that looks like in practice: ICM/ ├── README.md ├── skills/ ├── agents/ │ ├── literature-intake.md │ ├── research-development.md │ ├── code-development.md │ ├── results-evidence.md │ ├── research-argument.md │ ├── thesis argument.md │ ├── self-review.md │ └── ... ├── research/ │ ├── context/ │ ├── literature/ │ ├── development/ │ ├── results/ │ └── reports/ ├── code/ │ ├── src/ │ ├── tests/ │ └── runs/ ├── thesis/ └── _system/ ├── rules/ ├── templates/ └── schema/ I can queue several research goals across different chats and let each one keep moving. The agents are not all doing the same job with different names. Each one has a bounded responsibility, its own context and a clear handoff to the next part of the work. - The literature-intake agent turns papers into usable research context. It extracts the claims, methods, datasets, assumptions and limitations that matter for my problem. It helps answer: what has already been tried, what can actually be reused and what still needs to be tested? - The research-development agent turns vague ideas into explicit questions, hypotheses and experiments. It forces the research to become testable before implementation begins. Instead of “try an LSTM”, the goal becomes something like: under these conditions, does recursive probabilistic prediction outperform a defined baseline? - The code-development agent owns the implementation. It builds the data pipeline, model interfaces and experiment code while respecting the assumptions defined by the research question. Its job is not to decide whether the research is meaningful. Its job is to make the proposed experiment executable and reproducible. - The testing agent checks whether the implementation behaves as intended. It verifies data shapes, transformations, edge cases, saved artifacts and the parts of the pipeline that can be checked mechanically. A passing test gives me confidence in the software. It does not give me scientific confidence in the conclusion.

🎁 $1,000 IN EDUBAWARE CREDITS 🎁 One winner takes it. 📋 THE CHALLENGE Build a folder-based AI translator that takes one kind of work and turns it into another kind of work. Same shape in, same shape out, every time. Not a summarizer. Not a writer. A converter with a contract. This week's deliverable is one translator folder that someone could drop into a Claude project, feed it the input it expects, and get back the output it promises. Every time. Without surprises. 🎯 PICK YOUR CONVERSION The conversion is yours. Pick one you do by hand right now and hate. A few sparks to get you thinking: - 📞 Sales call transcript → CRM notes in your team's exact fields - 📝 Long-form essay → LinkedIn carousel, slide by slide - 🎙️ Meeting recording → product requirements doc - 🔬 Research paper → investor one-pager - 🐛 Bug report thread → Jira ticket with repro steps - 📧 Customer email → support ticket with severity and category - 📊 Spreadsheet export → weekly status update - 📖 Interview transcript → case study draft - 🧾 Receipt photos described in text → expense report line items - 📋 Discovery call notes → SOW first draft The more locked-down the output, the better. "Turns notes into a doc" is not a contract. "Turns discovery call notes into a five-section SOW where section 3 is always scope exclusions" is. 🔥 THE ANGLE THIS WEEK Last comp was The Auditor. Every finding cited a provision so a reader could open the standard and check. The comp before that was The Cartographer. Every card cited a file and a line so a reader could open the source and check. The Translator is the same discipline, one more time. Every line in the output traces to a line in the input. ↔️ A translator has three properties that a summarizer does not: 1. The output has a fixed shape. Same fields, same order, same format, regardless of what the input looked like. If the input was messy, the output is still clean. If the input was short, the output still has every field, marked empty where there was nothing to fill it.

Saw a good article on voice skills this morning. Instead of adopting them I asked GPT Astra to run an audit. Btw, I already had a voice-editor skill but it still wasn't quite sounding like me. "Research how to get AI to write in your voice, including any specifics for Claude models or GPT models, if there are differences in the models. But I feel like the voice profile that I have currently, and the Voice Editor skill and the Voice Refine skill, aren't really nailing it. I have to frequently revise and tell it the same things and get it to not write like AI. Go find all the skills, plugins, connectors, do a last 30 days search of Reddit, X, and other platforms, and then come up with an action plan for how we can improve so that you can write in my voice and so Claude can write in my voice. So basically any agent with the right skills can write in my voice." And it came up with a solid plan (condensed version more sensible for posting here - the actual plan is longer, more detailed, and contains specifics about my environment). ---------------------------------------------------- The goal: drafts that need less work before I can send them, with the facts and intent intact. The plan ✍️ My writing + corrections ↓ Examples I review and approve ↓ Small shared voice guide + guidance for each situation ↓ Draft → Check → Small repairs ↓ 🧪 Blind comparison → My edits → Next review A few things I'm building into it: - Start with real evidence. Compare an AI draft with my final version where both exist. Keep track of who wrote what. - Match the situation. A quick email, technical explanation, and community post need different amounts of detail and polish. Pull a few relevant examples for the task. - Keep lessons conditional. Removing an extra question from one email doesn't mean every email should have exactly one question. The recipient and purpose matter. - Keep me in the loop. The system proposes examples and rules. I approve them before they change the voice guide. - Keep source material private. Test with fictional tasks. Share only examples I've approved for that purpose.