I'm currently using AI for a pet project writing a private weekly newsletter for my fantasy football league, and right now I'm at the [copy-paste] stage. The thing that really caught my attention from this lesson is the entire concept of separate files for tasks, because that level of breaking things down step by step is something that I've had to do most of my adult life. I often find myself in roles that require creating SOP's or training materials and I feel like talking to an LLM is like talking to the 'new guy' or a child that read the internet! I'm excited to learn how to automate this process and see how structured approaches to AI can help me save time in the process. It currently takes me anywhere from 3 - 5 hrs to put it all together because even with my current prompts/skills, I need to heavily edit/audit the output. Figured this would be a great place to start as it's a simple test based project that can be broken into steps, and has a measurable output. I promise my next project will be far more exciting whatever it is! lol.

Hey Clief Notes fam! 👋 I'm Mike and I'm stoked to be here. First time posting and using the Decster plug-in as I work through the lessons. 🙋 A little about me: Most recently, I worked in Customer Success and Account Management for a SaaS company that services restaurants, with a background spanning tech, restaurant management, and automotive (total gearhead). Outside of the day job, I have a full time responsibility to walk my dog twice daily, home DIY projects, and running a private newsletter for my fantasy football league. 🎯 My current goal: mastering generative AI integration 💪 What I'm currently building/working on: Streamlining that newsletter I mentioned as a 'project' to better learn with. 🤔 My biggest struggle or question right now: Effectively bridging the gap between customer success strategy and technical AI integration, while navigating industry non-compete constraints as I look to pivot my career. Looking forward to getting to know the community better!

Hi all! Video two is done. The first one was the human side of how I work. This one is the Claude side: how I run it so the right model does the right job, without me having to tell it. It's 3 minutes 45 seconds, and I explain it as a mechanic's shop: - Haiku is the parts runner - Sonnet is the mechanic - Opus is the foreman - The Fable models are the specialist I call in on purpose You don't pay the foreman to fetch a filter, and you don't ask the runner to decide if the engine is worth saving. Then it covers how my rules sit in layers (one for everywhere, one for each project), why that keeps the cost of a request down (the cache), how helpers and workflows use it, and my own numbers from the last month. About 96% of my input tokens were cache reads, which at public list prices works out to roughly 90% cheaper than paying full price for every token. That's list-price math, not my bill, and the video says so. I'm not saying it's perfect or right. It's just what we built. I'm still learning and I still get things wrong, so if you spot something I got wrong, please tell me. Video: https://youtu.be/6RonYbHcbbw First video, if you missed it: https://www.skool.com/cliefnotes/the-most-productive-procrastination-period-ive-ever-had Heads up: the narration is an AI clone of my own voice (ElevenLabs), every drawing is an illustration, and I wrote the words and directed Claude to build it. Which model do you reach for first, and what makes you switch up or down?

Does an AI really understand your job, or does it just follow a rulebook? In 1980, John Searle imagined a man in a room who knows no Chinese. He gets Chinese symbols, follows a rulebook, and sends back perfect answers. People outside think he understands. In reality He doesn’t. I used to think that’s all AI is. Then I heard Geoffrey Hinton argue that modern AI isn’t just guessing the next word. In building that guess, it forms a model of the world, and he says that is what understanding is. He argued that the current AI understands like a human and is closer to us then machine code. So I’ve been wondering about ICM (organising a job into folders, stages, and rules, judgement calls, opinions written down on how the work should be done). Are we just handing it a better rulebook? Or are we giving the AI a real picture of the job, with a human checking each stage? I lean toward the second. What's your opinion?

📣 Update (10/22/26): Comp 15 Rules Added Current Challenge: Competition 15 The Challenge 💪 - Rebuild a past comp entry. Yours or someone else's. Then prove that its better. Rules & submissions 🗒️ -https://www.skool.com/cliefnotes/weekly-comp-15-the-rebuild Good luck everyone! --- What this thread is: I’ve seen a number of people either not know competitions are going on or know how to find them so here is a quick single source thread where you can get to them all. I’ll keep this updated as new challenges are released. See the current challenge as well as all past challenges below! Format for community challenges: https://www.skool.com/cliefnotes/how-competitions-work-from-now-on?p=84912d60 Weekly leaderboard competition: Alongside the biweekly challenges, there is a weekly leaderboard challenge where the member at the top of the 7 day rolling leaderboard wins a free upgrade to their membership. This winner is announced on Mondays typically. --- Past Challenges Competition 1 The Challenge 💪 - Build a brand voice document for a pet grooming business Rules & submissions 🗒️ - https://www.skool.com/cliefnotes/first-ever-weekly-competition-is-live?p=90f50bf9 Results🏆 - https://www.skool.com/cliefnotes/weekly-comp-1-winner-ian-barriopedro?p=0501b57e Competition 2 The Challenge 💪 - Build an artifact of a financial advisor client intake system Rules & submissions 🗒️ - https://www.skool.com/cliefnotes/weekly-comp-2-the-artifact-sprint?p=15145a68 Results🏆 - https://www.skool.com/cliefnotes/weekly-comp-2-winner-virgilio-robinson