First paid-path audit tomorrow — talk me through it? Tomorrow I run my first automation audit for a medical/behavioral health billing company. Free audit, paid build after — that's the deal. Setup so far: Pre-call email sent (tools list, screenshots, one real failure example) Client is recording Looms of the broken automations, narrating the "why" while clicking Zoom cloud recording on → transcript drops into 00_capture → workflow-map after One thing that's got my attention: they've worked with AI companies before, and those companies shut down. So I'm walking into a client who's been burned. Questions for anyone who's done a first client audit: What do you wish you'd asked on call #1? How do you handle a client who's skeptical because the last AI vendor disappeared? Anything you'd add to the 60/30/10 gut-check when auditing billing work specifically? Will report back after the call.

A few days in. Starting to see where the Clief Notes AI is actually becoming useful. Some of you are using it to figure out where to start. Others are asking specific questions and getting pointed back to lessons or drops they didn’t even know existed. And some are basically using it as search: “Have we covered this before?” That last one alone saves a lot of scrolling. @Anthony Brady shared a really good example of how he’s been using it. He said the agent has been helping him find the right lessons, figure out where to go next, and actually apply the material to what he’s building. He’s even been pushing it with harder questions, and when it doesn’t know something, it tells him instead of just making something up. What I really liked was how he described using the tools together: The agent helps him navigate. NotebookLM helps him go deeper. And Decster keeps him moving through the course, completing assignments, and staying active in the community. That’s exactly what we want this to become. Not another AI tool you open once and forget about. Something that actually helps you use everything already inside Clief Notes and keep moving forward. We’ve also caught a few questions where the agent could’ve pointed somewhere better. Keep sending those. The more you use it, the better we can make the experience. This thing should get more useful as we see how you actually use it. If you haven’t tried it yet, start simple. Ask it the next question you would normally post in the community. Have you tried it already? If not, comment “Navigator” and I’ll send you access to it.

This is a brain dump turned into a post, summarizing the work I've done with Jake's ICM method. I want to share what I've learned to see how these use cases resonate and what I could do better. It took me probably two months of reading through the community and using the resources available before this actually clicked. What did it for me was a post @Curtis Hays wrote a few months back (https://www.skool.com/cliefnotes/the-folder-system-became-my-agency?p=1e30aba0). Reading that post is what helped me understand how this actually works, and I'm hoping this post can do the same for someone else. There are a lot of different ways to come at this. >>>How I wrapped my head around this I lead development teams, and I also do what I'd call typical project or product management work. So a lot of how I thought about this started with a simple question: what are the most common things I produce or share, at a daily, weekly, or even monthly level? I wrote those things down. From there I asked how they actually get created, where they start, what the process of drafting or iteration looks like, and where they end up. About 50% of my day is spent on the phone, and most new ideas, action items, and questions come out of those calls. Sometimes it's email, but most of the time it's calls. Before, I'd use AI to help draft or compile something, but I was feeding it context manually every time, sometimes a brain dump on the call itself, sometimes a brain dump afterward. I'd work on the thing, it would spit something out, and I'd save it or send it. What I realized was that I needed a way to take the information from those calls and actually store it, so I could recall it and use it again for whatever needed to happen next. Here are 4 different use cases of how I use this ICM framework. The first 2 are functional, I use them everyday. The last 2 are more ideas, but only because the opportunity to implement has not presented itself just yet.

I built a system. It teaches AI how to build visual identities. It doesn't drift. Here's the story: I used to hand-build every visual identity myself. Client work. My own channel. Same routine every time. I explained the colors again. I explained the mood again. Nothing carried forward. Then I tried AI. Same brief, ten times. Ten different looks. One shot came out "noir". The next looked like a postcard. Same words in. Different result out. The model forgets. So I changed the approach. I stopped writing better prompts. I started building in stages. 3 stages. Each one builds on the last. Stage one locks the idenity. Color. Material. Type. The rules for how they combine. Stage two runs that identity through real scenes. A person. An object. A place. An idea. Same rules. New subjects. Stage three puts it in motion. It reveals. It connects. It transforms. Same materials. Same colors. Now it moves. Nothing gets re-explained. Each stage hands its work straight to the next. Drop them in Higgsfield. Prompt each scene. Full animation - no style drift. I tested it on a style I'd never planned to use. "Archival Investigative Montage." Ran it cold. It came out the same quality as everything else. That's the test that mattered. Not the polished demo. The one I didn't prepare for. Are you building multi-step AI systems yet? Or still prompting one shot at a time?

"Hello everyone! 👋 I'm Javier, and my coding knowledge consists of HTML+CSS, and basic JS+PHP. My real thing is storytelling. I started using Claude Code in VSC with an academic project I was doing by hand: a historical archive manager (backend). For that project, I needed documentation, so I created a new project that crawls sources and indexes them, extracting the facts. The succession of needs to resolve generated several projects (one per day), all interrelated: distillation of pictorial styles, image generation, video editing (integrated via Python with DaVinci Resolve), audio generation... All of them operational, with a minimum viable feature test. During the process, Claude encountered execution frictions, many thanks to VSC's LINT extensions (Docker stuff, system paths, nomenclature like python vs. py...) and reasoning issues (overlooking instructions or paths), so I started (well, Claude did) to establish a system that logged these frictions to avoid them in the future. My approach was to keep everything on track as much as possible with code, which is deterministic: hooks (preferably pre-tool), access doors to edit Claude's memory files, etc. Everything documented both in the repo and in the GitHub commits. At this point, I needed something to organize the chaos of ideas I had for each project, to monitor the repo ecosystem (health, status), and to log useful info like repos, documentation, etc. So I created the 'Organization' repo, to rule them all, with an update propagation system. In theory, it was perfect: absolutely everything was logged. The problem is that almost everything went into a drawer to be forgotten. Too much context in Claude's reasoning, poorly mapped. I discovered Karpathy and Obsidian, and started moving the useful info from 'Organization' to a new version, creating a vault with more segmented info, and keeping Claude on track when consulting it (using Kepano's skills: https://github.com/kepano/obsidian-skills/tree/main).