I vibe coded a little skill and put it up on github https://github.com/blackghostosint/icm-workspace-architect . Hoping it's ok with @Jake Van Clief - I included your research document and a link to the skool page. If you test it out I'd love some feedback. Just playing around on a Sunday.

Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.

Tear it up today keyboard warriors! 🔥😎🫡

Many of us in the AI space are still selling the wrong thing. We package custom models, prompt engineering, agent setups, or hourly consulting. The conversation stays locked on tools, hours, and technical specs. By 2026 that model has clearly hit its limit. I’ve lived the shift firsthand while grinding open-source stacks for nonprofit work—Cloudflare tiers, Alibaba credits, Jake’s method, ZeroClaw experiments, and finally landing on Hermes Agent + Cognee. The tech itself became fast and cheap. Specialized work turned into a commodity almost overnight. Clients compare on price and speed, and the race-to-the-bottom kicks in. That’s why I flipped the script: stop selling the service and start selling the measurable value. Lead with real outcomes—less manual grunt work, tighter decision loops, lower operational costs, and repeatable processes that actually move the needle. Structure engagements around shared success metrics instead of hours or fixed deliverables. Price based on impact: performance bonuses, value-share pieces, or retainers tied to sustained results. When the system improves, everyone wins. Jake’s approach is a perfect example of this in action. He’s giving away his Interpretable Context Methodology for free, openly sharing the full system so anyone can adopt it. That single move creates exponential value across the entire community—more capable setups, clearer thinking, and faster progress for all of us without gatekeeping or hourly billing. Keep your own work minimal and transparent. Dig into the client’s real friction points. Co-define success in concrete terms. Build lightweight, maintainable architectures instead of black boxes. Show clear before-and-after results. The outcome is powerful: the system handles the repetitive load reliably, freeing up attention for the judgment and creative work only you (or your client) can do. You shift from being another vendor to becoming the guide who cuts through the noise. I’ve seen this mindset pay off in my own volunteer projects and early client conversations. It just feels cleaner and more aligned.

Every few months, a new AI orchestration framework drops. More dashboards. More abstractions. More complexity. You wire up a simple workflow… and spend hours debugging it. Here’s the truth: most AI workflows don’t need “smart” orchestration. They need structure. A simpler approach already exists: Jake's folder architecture. Inspired by Doug McIlroy and Unix pipelines: Do one thing well Use plain text Make steps work together The idea: Folder = Pipeline Each step is a folder: instructions.md → what to do output.md → result Flow: AI runs → human reviews → move to next step That’s it. No frameworks. No hidden state. Example: /01-research → /02-draft → /03-review → /04-publish Why it works: Clear input/output at every step Human becomes the control layer Easy to debug, edit, and stop Works with any AI tool Upgrade it with one small addition: Add status.md RESULT: SUCCESS | WARN | FAIL Now every step is measurable, not guesswork. Rules that make it powerful: • One folder, one task • Plain text only • Always include a stop instruction • Review before moving forward • Version your pipeline like code When to use it: When accuracy matters more than speed When human review adds value When you want clarity, not abstraction The Unix pipeline is 50+ years old and still runs the internet. Your AI workflow doesn’t need more tools. It needs better structure. Thanks to @Jake Van Clief for this workflow.