Folders Over Agents: The AI Layer Nobody Teaches

Added: 2026-06-04

[00:00:00]If you've been watching AI for the last few years and feel like you're constantly falling behind, this video is going to change how you think about all of it. I'm going to show you why almost every AI course, every prompt training, every type of package or thing or training that's going on out there is teaching the wrong layer. I want to teach you the layer that almost nobody else is teaching. The one that survives the next model update, every framework depreciation, every bigger email or chain that says we're sunsetting this product. the layer that all the big guys know about but they're not talking to you about. And I'm gonna show you what 30,000 people in our community are already doing with it. My name is Jake Van Clee. I'm founder of Adoba. And for the last 3 years of my life, 15 hours a day, I have been learning and I'm about to share what I have learned throughout all of this process in this new AI world and what the next decade is going to look like. Perhaps you learned prompting six months ago or a year ago ago and it worked for a while and then the models got better and now most of that became irrelevant. Or perhaps you built some fancy agentic framework or a new startup tool and it worked but then the next API update or the next cloud update the whole thing broke or became irrelevant again. Or even worse, maybe you took a course paid for a couple courses and half of what was in those courses was outdated in less than 90 days. I mean think about it. You hop on Twitter, LinkedIn, all of these Instagram and YouTube and somebody is posting a new workflow, a new tool, a new you have to try this or check out this feature. This changes everything and you haven't seen it yet. So now you feel like you're falling more behind and it just keeps piling up and you don't know what to learn. But here's the thing that nobody's telling you. You're not falling behind because AI is moving too fast. AI isn't moving that fast. Almost 10 years ago, the attention is all you need paper from Google came out. That's what all these transformer models and language models are built off. Basically, that's been 10 years of patterns that have existed and are the same patterns today.

[00:01:54]And even further, these same patterns, these software fundamentals have existed for so much when personal computers were first being built when the internet was being built with mobile phones all the way back to the 1950s. We've seen this movie play out before. And there's fundamentals that have existed the whole time that you can learn. Is you're not behind because you're slow. You're behind because you're learning the wrong layer. And until somebody teaches you which layer to be at, what that layer even looks like, you're going to keep feeling like this. And you're going to keep chasing and you're going to keep starting over. And every time something updates, you're going to feel behind.

[00:02:34]We can change that with a simple process. I want to show you how to slow down, how to stop and think about what's going to be useful in 10 years, not just two months from now. And I really want to say that and explain that through two main stories. What I'm going to teach you right now didn't come from a lab, PhD program, or me sitting there and running studies and ethics programs. Not hating on those, but experience teaches a lot. And this story comes from me being in the Marine Corps for over eight years and then another three years of being frustrated by gatekeeping and people telling me and using fancy words to hide something simple. So in the Marine Corps I was working on cryptographic systems. I was working on F-18s, F-35s, the avionics behind them.

[00:03:20]These were zero defect envir certifications. You can have engineers and PhDs. You can have experts, people with high rank telling you the way to do something, but at the end of the day, they're hearing that from someone else.

[00:03:33]You can actually cut 90% of that process out and still come to the same outcome that you needed. Now, yes, there is still important times you should listen to that experience. There is times that that matters. But for most situations, for almost everything, in order to make real progress, you need to think about the fundamentals and ignore everything else. And living in that environment, living in how the world was supposed to work, but then seeing how it actually worked was the first crack into how I thought about expertise. The f the final crumble happened when I was actually getting my master's degree. And it's actually how I ended up building my methodologies and processes that I'm working with today. It's ICM. The second story is how I built the methodology that thousands of people are using today. And I want to be honest with you about this. This is not the version most founders would tell. I didn't sit down with a design methodology. I didn't have a Eureka or whiteboard moment. It was really all the way back in 2022 when I started messing around with language models around my research and and academic. I was telling my professors that I wanted to use them to essentially help me write articles or at least see what would happen if they did write these articles, these these academic stages. What was missing? I had to organize everything. every output I needed to track and I put it into files so I could copy and paste it over and I kept creating organization structure for these AI just to track what it was doing and understand it. That was it. That was the start. And over time I I realized that that process for something that wasn't even coding was git. That was version control. It was more than just a basic process. It was something that could be used to scale my thought. I stopped using language models just for software coding and building, but looked at for outcomebased work. How could I use these systems to create animations or to organize things completely differently for my writing and language work? And I had to organize everything completely different. I had to be able to set it up for my chaotic creative environments where I would open sessions randomly without losing context. I needed to be able to separate files or route the agent to specific places. But I kept working through this whole chaos of my own work. I built my own agentic flows. I tested them. I used other people's frameworks like lane chain and semantic kernel and all of these things.

[00:05:56]But I kept coming back to the basic files and systems because I could get to the outcome faster. And more importantly, I could see and control the outcome as it was happening. And I realized that that process worked and I know I needed to formalize it. So, I wrote a paper, but I didn't write it to formalize it. And I wrote it spitefully because I knew that these researchers, these PhDs that I was working with every day would never take it seriously if I didn't write it in the form that they were used to, in the way that they like to gatekeep knowledge. And don't get me wrong, I love academia, but I literally had PhDs tell me to my face that language models and AI were useless.

[00:06:40]Meanwhile, I was actively building real tools, things that worked with them, doing things that was never possible before, getting actual outcomes, and they were telling me my tools didn't work while I was using them. And the critics aside, they had good reason because other people, other AI gurus and instructors were teaching features, and they still are. They're just teaching basic prompts or frameworks and they're giving AI a bad name because their courses are shallow or the outcomes weren't really real. They weren't focusing on actual realworld implementation. So I just got angry. But let me just give this methodology away.

[00:07:20]Let me actually show what's working, share it with everyone, let them use it.

[00:07:24]And then I could use that as proof to say look this is the way. This is where you get real outcomes. I didn't want to gateep it. I wanted to prove it. And in all of this, I did some research of the past. Where else did people have to deal with these sort of problems, especially in the computation field? And it brought me back to a wonderful, most brilliant woman named Grace Hoppers. They wouldn't use this. They told her that it wouldn't work. She had a working compiler in her hand and they wouldn't touch it. I'm not telling you this because AI is going to be this distant thing that's going to work one day in the future. I'm telling you this because it works right now and I've been I have something in my hand that is working and I've been making it work for 3 years straight with over 30,000 other people actually using it and getting outcomes from it. That's the story. That's where all of this methodology and process comes from. But I keep using the key words. I'm still gatekeeping even in this video and I don't mean to because it's hard to describe it so quickly. So, let me tell you what the actual methodology is because this is really the part that changes everything. Every AI course you've ever taken, every framework you've learned, every tutorial you've watched has been teaching you agents or agentic flows. It's been telling you, oh, you got to use NAND or Langchain or Autogen or all these multi- aent agentic systems. You have to have these orchestrations. And that's great. Some of them can work, but that's still the surface level. That's not getting you set up for the next decade. Agents, frameworks, all of these things can be absorbed. They can break. They change with model updates. A lot of them can be outdated in 90 days because of how things are adopting. They're not moving fast because those systems are out those systems are not becoming obsolete because AI is moving too fast. They're becoming obsolete because people are building at the wrong abstraction layer.

[00:09:22]And every 90 days, you pretty much have to start over because you're not learning the right layer. The layer underneath all of that is what I am teaching. It's what my interpretable, it's what my M focuses on. Interpretable context methodology. And the tagline is the simplest way I can say it. Folders over agents. This is me not saying that you shouldn't build intense infrastructure. It is me saying that the organization and context structures are more important than all of it. The way you structure information, the way you organize folders, the way you route between context layers, that's the architecture. The agent is just the thing using the architecture. When you build a folder system, a file setup that works, it doesn't care what model you use. It doesn't care what framework or AI you have. It doesn't care what's hot this month. If cloud updates or OpenAI updates something or version 6 comes around, it works. The architecture, the architecture and context will always the architecture and context survives. It can abstract infinitely. When Claude updated, my workflows kept working. When GPT 5.5 dropped, my workflows and systems kept working and got better.

[00:10:46]When the next model lands in a few months, my systems will work better.

[00:10:52]There is no replacing it because it is the fundamental of how these systems work. And realistically, my methodology works on five layers of content.

[00:11:02]Identity at the top, then routing, then stage contract, followed by reference material, then working into working artifacts. And you can organize all sorts of databases and folder bases and scripts around this. And any AI agent can come in and read everything. That's the whole framework. It looks simple. It can be broken down simply because it is.

[00:11:25]And there's a reason nobody else is teaching this. It doesn't really look that impressive once you start doing it.

[00:11:32]There's no demo video where I show you a sexy agent multi-swarm system doing your job for you. There's no flashy framework name. There's no library to import. It's just folders and the discipline of organiz through the lens of a world of AI. It is me taking what worked in the 1970s and has continued to work and work better every year since and simply adapting it to this new set of abstraction, this new set of AI tool.

[00:12:02]And you see, most educators can't sell that. Either they don't know how to articulate it or they need the spectacle. They need the latest tool.

[00:12:10]They need the the new shiny thing because that's what gets them clicks and gets them views because that's all they care about. I'm not selling a spectacle.

[00:12:20]I'm trying to give you the layer underneath. If you spend 3 months learning my processes and systems in 5 years, you'll still be able to get value from it. You're still using it. If you spend three months learning the latest agent framework, nine months, most of it's going to be either useless, absorbed, or you're going to be relearning a whole new version of it.

[00:12:43]And that's it. That's the difference.

[00:12:45]That's my whole pitch. And I could give you proof. I could spend the next five minutes telling you all the enterprise companies I've been working with, whether it's Pacific Life or KPMG or the UK government. We've done it all. I've worked with them and gotten real outcomes, real outputs, and real reports. You can go check it online. But honestly, I don't care about recommendations or big companies or anything like this. The real proof comes from my community. We have 30,000 people in my community at the time of this recording. And most of them got there within 2 months. 2 months. And 30,000 people join to learn about what we're building. They're not paying to be there. Most of them are all on the free tier. Maybe 1,500 to 2,000 of them are paying for more time with me. Everyone else is free and every single day all of them are posting real win. They're getting token reduction. They're cutting API costs and hour costs and structural issues by sometimes 80 90 95% because they finally understand how to structure context, how to structure thinking in software. These are real outcomes.

[00:13:53]people getting jobs, people getting raises, people getting promoted, people closing new clients, and people building products that work because they're saving hours and hours of week and finally have a system that doesn't break. They finally have a system that they can understand. And they're not posting about this in the community because I'm paying them or to justify a purchase. Most of them haven't bought anything. They're posting because it works and they want to share what they're proud of working. That to me is the proof I trust. I care about that and that's what you should look at more than anything out of all the stuff I do. And they're doing that not because AI is magic, not because they're keeping up with all the new updates, but because their structures were right, because they had the context routed correctly, because the architecture, the data, the thinking survives transitions from one model to the next or are amplified by them. When CTO's or software engineers come asking for time with me, it's not because they saw a sales page. It's not because I'm sending cold emails. I don't have any automations for messages. I don't have any automations for crazy emails hitting everyone else. It's because they're seeing outcomes from stuff I'm giving away for free. It's because their friend had shown them an actual system they've built and they are coming to me wanting to know more, wanting to know how it works. That's word of mouth from people who have nothing to gain by talking about it.

[00:15:21]Unfortunately, and fortunately, I've had a massive influx of people asking to learn more. And I've been wanting to solve this problem, as I said, of this knowledge gap. There's such a huge knowledge gap. And I know it can be solved with the right systems, with the right learning at the right layer. So, I'm going to be launching something called the Lysum. This isn't a course.

[00:15:41]This isn't uh some weird thing where I'm going to be telling you how to make money online. This is learning. This is knowledge. This is schooling. This is knowledge packaged into real work. I'm going to be creating three cohorts. Each one is going to be focusing on a specific nuance of my method. Technical, business, and creator. Those are the three cohorts. Same methodology for each one, but different applications of the methodology. If you're a developer or an engineer or technical founder, you might want to go into the technical cohort.

[00:16:15]You will build an actual production system, learn about these methodologies.

[00:16:18]You're going to think about API integrations, agentic architecture, thinking through the lens of my methodology, the underlying processes.

[00:16:26]This is where we get into the deep weed, the technical systems, and you end up with a tool or a system you ship. If you're in ops or management or you're running a business or looking at doing consulting, then my business cohort will be learning similar things as the technical except we're spending more time on how to direct technical people.

[00:16:44]What happens when you have a team of people using these tools and models? How do you get ROI on these implementations?

[00:16:50]Your capstone project is going to be focused around creating the systems of a company. What does an AI native company even look like? You're going to have that automated spec, that process, that structure before you leave. And if you're a creator, you're an artist, you're a marketer, or maybe even just an educator or solo operator, then the creator fund is for you. I'm all about show your work. And sometimes it's hard to show your work in a world where everything is filled with yapping and talking heads. Well, here we're going to focus on the methodology, but we're going to build content pipelines. We're going to build oneperson systems that have allowed me to get millions and millions of views across all of my social medias with just myself and an audience scaling infrastructure. You're going to look at a capstone project that focuses on content production to run with or without you to scale who you are, not replace it. Again, each of these are focusing and teaching the same methodology, the same processes just with slightly different examples, different time waiting and different capstone. Now, you might be wondering, okay, well, if it's not a course, if it's not this annoying asynchronous thing, what is it is? These are going to be real live sessions over fourth trainings work with me and an entire team I am putting behind of highly educated instructors to help support you in and out of the class. You're going to get all of the recordings for lifetime.

[00:18:15]You can pull the transcripts, give them to your AI, re look back at them, listen to those questions again. You're going to get written curriculums. You're getting private cohort Discords. You're even going to have a certification by the end of this that's from my official company, Adoba, that is training enterprise people and helping them hire people. So, that training assessment certification is actually useful and something you can put on your resume.

[00:18:36]You're going to get lifetime access to my community through VIP going to help you. I'm not just teaching you and dropping you off. I am trying to make an impact on the world through knowledge, through sharing that process and building with people. I want to put my money where my mouth is and work with you. And here's the thing that no one else is doing. I'm going to guarantee you that you walk away with a working product, a working company or process.

[00:19:06]Not a just a certificate to hang on the wall, not a course completion badge, not a oo I hope you learn and utilize this.

[00:19:13]A real functioning thing that has outcome and output that you built during the program.

[00:19:21]And my guarantee is that if you don't, I will personally sit and work with you. I will bring my team on and finish it until you have something that works. I will ensure you don't walk away empty-handed. I don't see any other programs offering that promise because they can't. I actually use this with companies. I build these things. We know this methodology works and it works for hundreds of enterprise people as well as thousands of people in my community. and it will work for you, too. Now, you might be wondering, "Oh, well, how much does it cost all this?" Don't worry about any of that. I'm just having a wait list right now. I'm trying to work with everyone, see what we're doing.

[00:19:59]We're looking at scholarships, getting people in for free, all of that stuff.

[00:20:03]We're trying to open up a weight list right now. That's all we're doing. And I need your help just to get in.

[00:20:09]Right now, we're just opening the wait list for public enrollment. If you join the wait list, you're going to be the first to see any sort of pricing updates. You're going to be the first to see start dates. You're going to be able to give feedback on when and how you want to join and you're going to get access to all of this stuff before we open it to the public. There's only, and here's the important part, my time is limited and there's only so many people I can teach. So, we're limiting it to 100 per cohort. That is 300 seats in total. Mind you, in my community, we have over 430 people active any given minute. So, when those seats are gone, they're gone. If you know this is for you, join the weight list now. And I want to leave you and all the pricings and getting in and call to actions, all that stuff out of the way. I just want to leave you with this one concept. Grace Hopper, the brilliant scientist I told you about earlier, was working had a working thing in her hand. She had something that could change the way people work. She had something that was valuable and people constantly told her it was useless.

[00:21:17]I have a working methodology in my hand.

[00:21:20]You have working ideas in your head.

[00:21:23]30,000 people are using my methodology.

[00:21:26]Fortune 500 companies and getting real output out of it. People may tell you the same thing they always tell people who try to build something. They might tell you it doesn't work. They're going to tell you it's useless. You can listen to them or you can come build with us.

[00:21:43]We'll build together. Folders over agents, layers over libraries, methodologies over tools. Then we dive into all those things I just said after the next cohort fills up soon. Pick your spot, get on the wait list, and I'll see you inside. Until next time friends, happy learning.