A 22-minute argument that context architecture — not agents, not prompts, not frameworks — is the only AI layer that survives the next model update.
Posted
3 months ago
Duration
Format
Talking Head
sincere
Views
18.9K
1.1K likes
57 · 43
Big Idea
The argument in one line.
The durable layer underneath every AI tool is context architecture — how you organize files and route information — and the agent is just the thing that reads it.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
A developer, operator, or builder who has rebuilt their AI workflow at least once because a framework went stale or an API changed.
Someone who paid for an AI course and found half of it outdated within three months.
A technical founder or solo operator who wants AI systems that keep working as models upgrade, not systems that require constant maintenance.
Anyone running into high API or token costs who suspects the problem is structural, not just prompt-level.
SKIP IF…
You want step-by-step code tutorials — this video is conceptual and methodological, not hands-on technical.
You are not interested in an origin story and sales pitch for a paid cohort program; the core idea is delivered in about four minutes of runtime.
TL;DR
The full version, fast.
Most AI education teaches agents and prompts — work that becomes obsolete in 90 days because it sits at the wrong abstraction layer. ICM (Interpretable Context Methodology) argues that the durable layer is context structure: how you organize folders, route information between contexts, and define what the AI reads at each stage. That architecture is model-agnostic — it worked before GPT-4, it works with Claude, it will work with whatever ships next. The video spends its first half building the case through a Marine Corps and academic origin story, then names the five layers (Identity, Routing, Stage Contracts, Reference, Artifacts), cites 30,000 community members cutting API costs 80 to 95 percent, and closes with a pitch for the Lyceum, a cohort program with a completion guarantee.
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Opens with the AI overwhelm pain point — prompts went irrelevant, frameworks broke, courses outdated in 90 days. Establishes the core claim: you are learning the wrong layer.
02:19 – 04:03
02 · AI is not moving that fast
Reframe: the attention-is-all-you-need paper is almost 10 years old. Software fundamentals have been stable since personal computers. The problem is abstraction layer, not velocity.
04:03 – 08:23
03 · Origin story: Marine Corps and master's degree
Jake traces ICM to zero-defect avionics work in the Marines (fundamentals over expert opinion) and a 2022 master's program experiment organizing LLM outputs in files for context tracking.
08:23 – 10:56
04 · ICM defined: Folders Over Agents
Names the methodology: Interpretable Context Methodology. Folders over agents — the way you structure and route context is the real architecture. The agent just uses it. Architecture survives every model update.
10:56 – 12:42
05 · The five layers
Identity, Routing, Stage Contracts, Reference, Artifacts. No library to import, no framework name, no demo video. Just folders and discipline applied through the lens of AI.
12:42 – 14:50
06 · Proof: community results
30,000 members in two months, mostly free tier. 80 to 95 percent API and token cost reductions. Real wins: jobs, raises, clients, products that ship. Enterprise clients: Pacific Life, KPMG, UK government.
14:50 – 17:42
07 · The Lyceum pitch
Three cohorts — Technical (ship a production system), Business (AI-native company spec), Creator (content pipeline that runs without you). 100 seats per cohort, 300 total. Eduba certification.
17:42 – 20:08
08 · Program details and guarantee
Live sessions, lifetime recordings, private Discord, written curriculum. Guarantee: if you do not walk away with a working product, the team finishes it with you.
20:08 – 22:03
09 · Grace Hopper close and CTA
Parallel to Grace Hopper — working thing in hand, people said it was useless. Closes with waitlist CTA. Folders over agents, layers over libraries, methodologies over tools.
Atomic Insights
Lines worth screenshotting.
Agents and frameworks go stale every 90 days because they are built at the wrong abstraction layer, not because AI moves too fast.
The attention-is-all-you-need paper is nearly ten years old — the underlying patterns are not new, just newly accessible.
Folder structure and context routing are more durable than any agent framework because every model reads files the same way.
The agent is just the thing using the architecture; the architecture is what survives.
A file-based context system does not care what model you use, what framework is popular, or what API changed last week.
You can cut AI API costs 80 to 95 percent by fixing how you structure and route context — most waste is architectural, not prompt-level.
Most AI educators teach features and demos because spectacle drives clicks; durable fundamentals are harder to sell visually.
The same software fundamentals that worked in 1970s operating systems are the correct layer for organizing AI context today.
Building a system at the wrong abstraction layer means starting over every 90 days; building at the right layer means compounding.
Grace Hopper had a working compiler and people told her it was useless — the pattern of dismissing working tools before they are legible repeats across every technology wave.
Takeaway
The layer underneath agents never needs rebuilding.
WHAT TO LEARN
Agents, prompts, and frameworks expire — the folder structure and context routing they read from does not.
Most AI tools become obsolete in 90 days because they are built at a high, volatile abstraction layer — the fix is to work one layer down, at context structure.
The five-layer ICM hierarchy (Identity, Routing, Stage Contracts, Reference, Artifacts) gives any AI system a stable foundation regardless of which model or framework runs on top.
Context routing — deciding deliberately what an AI reads at each step rather than dumping everything into one prompt — is the single highest-leverage change most builders can make.
Organizing AI outputs into files from the start is how version control instincts transfer to AI workflows; the discipline matters more than the tooling.
Cutting API and token costs 80 to 95 percent is usually an architectural fix, not a prompt fix — the waste comes from unstructured, repeated context.
A system designed around durable fundamentals compounds as models improve; a system built around a specific framework resets every time that framework is deprecated.
Glossary
Terms worth knowing.
ICM (Interpretable Context Methodology)
A framework for organizing AI workflows around folder structure and context routing rather than agent orchestration. The premise is that context architecture is more durable than any specific model, framework, or prompt technique.
Abstraction layer
A level of a software system that hides the details of the level below it. ICM argues that agents and prompts are high, volatile abstraction layers, while file and context structure is a low, stable one.
Stage contract
In ICM, a formal agreement about what information is available, expected, and produced at a specific stage of an AI workflow — one of the five layers of context.
Context routing
The practice of deliberately directing which information an AI model sees at each step, rather than dumping everything into a single prompt or session.
The Lyceum
A cohort-based learning program by Eduba teaching ICM across three tracks (Technical, Business, Creator), with live sessions, a capstone project, and a completion guarantee.
“You're not falling behind because AI is moving too fast. You're behind because you're learning the wrong layer.”
One-line reframe of a universal pain point — zero setup needed→ TikTok hook↗ Tweet quote
10:05
“The agent is just the thing using the architecture.”
Compact, quotable thesis of the entire video→ IG reel cold open↗ Tweet quote
11:39
“There's no demo video where I show you a sexy agent multiswarm system doing your job for you. There's no flashy framework name. There's no library to import.”
Self-aware anti-spectacle line lands as credibility move→ newsletter pull-quote↗ Tweet quote
12:20
“If you spend three months learning my processes, in five years you'll still be able to get value from it. If you spend three months learning the latest agent framework, nine months, most of it's gonna be either useless, absorbed, or you're gonna be relearning a whole new version of it.”
Clear before/after comparison with a concrete time frame→ IG reel cold open↗ Tweet quote
The Script
Word for word.
Read-along
Don't just watch it. Burn it in.
See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.
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metaphoranalogystory
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 gonna 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 wanna 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 Cleef.
I'm founder of Aduba. And for the last three years of my life, fifteen 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 gonna look like.
Perhaps you learned prompting six months ago or a year 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 a new startup tool, and it worked. But then the next API update or the next Claude 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 ninety 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 ten 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 ten years of patterns that have existed and are the same patterns today. 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 nineteen fifties.
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 gonna keep feeling like this, and you're gonna keep chasing, and you're gonna keep starting over. And every time something updates, you're gonna feel behind. We can change that with a simple process.
I wanna show you how to slow down, how to stop, and think about what's gonna be useful in ten years, not just two months from now. And I really wanna say that and explain that through two main stories. What I'm gonna 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 eighteens, f 30 fives, the avionics behind them. These were zero defect 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. 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 file 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 wanna 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.
Moment. It was really all the way back in 2022 when I started messing around with language models around my research and and in 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 outcome based 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, 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. 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. 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 real world implement so I just got angry.
But let me just give this methodology away. Let me actually show what's working, share it with everyone, let them use it, 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 wanna gatekeep 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 gonna be this distant thing that's gonna 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 three 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 keywords. 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 gotta use n eight n or lang chain or auto gen or all these multi agent 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 ninety 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. And every ninety 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 ICM 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 Claude updates or OpenAI updates something or version six 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. When the next model lands in a few months, my systems will work better. 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.
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.
And there's a reason nobody else is teaching this. It doesn't really look that impressive once you start doing it. There's no demo video where I show you a sexy agent multiswarm 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 organize through the lens of a world of AI.
It is me taking what worked in the nineteen seventies 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. 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. 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. I'm trying to give you the layer under If you spend three months learning my processes and systems, in five 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 gonna be either useless, absorbed, or you're gonna be relearning a whole new version of it.
And that's it. That's the difference. 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 two months.
Two months and 30,000 people joined to learn about what we're building. They're not paying to be there. Most of them are all on the free tier.
Maybe a 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 eighty, ninety, 95% because they finally understand how to structure context, how to structure thinking in software.
These are real outcomes. 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 wanna 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 CTOs 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. 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 gonna be launching something called the Lyceum. This isn't a course.
This isn't, uh, some weird thing where I'm gonna 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 gonna be creating three cohorts. Each one is gonna be focusing on a specific nuance of my method all 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 engineer or technical founder, you might wanna go into the technical cohort.
You will build an actual production system, learn about these methodologies. You're gonna think about an API integrations, agentic architecture, thinking through the lens of my methodology.
The underlying processes is where we get into the deep weeds of the technical systems, and you end up with a tool or a system you ship. If you're an 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.
What happens when you have a team of people using these tools and models? How do you get ROI on these implementations? Your capstone project is gonna be focused around creating the systems of a company.
What does an AI native company even look like? Like? You're gonna 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 gonna focus on the methodology, but we're gonna build content pipelines. We're gonna build one person 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 gonna 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? Are gonna be real live sessions over forth 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 gonna get all of the recordings for lifetime. You can pull the transcripts, give them to your AI, relook back at them, listen to those questions again. You're gonna get written curriculums.
You're getting private cohort Discords. You're even gonna have a certification by the end of this. That's from my official company, Aduba, 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. You're gonna 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 wanna put my money where my mouth is and work with you. And here's the thing that no one else is doing. I'm gonna guarantee you that you walk away with a working product, a working company or process.
Not a just a certificate to hang on the wall, not a course completion badge, not a, oh, I hope you learn and utilize this. A real functioning thing that has outcome and output that you built during the program.
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.
We're looking at scholarships, getting people in for free, all of that stuff. We're trying to open up a waitlist right now. That's all we're doing, and I need your help just to get in.
Right now, we're just opening the waitlist for public enroll. If you join the waitlist, you're gonna be the first to see any sort of pricing updates. You're gonna be the first to see start dates.
You're gonna be able to give feedback on when and how you wanna join, and you're gonna 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 a 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 waitlist 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 wanna 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.
I have a working methodology in my hand. You have working ideas in your head. 30,000 people are using my methodology, 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 gonna tell you it's useless.
You can listen to them, or you can come build with us. We'll build together.
Folders over agents, layers over libraries, methodologies over tools, and 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.
The Hook
The bait, then the rug-pull.
The opening line is an empathy mirror for every builder who has rebuilt their AI stack at least once: you feel behind because you are learning the wrong thing, not because you are slow. What follows is a 22-minute case that the right thing to learn has existed since the 1970s — it just does not make for a good demo reel.
Frameworks
Named ideas worth stealing.
11:00model
ICM Five Layers of Context
Identity
Routing
Stage Contracts
Reference
Artifacts
A five-layer hierarchy for organizing AI context in file systems. Identity defines what the AI is; Routing controls where information flows; Stage Contracts set expectations per step; Reference holds stable background material; Artifacts are working outputs.
Steal forAny multi-step AI workflow where context bleed or stale prompts are causing failures — structure your project folder around these five layers instead of building agent orchestration
CTA Breakdown
How they asked for the click.
VERBAL ASK
20:08product
“Join the waitlist now. There are only 300 seats total — 100 per cohort. When those seats are gone, they're gone.”
Scarcity-anchored waitlist CTA with no price revealed — smart pre-launch move. Closes with a Grace Hopper parallel to reframe the invitation as joining a movement, not buying a course.
Add Modern Creator as a preferred source and Google shows you more of our breakdowns in Search, Top Stories, and AI Overviews. It only changes what you see, and you can undo it in your Google settings anytime.
Add to Preferred SourcesOpens your Google source preferences with us pre-loaded. Tick the box and you're done.
A 90-minute Zoom webinar arguing that most AI projects don't fail because the model is weak — they fail because nobody organized the context, and a plain folder of markdown files beats the agent swarm almost every time.
A mastermind member's question about landing clients turns into Jake Van Clief's longest, most honest answer on how to go from zero to paid, and why buyers really open their wallets.
A creator prompts OpenAI's Codex into building a free, self-hosted dashboard that replaces a stack of SaaS tools for news, mentions, newsletters, and audience tracking.
A rapid-fire tour of seventeen ways to use Claude's design tool for real work, from rebuilding old branding to wireframing a product before writing any code.
Anthropic merged Claude's memory between chat and Cowork so both remember the same things, but the sync only works if Cowork runs in the cloud, not on your own machine.
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