I've Made 1,800 Tutorials. Most of Them Are Already Dead.

I’ve been making tutorials for 10+ years. I started by teaching Adobe Photoshop, Premiere, After Effects, Audition and podcasting, and for the last couple of years, AI tools: Claude Code, n8n, VPS servers, RAG, agents. My tutorials have helped a lot of people over the years. Even though some of them make me cringe now, I’m still proud of them.

The problem is, they don’t last.

That’s always been true with software tutorials, but AI has put it on fast-forward. A menu changes. An API updates. A model gets replaced. A tutorial that was completely accurate when I published it can be outdated within weeks. Then the comments start: “This doesn’t work anymore.” They’re right. It doesn’t.

And that’s the bigger problem: the tutorial model itself is broken, and AI broke it for good.

No human can re-record fast enough to keep pace with tools that change weekly.
Creators burn out on the AI treadmill, while the person just trying to build something wastes a weekend stitching together half-working advice from five different videos, each outdated in its own special way.

Making tutorials isn’t just something I do. I’ve spent more than a decade thinking about how to teach things better, what makes something click, what makes a tutorial useful, and what makes it worth someone’s time.

So I started asking a different question:

What would it look like to completely rethink the AI tutorial-making process?

Agent Evergreen is my answer.

So here’s what I’m doing instead

I’m building Agent Evergreen: a living library where the unit isn’t a video or course. It’s a working system you own forever and is designed to stay current as the tools change.

Every system in the library ships complete with:

A member-owned working repo — the actual code, workflows, configs, and prompts. Make an independent copy in your own account and run it.
A build guide — written the way I teach: clear, concise, no-fluff, plain English, the why included.
A walkthrough video — short video from me. Some habits are worth keeping.
Adaptation notes — “but my stack is different,” answered honestly.
A public changelog and a dated stamp: Verified · tool + version · date.

That last one is the whole ballgame. Here’s the promise no tutorial has ever made you:

When the tools change, the system gets maintained — and you can see exactly when it was last verified. Every active system is re-checked monthly. If a supported dependency breaks it, I publish a fix, workaround, or clear status within seven days. The stamp is public. If something’s stale, you’ll know and so will everyone else, which means I can’t hide from it.

How one person can possibly promise that

Because I’m not doing it alone and this is the part I find genuinely exciting.

I run a small fleet of AI agents.
They watch the release notes of every tool the library depends on.
They flag what broke.
They draft the updates, the guides, the diagrams.
They handle what used to be the treadmill: the re-recording, the reformatting, the “what changed in version 2.4” grind.

This matters — the agents draft. I decide.

Every guide gets my edit.
Every system gets me on camera and the microphone.
Every stamp is me, personally, having verified the thing runs.
I’ve watched the internet fill up with AI-generated content nobody checked.
That is exactly what this isn’t.
The agents do the grind so I can spend my time where a human actually matters: building real systems, making judgment calls, and teaching.

I call this approach Programmatic Knowledge. I build once. The agents keep it alive.

I’m building it in public, starting now

Over the next 30 days, I’m building the first system in the library live, in public: The Content Factory. This is the exact agent-run pipeline that operates my entire business. The capture habits, the agent fleet, the automation that turns raw work into published tutorials and newsletters.

September 3 update: Building the real pipeline exposed a more useful center for System 001: Build Your Own Content Agent Fleet. I’m still opening up my actual Evergreen implementation, but the product will teach you how to build an understandable agent team around your own creator business—not require you to copy my Directus, Astro, Beehiiv, Stripe, or VPS stack. That infrastructure is the case study. The transferable agent method is the product.

It’s the most honest first product I can think of, because it’s the machine this whole thing runs on. You’ll watch me build it here and in AI Unplugged every Friday. Then on Friday, September 11, it becomes the first system in the library — and Agent Evergreen opens.

The Founding 100

When it opens, the first 100 members get founding terms:

$149/year, locked for life.
Every system, every future system, and a meaningful library release every month — a new system or a substantial expansion
The freshness guarantee
Vote on what gets built next.
Every system publishes its own conservative time-saved estimate. If Agent Evergreen isn’t worth the founding price to you during your first 30 days, I refund your year — one email, no friction.
Cancel whenever; member-owned working copies and downloaded releases remain yours under the usage license.

After 100 founding members, it goes up to $200/year. The founding price never changes for founding members. That’s the thank-you for backing this when the library is one system deep.

Want in? Join the founding waitlist → — waitlist members get first crack at the 100 spots on September 11.

Why I can make this promise

I own every part of this. The site, the content, the agents, the infrastructure — no algorithm decides whether you see my work, no platform takes a cut of your trust. I love YouTube and I’m not leaving it. But this library answers to exactly one group of people: the members.

I’ve spent 10+ years teaching people that this stuff is learnable. This is the next version of that promise: not “watch me do it”… “here, it’s yours, it works, and it’ll keep working.”

Let’s build. — Mike

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