Build & Sell with Claude Code (10+ Hour Course)

I'm about to take you from a complete

beginner to a pro cloud code user. Even

if you've never touched the tool before,

by the end of this video, you'll be able

to build automations, websites, apps,

[music] whatever you want. You'll even

have your very own AI executive

assistant. So, I have put a ton of time

into making sure that this course is as

comprehensive as possible, and I've laid

it out in the exact order that I would

have wanted to learn Cloud Code if I was

starting over. So, we've got 24

different chapters that are covered in

this course. Let's take a quick look.

I'm going to start off by telling you

guys about the shift in the Agentic AI

market and why you should be learning

Claude Code. I'm going to help you guys

get set up. We're going to go over the

cloud code operations. We're going to

talk about tokens and context when it

comes to just dealing with AI in

general. We're going to talk about

cloud.md. You're going to build your

first workflows. We're going to deploy

those automations so that they actually

can run 24/7. We'll talk about project

architecture, the built-in commands,

rag, building and deploying websites,

APIs, and MCPs. We'll take a look at the

Google CLI. I'll help you guys build

your very own executive assistant. Then

we're going to deep dive into skills,

sub agents, agent teams, browser

automations, permissions, context

management, GitHub work trees. We've got

some fun hacks for you guys and fun

things that you can do with cloud code.

And then finally talking about how you

can actually monetize this new

knowledge. So I don't want to waste any

time. Let's just get straight into the

course.

All right. All right. So, before I have

you guys open up Cloud Code and we start

getting our hands dirty, I just wanted

to sort of talk about the actual space

and what this shift means and why it's

so important. So, that's what we're

going to be covering in this section.

Check it out.

Aentic workflows are not just [music] a

trend. They're the future of the AI

industry. More and more companies are

making the shift to agentic workflows.

And this is just getting started because

it's estimated that the AI agentic

market is going from about $7 billion

this year to around 93 billion in the

next couple of years. So, I can tell you

right now that knowing how to build aic

workflows is going to be one of the most

valuable skills that you can have. So,

in this video, I'm going to break down

why you should be building aic workflows

and then I'm going to actually build one

live in front of you so you can see

exactly how it works. And by the end,

I'll show you how to actually sell these

if you want to make some money with your

skills. So, let's get into it. So,

before we build anything, I want to show

you why this all matters because it's

not just hype. This is real money moving

into real technology. Right now, the

Aentic AI market is sitting at around $8

billion. By 2030, that's expected to hit

40 to 50 billion. That's not just a

small jump. That's an entire industry

being built in front of our eyes. And

it's not just projections. About 25% of

enterprises are already deploying

Agentic pilots this year. And by 2027,

that number will jump to 50%. So half of

major companies will be running some

version of Agentic Workflows within the

next 2 years. And with that comes

massive budget allocations, new security

requirements, and a ton of new

opportunities for people who know how to

build these systems. So why is this

happening now? What's driving the shift?

It comes down to pretty much one thing

which is companies are starting to hit

that ceiling of what traditional

automation can do and they're starting

to realize they could move a lot faster

with more agentic workflows. If you've

been building workflows in tools like

end to end or Zapier, you know the

drill. You map out every step. You

connect the different nodes or blocks.

You handle the edge cases yourself and

it works until it breaks because

traditional workflows will break when

they hit something unexpected. And when

that happens, someone has to usually go

in manually and fix that. And that's

maintenance. That's time. That's

ultimately money. Now, I do want to be

real with you here because there's a lot

of noise online about a dentic workflows

that makes it sound like they're just

completely magic and they fix themselves

forever. And that is partially true, but

only in a specific context, at least

[music] right now. Cuz when you're

actively working in a tool like Claude

Code and you trigger a workflow yourself

and say, "Hey, go research these

competitors and build me a report." The

agent is sitting right there with you.

So, if something breaks, the agent can

catch it mid-run. It can adjust its

approach. It can update its tools and

keep going. That self-healing piece is

very, very real and it's incredibly

powerful while you're building and while

you're iterating. But once you deploy

that workflow to run on its own on a

schedule or triggered by a web hook or

something like that, that is when you're

deploying the code, you're deploying the

tools, not the actual agent itself. So

if you've seen my previous videos where

we've used the WAT framework, we are

basically deploying the W workflows and

the T tools, but not the A agent. But

I'll cover this more in depth later

during the live build if you're

confused. But what this means is that

the self-healing ability ultimately goes

away when the code is up in the cloud,

you know, running automatically. And at

that point, it does behave more like a

traditional automation. But that's

really a good thing because automations

are predictable. They're deterministic.

And those types are the best ones. So

then where's the real advantage? Really,

it sits in how you build. Traditional

automation is like building a train

track by hand. You're laying every rail,

every switch, every connection all by

yourself. Whereas with aentic workflows,

it's like you're just telling a

construction crew, "Hey, I need you to

build a train track from here to there."

And then they build it for you. Meaning,

if they hit a problem during

construction, they would figure it out.

So you end up with a better train track.

It's built faster with fewer mistakes

because the agent handled the edge cases

during the build process that you might

have missed or not thought of. And then

the idea is you battle test it before

you ever actually deploy it. So then you

have a lot of confidence that it will

always work. So in our train analogy,

before we deploy that train track, we

would have like 10 different types of

trains test drive on it. They would be

different weights, different lengths,

and maybe different wheels. And we'd

want to make sure that our track can

work for all different types of trains

before we deploy it. And the reason this

is actually possible now is because the

technology has finally caught up. LM

have gotten really reliable enough to

use in production and we're not just

playing around with chatbots anymore.

These models can reason, they can make

decisions and they can execute

multi-step tasks with real consistency.

On top of that, we've got things to use

like skills or MCP or aa. We've also got

infrastructure like trigger.dev, modal

or versell that make deployment way

simpler than it used to be. And most

importantly, we've got tools like cloud

code that make all of this accessible to

non-developers. So, we can see that the

market is absolutely shifting towards

aic systems and the numbers back it up.

But here's a question that's probably on

your mind. Does this mean everything

that I've learned about Naden or