Claude Code for Non-Coders (6 Hour Course)

0:00In this course, I'm going to take you

0:01from a complete beginner to someone who

0:02is AI native and can build automations,

0:05AI agents, literally anything that you

0:07can describe, you're going to be able to

0:08build by the end of this course. These

0:10are the topics that I'm going to cover

0:11with you guys. So, feel free to skip

0:12around to whatever pies your interest,

0:14but I'm going to go through all of this

0:15in order, and I'm going to do real

0:17examples and step-by-step builds

0:18throughout. So, don't want to waste any

0:20time. Let's just jump straight in. So,

0:22this whole course is basically assuming

0:23that you know nothing and that you don't

0:24have a technical background, which if

0:26you guys don't know who I am, my name is

0:28Nate and I do not have a technical

0:29background. But, I've been able to do

0:31some pretty incredible things with AI in

0:32the past couple years. I've got multiple

0:34different businesses that are based on

0:35content, education, certifications,

0:37events, consulting, and all of that is

0:40powered by a small team who is really,

0:42really good at using AI. The whole idea

0:44is that one person can do the work that

0:46it used to take teams. And I felt that

0:49ROI myself. And if you guys haven't, by

0:50the end of this video, you will have a

0:52very clear path to feeling that ROI and

0:54having AI systems that help you do more

0:56and that will eventually become the

0:58baseline. So, it's great to be getting

0:59ahead of this stuff. Okay, so first

1:01things first, what is Claude Code?

1:03Because I know the word code probably

1:04intimidates a lot of people. If you've

1:06never coded before, you don't have to

1:08code. So, Anthropic, that's the company

1:10that is behind this ecosystem, right?

1:12Um, the cloud models, Claude Chat, that

1:15is probably what a lot of you guys are

1:16used to. So, there's basically these

1:18three products. The first one is Claude

1:20chat and that is where you can talk to

1:22Claude in the web or wherever you want

1:23to do it on your phone and you just talk

1:25to that large language model the AI

1:27model and you get an answer. So kind of

1:29like a a chatbot. Then we've got Cloud

1:31Co-work which is kind of like the next

1:33step up. Fun fact, Cloud Co-work the

1:35entire product was built using cloud

1:37code by a few engineers. It would have

1:39taken the team weeks and weeks and much

1:41larger team but they were able to ship

1:43this in like a week with a few

1:44engineers. So very very cool. And cloud

1:46co-work is much more for kind of like

1:48your knowledge workers, your managers.

1:50It's much more of a simple interface to

1:52do simple automations and stuff, but

1:54cloud code is just the most powerful.

1:57And the cool thing about cloud code is

1:58like I said, you don't need to know how

2:00to code at all. And so I really don't

2:02ever use cloud co-work even though you

2:04could argue a lot of the stuff that I do

2:06in cloud code could be used in co-work.

2:08code is just way more powerful and it's

2:09where I see the future as far as

2:11thinking about building your own agents

2:13and just having things that are a lot

2:14more agentic for you. So those are kind

2:17of the three products, but what's cool

2:18about it is they're not that different

2:20to actually use. So if you learn one

2:22really well, you're going to be able to

2:23transfer those skills wherever you go.

2:24So don't feel like you're locking in by

2:26choosing one of these products. That's

2:28why I choose the most powerful product.

2:30So if I open the Claude desktop app,

2:32what do we see right here? This is

2:33Claude chat. You know, I can come in

2:34here and I can say, "Hi, Claude." and it

2:37can kind of understand a little bit

2:38about me. It can see my preferences.

2:40Obviously, it knows my name, but this

2:42isn't where you're going to get real

2:44work done. This is where people are kind

2:46of treating this as like a Google search

2:47engine or they're connecting their Gmail

2:49and helping it, you know, having it help

2:51them write email drafts and stuff like

2:52that, but it's not true agentic power.

2:55And this is Claude Code. What do you

2:57see? It's a very similar interface. You

2:59just talk to it in chat, but now what

3:00happens is it's able to access your

3:02local files. As you can see here, I can

3:04choose a folder to actually work within,

3:06whether that's my desktop or my

3:07downloads or my Herk 2, which is kind of

3:09my AI operating system, which is a term

3:11I might throw out a little bit today.

3:13Don't worry about it too much. At the

3:14end of this video, I'm going to show you

3:16a full course around how to actually

3:17build your own AI operating system, but

3:19this comes first. The whole point here

3:21is that Claude Code is able to work

3:24inside of local files as well as touch

3:26anything out there online. It can touch

3:28your Gmail, your Slack, your CRM. It can

3:30touch anything online, but it can also

3:32touch your local files, which is really,

3:33really important. And the other cool

3:35thing to note is that when you're in

3:36cloud chat, you know how you guys

3:38probably are aware of models like Opus

3:40or Sonnet regardless of the model or

3:42even Fable. Now, Claude Code is powered

3:44by all of those same models. So inside

3:46of cloud code, you still use sonnet or

3:48haiku or opus or fable, but the code

3:51aspect is basically just enthropic

3:53saying, "Hey, because you're working

3:55with local files, here are some other

3:57things you can do like web searches and

3:58web fetches and searching through local

4:00files that make you a little bit more

4:01powerful than cloud chat." So

4:03fundamentally, it's not that different

4:05from cloud chat. I don't ever use cloud

4:07chat anymore. Everything that I would

4:08typically do in Cloud Chat, I'm doing in

4:10Cloud Code because Claude Code knows

4:13everything about me, my business. It can

4:15see all my YouTube videos. It can see

4:17all of my Slack threads. It can see

4:18everything that I'm doing, which makes

4:20this feel way more like an employee

4:22rather than just like Claude chat, which

4:24kind of feels like a virtual assistant,

4:25right? I'm not reexplaining myself to

4:27Claude Code because it knows everything

4:29about me. So, you're probably going to

4:31hear the term harness, magenta harness,

4:33AI harness. That's what Claude code is.

4:36And this is kind of the way that I think

4:37about how you build with AI. This three

4:40sort of layer circle. At the core, we

4:42have the AI model. So, Opus 4.8 or Fable

4:45or GPT, whatever model you're using,

4:46that's at the core. Then around that, we

4:48have the AI harness. So, cloud code is a

4:51harness that uses AI opus or other cloud

4:55models to power it. And then on top of

4:57that harness, you have you. So, your

5:00brain, your prompts, your context, your

5:02data, your business. That's how it

5:04works. So if we make this a little bit

5:06more practical with analogy here, we've

5:08got the model here. We've got a car and

5:10then we've got you. So opus is a model,

5:13right? But without this is basically the

5:15engine. So we put this inside. If I can

5:18make the layering right, we put this

5:20basically inside of the car. Wherever

5:22you think the engine in this car might

5:24be, let's just say it's here. Then this

5:27is still not going to really be that

5:28useful. It's not going to be able to

5:29drive or go anywhere without the human.

5:31So the human has to come in here and

5:33drive the car. So the human is steering

5:35the harness and the harness is powered

5:36by the AM model. That might make no

5:38sense to you at this point. That's okay.

5:40I just wanted to sort of lay out these

5:42terms because you will hear these terms

5:44the more you get into AI. So just a

5:46simple way to think about it because as

5:48you continue to evolve and as the tools

5:50continue to evolve, you might switch out

5:52the model. So maybe you'll be using

5:53cloud code, but eventually you might

5:54switch this out for a different model or

5:56maybe you'll be using the model, but

5:58you'll switch out a different harness.

5:59Maybe you want to get in a different car

6:00and just drive it in a bit of a

6:02different way. So they're

6:03interchangeable but at the core this is

6:05what we're looking at AI harness and you

6:08and really the most important thing in

6:09this whole picture is you. If you are

6:13not giving it the right context then

6:14it's not going to be able to do the

6:16right stuff for you. So that's what it

6:17looks like. Okay. So that is what cloud

6:19code is. Now before we start getting

6:20into some of the technical stuff of

6:22getting set up and you know building

6:23stuff I wanted to talk about the mindset

6:25because the mindset around this stuff is

6:27so important for many reasons. I think

6:29the first one is that you know the space

6:30changes so fast. new models, new tools,

6:33new headlines. The space changes fast.

6:35So, how do you learn things in a way

6:37where it's not outdated next week? And

6:39that's what I want to teach you guys

6:40today. That's so important, in fact,

6:42that I actually wrote a book about this.

6:44It's called Becoming AI Native. And in

6:46this book, I go over kind of like

6:48throughout the chapters, these 12 big

6:49mindset shifts. And so, throughout this

6:52course, I'll probably relate back to

6:53some of these mindset shifts a little

6:54bit. But, I wanted to talk a little bit

6:56about mindset before we jump into the

6:57weeds of everything today. But before we

6:59hop into those mindset shifts, I want to

7:01talk about real quick with you guys. Six

7:03AI skills that I think every single

7:04person needs to master just to basically

7:06future proof their careers. AI is real

7:08and it's not going away. Just like

7:10social media replace newspapers and

7:12billboards and Netflix replace cable, AI

7:14will change and replace millions of

7:15jobs, including yours, unless you learn

7:17six important skills. These six AI

7:19skills will futureproof your career so

7:21that you don't have to try and start a

7:22business if you don't want to or switch

7:23careers if you don't want to. So, these

7:25six skills are going to apply no matter

7:26what job title you hold or whatever

7:28career you're pursuing. And I can

7:29guarantee you the last skill in this

7:31video will surprise you because it's the

7:32most unique skill, but it does work. So,

7:34let's just get started with skill number

7:36one, the AI person. So, becoming the AI

7:38person. I know that that sounds like

7:39super obvious because this is an AI

7:41video, but I think that a lot of people

7:43misunderstand what that actually means.

7:45If you're watching this, you probably

7:46feel like you're on the beginning side

7:47of AI. And honestly, you probably are

7:49because there's so much happening right

7:51now. There's always new models. There's

7:52always new tools. There's always new

7:53agents and workflows, new benchmarks,

7:55all of this kind of stuff coming out

7:56every single week. But I bet if you went

7:58and you talked to your friends or your

8:00family or just a lot of the people that

8:01you work with, they probably already

8:03think of you as an AI person. Or if they

8:05don't, you want them to. And that's the

8:07point. Being the AI person is relative.

8:09It doesn't mean that you're the best AI

8:11engineer in the world or that you

8:12understand every single AI model and the

8:14architecture behind it. You guys

8:15probably think of me as an AI person,

8:17but in reality, I feel overwhelmed every

8:18single day by how much I still don't

8:20know. But just remind yourself it's all

8:22relative. It just means that inside of

8:24your circle you know more than the other

8:26people and that matters way more than

8:27you might think. I've seen this happen

8:28over and over in my communities. People

8:30start picking up AI almost like a hobby.

8:32So they're playing with Claude, maybe

8:33they're testing out codecs, maybe

8:34they're messing around with Google V3,

8:36building little tools or little AI side

8:38projects, automating parts of their job,

8:39just running a bunch of experiments. And

8:41then what happens is they start showing

8:42people and that's usually all it takes.

8:44They show someone at work, hey, look at

8:46this thing I built this weekend. Or hey,

8:47I used Claw to clean up this process. or

8:49hey, I figured out a way to make this

8:50task, which normally takes me three

8:52hours, only take me 20 minutes because

8:53of AI. And all of a sudden, that person

8:55becomes known as the AI person. And the

8:57reason this is so powerful is because

8:59companies are about to have a ton of

9:01like AI moments. They're going to get

9:02access to a new model or they're going

9:04to spin up a new internal product or

9:05maybe they're going to want to build a

9:07little AI task force internally. And

9:08when that happens, someone's going to

9:10say, "We need someone to lead this." You

9:11want someone in the meeting to say,

9:13"Actually, I know someone who's really

9:14into this stuff." Or something like, "We

9:16should ask Nate. you know, he's told me

9:17that he's been playing around with all

9:18these AI tools. That's how these

9:19opportunities open up before there's a

9:21formal job title. And that's how people

9:22get pulled into better projects. That's

9:24how people become more valuable without

9:25quitting their job and starting from

9:27zero. And the data backs this up. So

9:29IBM's 2026 CEO study found that 85% of

9:32CEOs said that all functional leaders

9:34have to become technology experts in

9:36their own domain. Not just the CTO, not

9:38just the engineers, not just the IT

9:40team, everybody. So whether you're in

9:41marketing, sales, finance, ops, legal,

9:43customer success, whatever you're in,

9:45this applies to you. And the thing to

9:46remember here is that it's not just in

9:48one department. It's not like cyber

9:50security where you kind of have like a

9:51new team and then you're done. It's

9:52going to seep into every single role and

9:54every vertical whether you like it or

9:56not. And I know some of you are probably

9:57thinking, "Okay, but my role doesn't

9:58really need AI and that's your mistake.

10:00Your role will need AI because every

10:02single role will." So instead of trying

10:04to switch careers or learn a completely

10:06new job, just take a look at your role.

10:08Take a look at what you already do and

10:09ask yourself, how do I become faster and

10:12better and more useful at this specific

10:14task and this specific role with AI? So,

10:16I mean, think about it. If you were an

10:17accountant when Excel came out and you

10:18basically just said, you know what? I'm

10:20good. I like the way I do this. I'm

10:21going to keep doing all of this on paper

10:23and with a calculator, you were done.

10:24Like, that was probably your last day at

10:26that company, if you said something like

10:27that, if you made kind of like that

10:29stubborn statement. The people who

10:30learned Excel first were just faster.

10:32They got through spreadsheets in a

10:33fraction of the time that it used to

10:34take them. Let's just say from two

10:36spreadsheets a week to 10. That's just

10:37random numbers, right? But that new

10:39level of output became the baseline. And

10:41AI is like that, but it's a lot bigger.

10:42Cuz right now, being the AI person feels

10:44like an edge, but in a few years, it's

10:46just going to be the new normal. So, the

10:47advantage isn't waiting until someone's

10:49forced to learn it. The advantage is

10:50becoming the new normal. While most

10:52people still think it's optional. So,

10:53practically, how do you actually do

10:54this? Well, I would say you pick one

10:56main AI tool and actually get pretty

10:58good with it. So, as of May 2026,

11:00recording this video, for me, that's

11:01Claude. And I use Claude for my general

11:03knowledge work and to build automations.

11:05But the exact tool isn't the point. The

11:06point is, you need one tool that you're

11:08not just messing around with. You're

11:09using a tool to actually deliver some

11:11sort of ROI. And then you can pick one

11:13workflow in your current job. You can

11:14take something that you already do every

11:16week. And then figure out how you can

11:17use AI to make it better or faster.

11:18Document what changed, how long did it

11:20take before, how long after, what got

11:22better, what still needed human

11:23judgment. Now, obviously, be smart.

11:25Like, don't expose company data or break

11:26any regulations. Don't try to automate

11:28something at work without getting like

11:30permission about it. And if your company

11:31has guidelines and you can't use Claude,

11:32then that's actually another opportunity

11:34for you to look at because you can go

11:36dig into what those regulations are and

11:37you can figure out which tools you

11:39actually could use. And that alone shows

11:40that you care. But I just want you guys

11:42to remember that you don't need to

11:43change careers. You can just find the AI

11:45native version of the career that you

11:46already have. So that's skill number

11:48one, become the AI person. But skill

11:50number two is where a lot of people are

11:51going to mess this whole thing up

11:53because the more you use AI, the more

11:54tempting it gets to just trust the

11:56output and say like that's good enough.

11:57And that's where skill number two comes

11:58in, which is taste and judgment. As AI

12:01gets better, it gets easier and easier

12:02to just trust the first thing that it

12:04gives you. And I saw this joke the other

12:05day that was on one side, we had a

12:07person with a bullet point and they were

12:09using AI to turn that bullet point into

12:11like a super professional structured

12:12email to send to the team. And then on

12:14the other side, we had that team and

12:15they were using AI to turn that

12:17structured email into just one bullet

12:19point. Now, it's funny, but it does also

12:20kind of create the perfect image of

12:22where work is going if we're not

12:24careful. You know, everyone's

12:24transforming things, but is everyone

12:26reading it? And that's dangerous because

12:28when you first start using AI, you

12:29review everything, right? Like you read

12:31every word, you double check the claims,

12:32you make sure it sounds like you, but

12:34the outputs start getting pretty good

12:35and then you get more comfortable with

12:36it and you just kind of let your guard

12:38down and that's the trap. And sometimes

12:39the giveaway is really small like m

12:41dashes. You know, AI is notorious for

12:43putting m dashes in everything because

12:44it's been, you know, trained on so many

12:46white papers and formal documents. And

12:48so like for me, I've basically never

12:50manually written or typed an M dash in

12:52my entire life. So if something goes out

12:54for me with five m dashes in it, people

12:55who know me are going to look at that

12:57and be like, "Okay, Nate obviously

12:58didn't write this. This is AI." And the

12:59problem is the second they think that it

13:01changes the way that they interpret that

13:03entire message. They start wondering,

13:04"Did this person actually read this? Is

13:06any of this true? How much of this is

13:07actually them?" And that's where taste

13:08comes in. And just to be clear, I'm not

13:10saying that I don't use AI to write or

13:11that you shouldn't. I think everyone

13:12should. It's just about taste. But

13:14anyways, this issue comes up for me all

13:15the time with video. So AI helps me make

13:17motion graphics that are honestly way

13:19better than what I could do by hand.

13:21They're way faster. They're cleaner,

13:22they're more polished, but it doesn't

13:23always get right where the motion

13:25graphics should come in or how long they

13:27should stay on screen or the visuals and

13:29like what's explaining and if it's

13:30distracting or if it's helpful. And that

13:32is still my job to watch the whole video

13:33back and give feedback. And that's going

13:35to be true in every field. AI can write

13:36the sales email. You still need to know

13:38if it'll annoy the prospect. AI can

13:39draft you the HR memo, but you still

13:41need to know if it'll make the employees

13:42feel weird. So, how do you actually

13:43build this skill? First, you should

13:45study the best work in your field. If

13:46you're in sales, study great sales

13:48emails. If you're in marketing, study

13:49great landing pages. Next, start saving

13:51examples. So, build a library of stuff

13:53that you actually like and stuff that

13:54sounds like you. And when something's

13:55good, don't just say, "Cool, that's

13:56good." And just copy and paste it

13:58somewhere else. Ask it why. Ask what

13:59made it good and ask what makes it clear

14:01and ask what makes it trustworthy and

14:03tell it why you think it's good and tell

14:04it what you like about it. Third, every

14:06time you correct AI, you feed that

14:08correction back into the system. So, the

14:09feedback loop is for good things, but

14:11also for bad things. If AI writes

14:12something and you change five things,

14:14say, "Hey, here are five things that I

14:16changed. Here's why. Update your

14:17instructions so that next time it's

14:18closer." And that's how you actually

14:20train the system to better understand

14:21your taste. Because at the end of the

14:23day, AI can generate the work. Taste is

14:24deciding what deserves your name.

14:26Because remember, if you produce

14:27something with AI, your name is signed

14:28to it. Whether that is something that's

14:30really, really good and the whole team

14:31loves it, you will get credit for it. Or

14:33if it's something that's bad, you will

14:35take the blame. It doesn't matter if AI

14:36wrote it, doesn't matter if you wrote it

14:37for that piece of work because your name

14:39is assigned to it. So now the question

14:40is, how do you actually get AI to

14:42produce better work in the first place?

14:43Because if you're just typing prompts

14:44and just crossing your fingers, then

14:46you're leaving a lot on the table. So

14:47that's skill number three and it's

14:48something that you can apply the second

14:49you close out of this video. And that

14:50skill is becoming a context engineer. So

14:52you might have heard the term prompt

14:54engineering. This was a huge thing a

14:55couple of years ago. The whole idea of

14:56prompt engineering was that if you

14:58wanted a better output from an AI model,

14:59you had to give it a good prompt. You

15:01had to give it a role, clear

15:02instructions. You had to tell it the end

15:03state. You had to give it examples. You

15:05had to tell it what to do and what

15:06explicitly not to do. But prompt

15:08engineering is getting less important

15:09over time because the models are just

15:11getting so much better on their own.

15:13Even Andre Karpathy, who's one of the

15:14goats of AI and actually just joined

15:16Enthropic, called context engineering

15:18the delicate art and science of filling

15:20the context window with just the right

15:22information. So translation in layman's

15:24terms, prompts are how you ask. Context

15:26is what your AI actually knows. In

15:28context engineering is way more durable

15:29than prompting because no matter how

15:31good the models get, they still need to

15:32know what's actually in your brain. So

15:34what's going on in your business, what's

15:35on your calendar, what your priorities

15:36are, stuff like that. So here's my

15:38personal example. I've built what I call

15:39my AI operating system or my AI OS. And

15:42basically, it has pretty much all the

15:43context that's in my head. It can see my

15:45meeting transcripts. It can see all my

15:46YouTube videos. It can read through my

15:48DMs and channels in ClickUp and Slack.

15:50It can pull my emails. It honestly knows

15:51what's going on in my world better than

15:53I do because it can recall everything

15:55instantly and perfectly. And I can't do

15:57that. So, it's kind of like this running

15:58joke that if someone couldn't get a hold

15:59of me, they should just message my AIOS

16:01and it would actually give them an

16:02answer that's better and faster than

16:04waiting for me to respond. And that's

16:05the point you want to get to where an AI

16:07has so much context about you that you

16:08can say something like that. So, how do

16:10you actually start that? Well, the

16:11simplest move, stop opening Claude or

16:13ChatGBT in a blank chat. Instead, spin

16:15up a custom GBT or spin up a Claude

16:17project and feed it real context from

16:18whatever you're working on. So, say

16:19you're running a marketing campaign for

16:21a new product launch. Don't just open up

16:22a fresh chat every time you need help

16:24with ideas. Spin up a project, drop in

16:26documents with your product details,

16:27your marketing calendar, add copy that's

16:29worked well in the past, add copy that's

16:30flopped in the past. Now, the AI is

16:32actually working with that context, not

16:34generic best practices. And the analogy

16:36I keep coming back to here is a summer

16:37intern. When a new intern shows up at

16:39the company, you have to sit them down

16:40and kind of onboard them, right? Like

16:42you have to explain what the business

16:43does, walk them through who's on the

16:45team and who does what. You have to tell

16:46them what current projects matter. And

16:47only after they have all that context

16:49can they actually contribute in a

16:50meaningful way. And AI is the exact

16:52same. And without the context, it's just

16:53a smart intern who's guessing. And

16:55remember, the context you're giving for

16:56the most part is data that's not

16:58publicly accessible. The context about

17:00your subject matter expertise, your

17:01brain, your IP, that's what makes the

17:03outputs unique. If everyone's using that

17:05same model and asking for the same

17:06things, then everyone's outputs will

17:07look the exact same. So your context is

17:09really, really important. So just

17:10remember, garbage in, garbage out. If

17:12you give your AI bad data and no

17:14context, then you're going to get a very

17:15generic output. So that's skill number

17:16three, become a context engineer. Now,

17:18skill number four is one of the most

17:19underrated skills on the entire list.

17:21And in the AI era, it might be the

17:23biggest separator between the people who

17:24win and the people who get left behind,

17:26and it's iteration speed. Now, if skill

17:28number two was about knowing what good

17:29looks like, this skill is about getting

17:31there as fast as possible. So, the two

17:33skills kind of work hand in hand, but

17:35this one stands on its own because in

17:36the era of AI, the people who iterate

17:38fastest are the ones who win. If you can

17:39move fast without sacrificing quality,

17:41you're just going to outperform

17:42everybody because every iteration is

17:43more data. Every iteration is a chance

17:45to learn what's working and what's not

17:46and a chance to make your skills and

17:48your agents and your prompts and your

17:49context, all of it better. The analogy I

17:51always go back to here is like you're

17:53teaching a kid to ride a bike. You can't

17:54just chuck a kid on a bike and say,

17:56"Have fun." And expect them to go ride a

17:58mile. That's not how it works. You would

17:59put them on the bike. You'd maybe put

18:00one hand on their back. You'd hold the

18:01handle and you'd start walking with

18:02them. You would feel if they were

18:04leaning left and correct them. You'd

18:05say, "Hey, you know, shift your weight

18:06over to the right a little bit. You're

18:07helping them calibrate. And after each

18:09run up and down the driveway, you

18:10continue to calibrate. You continue to

18:11iterate and adjust. And the more time

18:13that that kid spends on the bike with

18:14your guidance, the more that you can

18:16start to slowly let go and eventually

18:17you take off the training wheels and one

18:19day you give them a little push and they

18:21just ride and they're pedaling and they

18:22are doing great." And that's exactly how

18:24building with AI works. You very rarely

18:26can just oneshot something. You use the

18:28data and you feed it back in and you

18:29make it better. And the thing is once

18:31you've taught one kid to ride a bike,

18:32teaching the next kid is easier and

18:33teaching the third kid's easier. And by

18:34the time you're teaching your 15th kid

18:36how to ride a bike, you've pretty much

18:37got the process down to a science. And

18:39now obviously every use case is

18:40different, right? Like some agents are

18:41more complex than others and and they

18:43don't always get built the same. But the

18:44idea of your process in building agents

18:47gets better every time. So hopefully you

18:48guys get the point that I'm trying to

18:49make here. Remember earlier that little

18:51example I said of like let's say people

18:52are typically producing two spreadsheets

18:54a week and then after Excel they move

18:56that baseline up to 10. The faster you

18:58can iterate, the faster you're going to

18:59be able to produce things, which means

19:00your new baseline is going to be higher

19:02than everyone else's baseline as far as

19:04like units of output. So how do you

19:05actually train yourself to be able to

19:07iterate and move faster? This part may

19:08sound silly, but the first thing I think

19:10is to master keyboard shortcuts. Stop

19:12using your mouse for every little thing

19:13and honestly stop typing everything,

19:15right? Like use voice input. It's way

19:16faster than typing. And we actually have

19:17a voiceto text tool called Glido that I

19:19use literally every day. So if you want

19:20to check it out, links in the

19:21description. But the bigger move is

19:23rapid prototyping. Don't sit there

19:24trying to plan the perfect version. Just

19:26build the ugly version fast. See what

19:28breaks, fix it, and iterate. That's the

19:29whole idea of getting out a P or a proof

19:31of concept. Now, there's another half to

19:33this skill that's just as important,

19:34which is knowing when to stop iterating.

19:36Because when you're building AI tools,

19:38it can feel like there's no such thing

19:39as a finished product. I've been there.

19:41There's always a nice to have. There's

19:42always one more feature you could add.

19:43So what you have to do is give yourself

19:45a north star. You have to tie one

19:46automation to one very specific business

19:49metric and you have to define what done

19:51is. You have to define what done looks

19:52like before you even start building. So

19:54if it's a customer support automation,

19:55tickets resolved per day. If it's a

19:57sales automation, maybe it's qualified

19:58appointments set per week. If it's an

20:00ops automation, maybe it's refund

20:01percentage going down by X%. So pick the

20:03metric, build until you hit it, and once

20:05you hit it, move into maintenance mode.

20:07Obviously, over time, you can probably

20:08find ways to improve it and maybe

20:10improve the metrics even more, but the

20:11heavy lifting is done. So whether you're

20:12building automations for yourself or for

20:14a client, a clear definition of done is

20:16what keeps you from scope creeping on

20:17yourself. And that's skill number four,

20:18iteration speed. Now skill number five

20:20is going to feel a little bit different

20:21and it's inspired by Iron Man. So this

20:23skill is building your own Jarvis. So

20:25you guys have seen Iron Man, right? Tony

20:27Stark doesn't sit at his computer typing

20:28prompts into Jarvis all day. Jarvis is

20:30already always there. He runs in the

20:31background and he notices things and he

20:33pings Tony when something needs

20:34attention. He'll even kick off tasks

20:36before Tony even asks. Now, this is

20:38different from skill 3. Context

20:39engineering was about teaching your AI

20:41what you know. Skill number five is

20:42about teaching your AI to act on what it

20:44knows without you having to be the

20:46trigger. So here's the way I think about

20:47this. Imagine you build an automation

20:48that only runs when you explicitly fire

20:51it off. That's great. It's going to make

20:52you a lot more productive. But if you're

20:54not around to trigger it, nothing

20:55happens. Now imagine you build a system

20:57that fires on its own. While you're in a

20:59meeting, while you're on a walk, while

21:00you're taking a nap on the beach, that's

21:02real leverage. So the move here is to do

21:04an audit of your day. What things do you

21:06do every week that get triggered by

21:07something predictable, meaning maybe a

21:09specific type of email coming in or

21:11every Monday morning or every Wednesday

21:12evening or every time a new lead lands

21:14in your CRM? Every one of those triggers

21:16is something that you can actually hand

21:18to a system and tell it to do X, Y, and

21:19Z when A or B happens. But here's the

21:22catch. The second you take yourself out

21:23of the loop, the risk obviously goes up

21:25because you're not sitting there

21:26watching it and making sure nothing goes

21:27wrong. There's no catching the mistake

21:29before it reaches a customer or pulls

21:31the wrong data or sends the wrong

21:32message to the wrong list. So the moment

21:34you remove yourself from the process,

21:35the system has to be pretty airtight and

21:37pretty battle tested. Which is exactly

21:39why a lot of people screw this up. The

21:40second they hear Jarvis or an always

21:42personal AI assistant, their brain jumps

21:44straight to building an AI agent for

21:46basically every function. Whether that's

21:47a new email or an end of week report. A

21:49lot of people just jump straight to an

21:51AI agent. So the real skill here is

21:52knowing when something needs an AI agent

21:54versus when it just needs a simple

21:56workflow that doesn't even use AI at

21:57all. So I think about this like a

21:58vending machine versus a slot machine. A

22:00vending machine is deterministic. You

22:02put in a quarter, you hit E4, you get a

22:03Coke. Same input, same output every

22:05single time. A slot machine is

22:06nondeterministic. You pull the lever.

22:08Sometimes you win, sometimes you lose,

22:09sometimes nothing. So AI agents are slot

22:11machines. And essentially, like every

22:13time you talk to an AI, it's almost like

22:14you're gambling. Like not really when

22:16you put the right harness and context in

22:17place, but you never know what's going

22:18to come out the other side. Agents are

22:20really powerful when you need deep

22:21reasoning and you need, you know,

22:23variability, but they cost more. They

22:24fail more often in unexpected ways. So

22:27they introduce more risk. But if you

22:28have a simple, you know, if this, then

22:29that, that's a workflow. And that's just

22:31a vending machine. Predictable, it's

22:32really cheap, and it doesn't break. So

22:34if your task is something like every

22:35morning at 9:00 a.m., pull last week's

22:37revenue from Stripe and post that in

22:38Slack, that does not need an agent. A

22:40simple workflow could do that in 5

22:41minutes and basically never fail. But if

22:43your task was something like read these

22:44incoming customer emails and understand

22:45what they actually want and draft a

22:47tailored response, now you need some AI

22:49in there because the input is messy and

22:51there's reasoning and you have to

22:52generate some sort of content. And

22:53honestly, this is the elite version of

22:55being the AI person that we talked about

22:57at the start. Because in a world where

22:58everyone is shouting AI, AI, AI, the

23:00person who can actually step back and

23:02say, "Hey, we don't actually need AI

23:03here. We can solve this cheaper, faster,

23:05and with way less risk." That person

23:07stands out way more than the one who's

23:09cramming AI into every single task. So

23:11being the person that has that take

23:12signals that you actually understand the

23:14business problem, not just the AI hype.

23:16When you're building your Jarvis, ask

23:17yourself two questions for every task

23:18that you want to automate. First one, do

23:20I actually need to be the one triggering

23:22this? Or can the system fire this off on

23:24its own? And second, does the step

23:26actually need AI or could a simple

23:27Python script or no code workflow do it

23:30at a fraction of the cost with less

23:31risk? And what you want to do is default

23:33to the simplest thing that gets the job

23:35done. Because the people who win in the

23:36AI era aren't the ones who are building

23:37the fanciest agents with hundreds of

23:39tools and hundreds of sub aents. They're

23:41the ones building systems that run

23:42quietly in the background, costing them

23:43almost nothing and doing real work

23:45whether they're there or not. So that's

23:47skill number five. But the final skill,

23:48I can guarantee is something that you've

23:50never heard of. At least not in this

23:51context. I'm talking about unemployment

23:53insurance. And no, I don't literally

23:54mean taking out insurance. Rather, I

23:56mean creating your own insurance. This

23:58might be a bit of a hot take. Not

23:59everyone's going to agree with me on

24:01this, but I'm bringing it up because I'm

24:02really confident this is going to become

24:04way more normal over the next few years.

24:06And the skill is building multiple

24:07income streams using AI so that no

24:09single employer or client can take you

24:11out. The old career model was basically

24:13like one job, one income, a 401k, maybe

24:15a few investments. But basically, all

24:17your eggs were in one basket. And if you

24:18got fired, you were kind of back on the

24:20hunt. You were polishing your resume,

24:21applying to 100 jobs, hoping somebody

24:23bit. And this new model that I'm talking

24:24about that I see emerging is job

24:26stacking. Your day job plus a couple of

24:28AI powered side income streams. I've

24:30already seen a ton of people running

24:31multiple remote jobs, you know,

24:32part-time gigs, side projects, and

24:34stacking that all to equal way more

24:36income than they'd ever make at just one

24:38full-time job. I'm not saying that every

24:39one of you guys should just go quit your

24:40full-time job and do this. I'm saying

24:42that it's already happening and it's

24:43about to become way more common because

24:45AI lets one person do work that used to

24:47take a team of five. Now, the thing I

24:48want to hammer home here, you don't have

24:50to stack five income streams in

24:52completely different domains. That's how

24:53people end up burnt out and broke. The

24:55better version is one passion with

24:57multiple branches. I'm a really, really

24:58strong believer that to be successful at

25:00anything in life, you have to enjoy it.

25:02You have to have at least some kind of

25:03passion for it. If you're chasing AI for

25:05the wrong reasons or you're going after

25:07something because someone said there's a

25:09lot of money in it, then people are

25:10going to be able to see right through

25:10that and it's going to be really hard to

25:12be successful. So, what I want you to

25:13take out of this is to figure out what

25:15motivates you. Figure out what you're

25:16actually passionate about. And that's

25:18where your north star comes from. I've

25:19got a few different income streams

25:20myself. And what's cool about it is that

25:22they all stem off of my same north star.

25:24Same theme, same expertise just packaged

25:26in a few different ways. For example,

25:27you've got your career, that's your

25:29foundation. your expertise about that

25:30career packaged into maybe a course or a

25:32niche newsletter or blog or microsass or

25:35maybe even some consulting on the side.

25:36It's the same domain, but it just takes

25:37different shapes and that's how you

25:39avoid the biggest trap with this whole

25:40idea which is distraction. When you're

25:42starting, just pick one and go hard

25:43until you have momentum under you and

25:45then you can sort of branch out. Now, a

25:46couple quick caveats to mention here is

25:48once again be smart, check your

25:49employment contract, watch out for

25:50non-competes, disclose whatever you're

25:52doing on the side if your company

25:53requires it, like don't do anything

25:54sketchy and don't burn your day job

25:56chasing the side thing and be safe. But

25:57how do you practically do this? Well,

25:59honestly, this really depends on who you

26:01are as a person, but if I had to give a

26:02default move, I would say building in

26:04public. Experiment with AI tools, build

26:06small things, and share what you're

26:06learning. Document the wins and losses.

26:08Build a tiny brand around the work

26:10you're already doing. Because the second

26:11you start posting, you become

26:12discoverable. That's how opportunities

26:14show up, clients show up, job offers

26:16show up, people want to work with the

26:17people actually doing the work. And this

26:19is also something interesting to think

26:20about. The world is shifting in a way

26:22where humans are using AI for almost

26:23everything, right? Which means when

26:25humans go to search the internet for

26:26something, they're probably going to do

26:28that through some sort of AI interface.

26:30Which means if you don't exist basically

26:32at all, somewhere where an AI can find

26:34you and find information about you, then

26:36it's going to be a lot tougher to be

26:37discovered. Now, if building in public

26:38isn't your thing, that's fine. You just

26:40have to find your own version. Maybe

26:41it's a quiet consulting practice, niche

26:43newsletter that doesn't require your

26:44face. Maybe it's a product that you

26:45build and sell without ever showing up

26:47on camera. Medium is completely up to

26:49you, but the point is you start building

26:50something that's actually yours. So,

26:52those are the six skills that I'd

26:53recommend learning and developing to

26:54futureproof yourself in the AI era. I'm

26:57a strong believer in adaptation and

26:58survival of the fittest. So, as long as

27:00you keep up with the changes and

27:01developments in the space that matter

27:03for your northstar, your ability to earn

27:05and live will always be protected. And

27:06now that we have discussed those six

27:07skills, keep those in mind as you work

27:09your way through the rest of the course

27:10and the rest of these mindset shifts.

27:12Everything loops back to those. So, now

27:14let's get back to those mindset shifts.

27:15The first thing is that AI native isn't

27:17what you know. It's not how much

27:19expertise you have or how many models

27:21you can name. It's about what your hand

27:23reaches for. So throughout this course,

27:25what I want you to think about is how

27:27would you do this manually? When you

27:29know that you need to respond to an

27:30email or that you need to analyze a

27:31report, what do you do? You probably

27:33open up the tab on your browser or maybe

27:35you even have it bookmarked and you're

27:37constantly switching between tabs and

27:39context switching. But being AI native

27:42means, okay, rather than doing this

27:44manually myself, let me default first to

27:47doing this with AI, doing this through

27:48cloud code, doing this through my other

27:50tools, using AI to do something like

27:52research and analysis before I give it

27:54my first pass. That's how someone

27:56becomes truly AI native and way more

27:58productive. I hardly ever leave cloud

28:01code. Most the day when I'm working, I'm

28:03working inside of this interface right

28:04here because it just makes me way more

28:06productive. And then the next one that I

28:07wanted to call out, and like I said,

28:08we'll probably revisit all these, is

28:10number four. Don't quit in the dip. The

28:12payoff is the climb. And here's what I

28:14mean by that. Let me just make a quick

28:16chart here. Whenever you decide to learn

28:19something new, there is typically a

28:21short-term cost that you have to bear.

28:22Whether that is because you're a bit

28:24overwhelmed or whether it's because you

28:25have to learn a new skill or set up a

28:27new system, whatever it is, there's

28:29usually a short-term cost you have to

28:30bear. And that discourages a lot of

28:32people. So let's say you start learning,

28:34you know, you start taking this course,

28:35you start getting a little overwhelmed.

28:37Do not quit. Do not click off the video.

28:39At least save it for later and come back

28:40to it because like I said, what happens

28:42is you start learning, right? And a lot

28:45of people expect that the learning is

28:47going to be linear like this. They

28:49expect this is how their progress is

28:50going to look. But typically what

28:52happens is their progress looks more

28:54like this and it becomes exponential.

28:57And so this gap right here, this is

29:00where people end up dropping out. they

29:02get overwhelmed and they quit here

29:03before they start to get all of the

29:05actual exponential benefits of learning

29:08the thing and implementing the new

29:10technology. And look at all this green

29:12that you're actually going to get when

29:13you keep learning and you keep building.

29:15Another way that I like to think about

29:16it is let's say on this range, right?

29:19The old way of doing something is maybe

29:22getting you results that are about here.

29:24And what happens is the dip that I'm

29:26talking about when you start to learn a

29:27new method. You may feel slower in that

29:29week and you may feel like you're doing

29:31it worse in that week because you're so

29:33used to the old manual way. But what

29:35happens is is that short-term dip, maybe

29:37you're dipping in 20% productivity. Is

29:40that dip worth, you know, maybe the 60%

29:44productivity that you ultimately will

29:46have? In most of the cases, the answer

29:48is yes. But this is where people drop

29:50off. And that's what I'm trying to

29:51prepare you guys for. Do not drop off in

29:53the dip. Do not drop off in this gap

29:55right here. And because I'm designing

29:57this course for knowledge workers,

29:58managers, regular people that don't have

30:00coding backgrounds. Think about it like

30:02this one overarching rule. You are just

30:05a manager. You're managing AI agents.

30:08What does that mean to me? Think about

30:09it like if you've ever managed an

30:11employee, which a lot of you guys

30:12probably have, but if you haven't, this

30:14is typically what it would look like.

30:15First of all, you get them onboarded.

30:17You let them get to know you. You get to

30:19know them. You let them get to know your

30:20business a little bit.

30:22You don't want to overwhelm them. You

30:23don't throw them 10 products on day one.

30:25You slowly phase them in until you start

30:27to feel a little bit more trust. But

30:29that doesn't mean you just let them run.

30:31Your job is to very, very clearly tell

30:33them, "Hey, this is what you need to do

30:35today. This is what good looks like.

30:36This is what bad looks like." And then

30:38when they finish their job and they give

30:40you something, you don't just pass it

30:41along or accept it as is. You review it.

30:44You look at it. You use your judgment.

30:46You use your taste. And then you say,

30:47"Hey, here's what you did good and

30:49here's what you did bad." Now take my

30:51feedback and iterate again and update

30:54your instructions. You know, remember

30:55that I told you this so that next time

30:57it's not bad and next time it's even

30:59better. And that doesn't mean it's going

31:01to be 100% on the first pass, but it

31:03means every single time that you get a

31:05deliverable from your AI agent, that's

31:06an opportunity to improve the system.

31:09And the cool thing is all of this

31:10improvement, all of this instructions,

31:12everything I'm explaining right now and

31:13everything for the rest of this course,

31:15it's just natural language. So if you

31:17can think clearly about what you want,

31:18which humans are pretty good at, and you

31:20can describe what you want pretty

31:21clearly, which once again, humans are

31:22pretty good at, then you will be good at

31:24managing AI agents. All right, so let's

31:26move on to number three, which is

31:27installing and signing in to cloud code.

31:31So you can just go ahead and Google

31:32cloud code install, right? You can click

31:34on the quick start docs, and then you

31:36can pretty much figure out right here

31:37how to get a cloud subscription because

31:39you do need a cloud subscription to use

31:40cloud code. And then how do you actually

31:42install it? And there's a few ways to do

31:44this. If you want to use one of these

31:45commands in your terminal to install

31:47cloud code on your device, that works

31:49fine. And by that I mean you would open

31:51up your command prompt or your

31:52PowerShell or, you know, whatever it is

31:54on your operating system and just run

31:56these commands. You could even have

31:58Cloud Chat help you out with this if

32:00you're having trouble for some reason.

32:01But it's super easy. And what I would

32:02recommend is getting the Claude Code

32:04desktop app or just cloud desktop app.

32:05So Google that, click on this right

32:07here, and then download for your

32:09operating system. So for me, I'm on

32:11Windows and I would download this, run

32:13the wizard and then when you open up

32:15Claude, it will look something like

32:16this. And you're just going to go ahead

32:17and get started. Now, this is where you

32:19are going to, like I said, have a paid

32:21plan for Claude Code. You can start on

32:23this pro plan. As you can see, you get

32:24Claude Code and then you can upgrade

32:26that later if you want to max the $100 a

32:29month plan or the $200 a month plan. I

32:31know that sounds expensive, but think

32:32about this. For 200 bucks a month, you

32:35can get basically a full AI employee,

32:36which is the cheapest employee you might

32:38ever get for the amount of work you can

32:40do. For reference, um, you know, a good

32:42project manager or a good software

32:43engineer could cost you way upwards of

32:45$100,000 a year, whereas this is only

32:47going to be 200 bucks a month, which is

32:49very cheap. So, start with Pro and

32:52upgrade later as you need. And once you

32:54have that account, then all you have to

32:56do is actually sign in on the Claude

32:58desktop app. All right. So, now that we

33:00are set up, let's talk about where to

33:01run Cloud Code. So, I just showed you

33:03guys how to use the cloud desktop app,

33:05right? You can come in here and we can

33:07talk to Claude chat right here. I can

33:09say something like hello or sorry, it's

33:11not cloud chat. This is cloud code and

33:12this is the cloud desktop app. So, I'm

33:15going to be using this throughout the

33:16course because I think the interface is

33:17super nice and we can manage all of our

33:19different projects and our chats on the

33:20lefth hand side. So, this is what I will

33:22be using today. However, I will say a

33:25lot of the times I do like to use this

33:26in VS Code. So, VS Code is just a simple

33:28IDE. It's completely free to download

33:30and it lets me use cloud code in the

33:31terminal like this or I can even use the

33:34uh cloud code extension which looks once

33:37again a little bit more userfriendly. So

33:39the point I'm trying to make here is

33:40there's a lot of different ways you can

33:41run it. Other YouTubers run it different

33:43ways, your friends might run it

33:44different ways, but under the hood it's

33:46basically all doing the exact same

33:47thing. So don't stress too much about

33:49where you use it. If later on in 2 weeks

33:51you want to switch to VS Code, you can

33:53do so and nothing will change. All of

33:55your files and projects will still be

33:56there. all of your sessions, all of your

33:58apps, whatever you built, it's still

33:59there. It's just the way that you

34:01actually interact with it. So, don't

34:02stress about it too much. Like I said in

34:04today's video, I am going to be using it

34:06right here with the Clawed Desktop app.

34:09Okay, so earlier I said that this can

34:11work with your local files, right? But

34:13what does that actually mean? So, if I

34:15open up my file explorer right here, you

34:17can see that I've got, you know, my

34:18downloads, my desktop, a bunch of other

34:20folders, pictures, whatever. And Claude

34:23Code is able to navigate through all of

34:25this, edit these things, move them

34:27around, find things for you. So, just as

34:29a super simple example, right here, I

34:30have AIS Live Black, which is just a

34:33logo, right, for our upcoming event, and

34:35this is in my downloads folder from a

34:37few weeks ago. Let's say I knew that

34:39that was there, but I forgot exactly

34:41what it was called or how to find it. I

34:43could real quick just say, "Hey, so I

34:46know that I have the AIS Live logo, the

34:49black version, somewhere in my downloads

34:50folder. could you real quick just find

34:52that for me and then you know just help

34:53me pull that up. And so what happens

34:55when you actually send off a message to

34:56claude code is it will tell you what

34:59it's doing. So right here it says

35:00searching AIS live. That means it's

35:02searching through my um local files and

35:06it's searching for these terms AIS live

35:08logo ais

35:10you know BB black AIS live. It found

35:13this right here. It found the AIS live

35:15PNG. Then it says, "Okay, there's it

35:17with the blue live and the red dot,

35:19which means it used its vision to look

35:21at it to verify that this actually is

35:23the real logo that I'm talking about."

35:25And now it's using something called

35:26PowerShell. So, it's basically just

35:28running commands. And it said, "Pulled

35:29it up. Here's the file. It's in your

35:31downloads." There's also an AIS Live

35:32White sitting right next to it. Do you

35:34want me to copy these into the brand

35:36assets so that it's in our project here?

35:38Or, you know, like what do you want to

35:39do with it? And basically, if I wanted

35:41to just find it again, I could just copy

35:42this file path right here. I could then

35:44go into my file explorer and I could

35:46just come in here, paste that, hit

35:47enter, and it will open up the picture.

35:49So now I was able to locate exactly

35:51where that was. But yeah, if I wanted it

35:53to organize my downloads folder or

35:54organize all of my documents, it could

35:56do so. Now, another quick example, it

35:59can also create things for you like

36:00Excel sheets, Google Sheets, Google

36:02Docs, HTML, um, documents, websites,

36:05apps, anything. So here's a quick

36:08example. I gave it a SLG goal prompt

36:10which I'll talk about later in this

36:11course, but that basically just means

36:12that I'm able to set a condition and

36:15Claude will keep working until that

36:17condition has been met. I mean, that's

36:19so that's just an employee, right? So, I

36:22basically said, I want a quarter 2

36:23assessment of my YouTube channel

36:24performance. This means that you have to

36:26go to YouTube, pull the data, and then

36:28put everything into an Excel sheet. And

36:30I don't only want you to display the

36:32stats. I want you to do deep analysis as

36:34if you are my master content strategist.

36:38an analyst. So then what happens is it

36:40reasons through and you can see here it

36:42even took screenshots to verify that

36:43everything looked good and to verify

36:45that everything was accurate and then it

36:47comes through and it gives me this Excel

36:48sheet and as you can see it put it here

36:50inside of my Herk 2 project in a folder

36:52called projects in a folder called

36:54YouTube Q2 2026 assessment and then it

36:56gave me this Excel sheet which if I pull

36:58this up this thing is pretty legit. We

37:01have a start here page with a bunch of

37:02you know just basically onboarding us to

37:04this doc. I can see the executive

37:06dashboard with my real-time stats,

37:07subscribers, my performance from the

37:09quarter, things that mattered. I can

37:11look at per video scorecard with length,

37:13views, views per day, watch hours, all

37:15these other stats, monthly trends,

37:17content pillars, format and length,

37:19audience, and traffic. So, this is

37:22insane. How long would this have taken

37:24you to manually go pull out, you know,

37:2775 videos, put all the analytics in

37:29here, color code it, design it, do all

37:32of this, and this literally took my AI

37:34agent about 10 minutes right here inside

37:38of Cloud Code. And once again, all I did

37:40was I used my completely natural

37:41language. All of you guys could have

37:42instructed Claude to do this. Okay, so

37:44we're flying through here, just getting

37:46through a lot of the beginning stuff,

37:47and hopefully you guys' mindsets are

37:48getting in the right spot and you're

37:49getting excited. Let's talk a little bit

37:51about prompting. What exactly is

37:53prompting? Prompting is the way that you

37:55talk to your AI in a way that actually

37:58helps it achieve the goal that you want.

38:01Now, there's a few levers to pull here

38:02when it comes to prompting. And it's

38:04really not that complicated. You know,

38:06we used to have this term, which it

38:07still exists, but there's this term

38:09called prompt engineering, which is

38:12basically the art of designing prompts

38:14to get your AI agents to actually do

38:16what you want them to do. Now, prompt

38:19engineering is becoming a little bit

38:20less important over the years. It's

38:22still very important, don't get me

38:22wrong, but the the models are getting so

38:24much better where the prompt is less

38:26important. It still is important, but

38:29over time, I think it will become less

38:30important because the models are getting

38:31smarter. But generally, the things that

38:33I like to tell my agent are the role. I

38:36like to define, hey, here's who you are.

38:39Here's what you're supposed to do. And,

38:41you know, context. So, you are a master

38:46content strategist like you just saw in

38:47that example. you are helping Nate who

38:50runs a business doing X Y andZ and here

38:52is what's important to Nate's business

38:53these X Y andZ metrics and here is the

38:55avatar for Nate's business so giving

38:57context on you know the background so

39:00context and background I should say

39:02because not only is the background

39:04important but specific context so I need

39:06you to help me run you know a Q2 an

39:08analysis on my YouTube channel so that I

39:11can look at all the stuff and I can make

39:13Q3 even better example instead of saying

39:16hey help me write this email to my boss

39:18say, "Hey, help me write this email to

39:20my boss. I need you to, you know, sort

39:22of tread lightly here because I've

39:23gotten in trouble twice in the past

39:24month, and I'm asking for more time off,

39:26and you know, he's a really great and

39:28understanding person, but I just feel

39:30like a little bit guilty for asking for

39:31this time off, so that's why I need help

39:33writing this email." Giving your agent

39:35that context is going to make it

39:37understand your actual desires much

39:39better. So, you're not sitting in this

39:41place where you're like, "Okay, AI

39:42sucks. It didn't give me what I wanted."

39:44it's probably because you didn't give it

39:46clear enough instructions of what you

39:48actually want. Now, the next thing I

39:49like to do is I like to negative prompt.

39:51Basically meaning what not to do. So, if

39:54you think about it like you're

39:55instructing a student or, you know, a

39:57kid who's trying to learn a new process,

40:00you have to tell them what not to do

40:02because they're curious and they're

40:04going to try different things unless you

40:05explicitly tell them not to. Now, if you

40:07were telling someone like, I don't know,

40:08a 45-year-old how to make scrambled

40:11eggs, you probably wouldn't tell them

40:12not to put their palm on the stove top

40:14because, you know, they've been around,

40:16they have experience, they know not to

40:17do that. But an innocent mind probably

40:20doesn't know that. So, you would say,

40:21"Hey, don't ever touch the pan. It's

40:23going to be very hot." So, negative

40:25prompting, I found, has really helped

40:27make sure I keep the agents on the guard

40:29rail that I'm actually, you know, trying

40:30to keep them on. And then another thing

40:32that I really, really like to do is add

40:34verification. Basically, the idea of,

40:37you know, and I'm actually going to type

40:38this out because it's so important in

40:39all caps. Make the AI prove its work. Is

40:45that the right its? That might not be. I

40:47think in this case, that's the correct

40:49it. But either way, you guys know the

40:50point I'm trying to make. Make the AI

40:52prove its work. Think about it like

40:54this. Let me make another one of my

40:57little axis charts here. Okay. So when

41:00you ask AI to do something, what are you

41:03ultimately looking for is you are

41:04looking for it to get you an output that

41:07is 100% perfect. Now realistically that

41:10doesn't happen very often. So what

41:12happens is on the first pass you're

41:14maybe getting somewhere around 60% of

41:16the way there and then what happens is

41:17you give feedback like we talked about

41:18earlier and then it tries again and now

41:20you get a little bit higher and you just

41:22keep doing these manual iterations of

41:23feedback until you eventually get to

41:25this point where you're satisfied with

41:26the work and maybe you take it home that

41:28last 2% or 1%. Now this is because it is

41:33not the one verifying its work. You're

41:35the one verifying its work. So all of

41:37these steps are keeping you in the loop.

41:39But what if you could actually make the

41:41AI check its own work so that on the

41:43first try now it's maybe getting and

41:45that's not completely aligned. Let's try

41:46that again. So that on the first try

41:48it's maybe getting you 80% of the way

41:50there. And then you the human have to

41:52iterate two or three times and then

41:53you're there. And this gap is what

41:55you're trying to close here by allowing

41:57the AI to prove its own work. So what

41:59could that look like? Let's say you are

42:01building um a website and you want to

42:04make sure that the form submission works

42:06and that it doesn't accept, you know,

42:08bad versions of an email. For example,

42:10if people are submitting their emails,

42:12it has to be, you know, an actual at

42:14something domain. Well, you can have the

42:17agent open up the website and test it a

42:20100 times and test that, you know, none

42:22of these edge cases sneak through and

42:24then it proves to you, hey, here's what

42:25I ran. Here's why I'm confident. And

42:27what what you'll find is when you do

42:28stuff like that, when it's trying to

42:30prove it's work and it finds bugs, it

42:32will fix the bug and then keep testing

42:33and then fix the bug again and then keep

42:35testing. So the way that you do

42:36verification is obviously different for

42:38whatever the task at hand is, but every

42:40task at hand has some sort of

42:42verification. Just think about it like

42:44this. If a human gave you this work,

42:46what would you do to approve it? Would

42:48you just read it through a bunch of

42:50times to make sure there's no

42:50grammatical errors? Would you actually

42:52open it up and use it? Would you make

42:54sure that there are no, you know,

42:56elements out of bounds or something like

42:58that? Whatever you would do to verify

42:59it, chances are you can tell cloud code

43:02to do that to verify it. So those are

43:05kind of like the main four things just

43:06to keep it simple that I'm always

43:08thinking about when I am prompting my

43:10cloud code to do something for me. All

43:12right, so let's talk a little bit about

43:13tokens and models. So what is a token?

43:17Well, a token is basically what we are

43:19being built for. So when you pay for

43:23credits on some account, you're paying

43:25for credits. When you are paying for

43:26your AI model usage, you're paying for

43:29tokens. A token is essentially how AI

43:32interprets text. So maybe four

43:35characters is a token or maybe a

43:37punctuation mark is a token. It's not a

43:39deterministic rule of what a token

43:40really is, but roughly 3/4 of a word or

43:43short words. So maybe in this scenario,

43:45the sentence cla code helps you is four

43:48tokens. Now you don't need to know

43:49exactly this like that's not important

43:51if you can identify hey that's one token

43:53that's 12 tokens. What's important is

43:55that you know the pricing. So different

43:58models cost different amounts and they

44:00all have different pros and cons. So if

44:02we look at for example right now in the

44:04current you know July of 2026 clawed

44:07models with their tokens. Haiku is fast

44:10and cheap. So for certain scenarios you

44:12only need Haiku because it's fast and

44:14cheap. It's $1 for a million input

44:17tokens and it's $5 for a million output

44:20tokens. Sonnet 5 is $3 for a million

44:23input tokens and $15 for a million

44:24output tokens. And this is a very

44:26balanced model. And Opus 4.8 is the most

44:28expensive model right now besides Fable,

44:30which is even more expensive, double the

44:31cost of Opus. But Opus 4.8 is $5 for a

44:35million input tokens and $25 for a

44:37million output tokens. Now, yes, you

44:39guys are noticing there's input and

44:40there's output. And the output tokens

44:42are more expensive. So, what is the

44:44difference there? Well, let me open up a

44:46new chat real quick. Or actually, let's

44:48just go to the one where I said hello.

44:49So, every time that I shoot off

44:51something, my prompt goes into the

44:54model. So, this is where we're being

44:56charged for Opus, for example. Actually,

44:58let me switch this model back to Opus.

45:00This is where we're being charged $5 per

45:02a million input tokens for this. Now,

45:04everything that it spits out, all of

45:06these things, all of these commands,

45:08every line of text that it gives us that

45:09it puts out, that's being charged for

45:12Opus at $25 per million output tokens.

45:16So, when it is outputting stuff, that's

45:19more expensive than what you're feeding

45:20into the model. So, for example, if I go

45:22back to the YouTube Analytics one where

45:24it gave us this Excel sheet, let me ask

45:26a quick question. How many tokens did

45:30you have to output to actually generate

45:32that YouTube assessment Excel sheet? And

45:35because you were using Opus, how much

45:38real money would that have costed me?

45:40Okay, so keep in mind that this is an

45:42estimate. But here is what this looked

45:45like. So it had to run different

45:47scripts. It had to check them out, write

45:50up the brief, it had to build the Excel

45:52sheet with different actual characters

45:53inside, and then it had to output

45:55basically all those commands. So they're

45:58calling this an average of let's just

45:59say 25,000 output tokens. And at the

46:02pricing for Opus 4.8 of $25 per million,

46:05this basically costed us about 63 to run

46:09because of the output tokens. Now what's

46:11interesting though is because in this

46:12session you can see that we've used

46:14about 428,000

46:16tokens out of the a million context

46:18token window which I will explain in a

46:20bit if that makes no sense to you. But

46:22the point here is that we probably sent

46:25in and the AI model had to look at

46:27hundreds of thousands of tokens because

46:28it had to pull so much data from YouTube

46:31and analyze so many things and that was

46:33going into the model. So those input

46:35tokens because typically you use a ton

46:37more input tokens. That's why they're

46:39build lower. This will all start to

46:41click a little bit more when you really

46:42start to get your hands on. But I just

46:43wanted to show you a real quick example

46:45of kind of the difference and

46:46understanding that different models have

46:48different, you know, pros and cons. fast

46:50and cheap, balanced, most capable, but

46:51they also come with different prices.

46:53Now, keep in mind because you guys are

46:56on a clawed subscription, when you come

46:58in here and you go to your usage, this

47:00is how you're being build. You're not

47:01being build per token because you're

47:03using this inside of your subscription.

47:05In your subscription, you have different

47:07limits. You've got a current session

47:09limit, which is a 5 hour rolling window.

47:11You've got all models, which is a weekly

47:13limit. And then right now, we have a

47:15fable limit as well. So you can only

47:16use, you know, this much fable before

47:18all of this would then switch usage

47:20credits on top. It's really cool because

47:21I'm on the $200 a month max plan for

47:24cloud code. And if I filled up every

47:26single session, every single weekly

47:27limit, I would actually be getting

47:29around $8,000 of inference out of my

47:31$200 a month plan. So we're getting this

47:34on a huge discount right now. So if you

47:36are using cloud code or you're using the

47:38cloud models or any AI models through

47:40API billing, which is through token

47:42billing rather than through a

47:43subscription, you are paying a ton more

47:46for those tokens. So that is something

47:48else important to keep in mind here. But

47:50don't stress about this too much. You're

47:52on a subscription, you're getting a good

47:53deal. Just monitor your session limit,

47:55monitor your weekly limits. I've got

47:57videos coming later and I've got, you

47:59know, sections in this course later

48:00about context window management and

48:02session limit management. So just keep

48:04that in mind for later. But I just

48:06wanted to break that down a little bit

48:07for you guys so that these terms all

48:09sort of click and make sense. Okay. The

48:11next thing we have here to understand is

48:13called the claw.md.

48:15So if I just real quick open up this

48:18project, right? This is my Herk 2

48:19project which I'll refer to a lot. This

48:21is my AI operating system. This is the

48:23place where I have basically everything

48:24about my business. If I go over here to

48:27my files and I scroll until I find my

48:31claw.md, as you can see, there's a lot

48:32of folders and files inside of this

48:34project. There it is. I scrolled right

48:36past it. This is my claw.md. So, let me

48:38open this up full screen. This is

48:40basically the system prompt for my AI

48:43agent. So, before I read this, let me

48:44just show you a quick visual. So, this

48:46is our little cloud code agent, right?

48:48And let's say we open up a new chat. So,

48:50this thing is completely fresh. It just

48:52woke up. And I go ahead and I shoot off

48:54a message to this thing that says hi.

48:56Right? So I say hi. I don't know why

48:59this keeps going green, but I say hi to

49:01my AI agent. What it does before it

49:03processes this message is it's going to

49:05read the cloudmd

49:07so that it can basically get, you know,

49:09it can orient itself with where we are.

49:12So it will read this cloudmd which is

49:16bunch of lines of text and then it will

49:18read my message and then it knows how to

49:20respond more accurately. So, if I go

49:23back into this real quick and actually

49:26just real quick close out of this file,

49:27what you'll see is that when I said

49:29hello, it said, "Hey, Nate, ready to go

49:31whenever you are." A few things on the

49:33radar in case any next AIS live is this

49:35Friday and Saturday, July 11th and 12th.

49:37CIA opens July 28th. Open threads,

49:40highros, link tagging, keep the best of

49:42fable, what do you want to work on? The

49:44reason it knew this is because it was

49:46able to orient itself with my cloud.MD

49:48MD and read about my projects and you

49:50know what's going on in my business so

49:52that it's able to help me out way more

49:54specifically. So let's take a brief read

49:56through some of the stuff that's in my

49:57cloudmd here. So this is me setting up

50:00the role, right? You are Nate Herk's

50:02executive assistant. Your job is to help

50:03him spend less time on operations,

50:04people management, and admin so he can

50:06focus on learning AI tools and making

50:08YouTube videos. That is his number one

50:10priority. I've then given it a routing

50:12map. So this is where things live. If

50:14you want to find things about, you know,

50:16his business, his team, his OTAAS, his

50:18strategy, go here. Here's the path. If

50:20you want to find things about corporate

50:22structure, entities, tax, IP, then go

50:24here. Voice and style, go here. Course

50:27knowledge, go here. Projects, as you can

50:29see, I'm just telling it where

50:31everything lives. And now it can read

50:33through this and it can help me out way

50:34more specifically. So, this is something

50:36that you'll build over time. I'm going

50:38to build one with you guys. Don't worry,

50:39just a sec. But this is basically just

50:42what it is. It's a system prompt for

50:43your AI agent and it changes all the

50:45time because remember how earlier I

50:47talked about the loop of getting an

50:50output

50:52using judgment to assess it and then

50:54instructing Claude to change so that it

50:56doesn't happen again. This is where I

50:57would say you know like let's say it

50:58gives me this output right let's say it

51:00gives me some research brief that was

51:01just absolutely horrible. I would then

51:03say okay

51:05Mr. Claude I read this research brief

51:08and I don't like this because you didn't

51:10use enough sources. is you only gave me

51:12four. Um, you also put a ton of m dashes

51:15in here which I don't like. So update

51:16your instructions in the claw.mmd so

51:19that you don't do this again. So that

51:20next time I ask you to do research, you

51:22do it better. And then claude will write

51:24changes in its own claw.mmd and then

51:27next time you talk to it, it will be

51:28smarter. So that's how these things just

51:30get smarter and smarter as you use them.

51:31And by the way, when you guys see a file

51:33that ends in MD, all that means is

51:36markdown. Markdown is basically just

51:38like a a computer language and it really

51:41just lets computers understand like

51:43headers and bullet points and really

51:46just structure like that. So this is

51:49markdown when it's rendered nicely for

51:51us humans to read. But this is kind of

51:54what raw markdown looks like. You can

51:56see we use these pound signs to

51:58indicate, you know, status and layers

52:00and we use these dashes for bullets and

52:02everything like that. But MD just means

52:04markdown. The same way later in this

52:06course you might see a py file which

52:08would be a Python file or a txt which

52:11would be a text file. So the dot and

52:13then the you know whatever comes after

52:15that the suffix or whatever is just the

52:18type of file. All right. So here's what

52:20I'm going to do that I want you guys to

52:21do with me. I'm on my desktop. You can

52:23be wherever you want but I'm going to

52:24create a new folder. I'm going to open

52:26up a new folder right here. And I'm just

52:27going to say I'm just going to call this

52:29knowledge work since that's kind of what

52:30this course is about. So, I have a

52:32knowledge work folder on my local

52:34desktop that I just created. And what

52:36we're going to do now is I'm going to

52:37open up Claude and I am going to click

52:39on the plus um right here, new session.

52:42And instead of working in my Herk 2

52:44directory, I'm going to open a new

52:46folder. And this is where I'm going to

52:47go to my desktop, and I'm going to find

52:48that knowledge work folder, and I'm

52:50going to open that up, which basically

52:52means we're now working inside of this

52:53knowledge work folder. Where is it?

52:56There it is. Okay. Select the folder.

52:58Now, it's going to ask if we want to

52:59trust this workspace, which I'm going to

53:01do because it says Claude may read,

53:03write, or execute files in this

53:05directory. So, only proceed if you

53:06actually trust the space, which I do.

53:08So, I click trust. Now, what happens?

53:10Well, let's just start building this

53:12thing out. I'm going to say, "Hey,

53:14Claude Code, my name is Nate." We're

53:16just going to start off conversation

53:17like that. Now, there is one thing to

53:19keep in mind here is because we're in a

53:22new project, Claude still knows a little

53:25bit about me. And the reason is because

53:27there's a difference between your global

53:29cloudmd and your project level. So let

53:32me explain that real quick as well. If I

53:34go down here, I've got some stuff ready.

53:36Okay, so global versus project. Global

53:39is something that claude reads every

53:41single time and that is at the global

53:43level. Whereas project is only

53:45specifically in that project. So the

53:48example you guys just saw, we looked at

53:50this cloud.MD where you know it said,

53:51"Hey, you are Nate's executive

53:53assistant." This gets loaded in whenever

53:55I'm inside of my Herk 2 project. But

53:58whenever I'm in this new knowledgework

54:00project, if I go over here and I go to

54:01my files, this folder is empty. So

54:03there's no project level claw.mmd file

54:05being read yet. But what there is is a

54:08global level claw.mmd. And that's how it

54:10knows my name. Well, I just said it, but

54:12that's how it knows other things about

54:13me because there is a global file. So

54:16real quick, I'm just going to show you

54:17what the global claw.mmd looks like. And

54:19before you get a little bit overwhelmed

54:21or stressed about this, like I said,

54:23this is something that evolves over time

54:25and it really isn't a big deal because

54:27you're able to change it so frequently

54:29by asking Claude to just change it. And

54:31for me, the global cloudMD is just

54:33things that I like and things that I

54:34don't like. So, let me show you exactly

54:35what I mean by that. The way I get there

54:37is I go to my PC, I go to my drive, I go

54:40to users, and I click on my user. And

54:42then there's a cloud folder right here.

54:44And then from here, I can open up my

54:46claw.md, which is just a markdown file.

54:48So, this obviously looks a little bit

54:50more um ugly because it's opened up as

54:52as markdown in my notepad. But look at

54:55this. This is my global cloudmd. These

54:57rules apply to every product on your

54:59machine. They govern any writing meant

55:01for Nate or publishes Nate. So, LinkedIn

55:03posts, YouTube scripts, comments,

55:04emails, captions, docs, anything. So, I

55:07have this AI phrase kill list, which I

55:09said, hey, every time it outputs

55:11something and I say I don't like how

55:12that sounds because it sounds like AI, I

55:14say add this to my global claw.mmd. You

55:16know, I don't like this stuff. So, it

55:18won't say all of these phrases. It will

55:20never write this in my LinkedIn posts.

55:21It will never write this in my YouTube

55:22video scripts or anything like that

55:24because this is a global rule. And look

55:26at this. It's also just learned other

55:27things about me over time. He here's the

55:29things he doesn't like. Here's other

55:30things that I've learned. And this is

55:32just my global claim. It's very simple.

55:34So, if you've got specific things about

55:35your business or specific things about

55:37the way that you like to treat Claude or

55:38you like Claude to treat you, then you

55:40can put those in your global rules

55:42because you're okay with the fact that

55:44every single project ever that you do

55:45with Claude on this local machine, these

55:48rules will be in play there. So, that's

55:49kind of the difference between um as I

55:52said global cloud.MD and project

55:55cloud.MMD. And it's important to keep

55:56this in mind because later when we talk

55:58about things like skills, there's also

56:00the element of is this a global skill or

56:02is this a project level skill? Okay.

56:04Anyways, this is a new project, right?

56:06This is called knowledge work. So, I'm

56:07just going to say I'm building a brand

56:09new project right here and this is for a

56:12course that I'm teaching. So, this is

56:13kind of a demo project for me, but I

56:15want to show my audience how to set up

56:17cloudmds, how to build skills, how to

56:19build automations. And so we're going to

56:20do a lot of this together, but right now

56:22what I want you to do is just initialize

56:24with a cloud.mmd and acloud folder.

56:27There doesn't have to be anything inside

56:29of the cloud. Um, but just initialize

56:31with a cloud.md folder and just throw a

56:34little bit of baseline information in

56:35there for now. And by the way, if you

56:36guys are interested in the voicetoext

56:38tool that you see right up on screen

56:40right here, it's it's very pretty, then

56:42check out the link in the description.

56:44It's called Glido. It's our tool that we

56:46created and it is the fastest and the

56:47most private on the market. So check it

56:49out. Anyways, as you can see, it is

56:51going to start building that for us. So,

56:52in a sec, we'll see over here on the

56:53folder side, we will see a cloudmd and

56:56we will see acloud folder. There we go.

56:58They just popped up. So, the cloud

56:59folder has nothing, but the cloudmd. Let

57:01me open that up real quick. This file

57:03gives cloud code the context it needs to

57:05work in this repository. Keep it short

57:07and current. Here's the project

57:08overview, demo project, here's a

57:10structure, here's the conventions, and

57:12any notes. This cloudmd file will grow

57:14over time. So as we go through this

57:17course and as we build new things, we

57:18will all watch this cloudmd file grow

57:20together. So that is just showing you

57:22how easy it is to get that set up and

57:24that your agent can fix that, edit it,

57:26delete it, stuff like that at any time.

57:28Now you guys are probably wondering what

57:29is this.cloud folder. So let me go back

57:31up here and show you that that is

57:33exactly what we are going to cover next

57:35is the cloud folder. These are basically

57:37the first two things that I always set

57:39up when I'm working on a new project in

57:41cloud code. I have the cloudmd which

57:43once again is the rules in plain English

57:44and then we have the cloud folder which

57:46is basically just think about it almost

57:48like a settings folder. It's the config

57:50folder. Usually the things that go in

57:52here there sometimes are more but the

57:53three things that I want you guys to pay

57:54attention to the settings.json which has

57:57things like permissions preferences the

58:00agents folder which is where you can

58:01build your own custom sub aents and put

58:03them in there and then skills which is

58:05where you package up your skills and

58:07they live in here so that Claude can

58:09call on them automatically. Now, just as

58:11an example, let me show you inside of my

58:13Herk 2 project what this looks like. So,

58:15I'm going to come back over here and I'm

58:16going to open up my doclaude, which is

58:18right here. You can see that I've got

58:20my, you know, agents, the one I talked

58:22about. I've got my settings files, and

58:24I've got my skills. Now, obviously,

58:26there's some other things because this

58:27project is massive. And that's why I

58:29wanted to call out just those three. And

58:31really, the most important one, in my

58:32opinion, is skills. We're going to talk

58:34about skills later more in depth, but

58:36look at this. Every single skill that I

58:37have in here is just natural language.

58:39So, for example, if I open up this grill

58:41me skill, it's just a markdown file. So,

58:43if I open this up, it looks very similar

58:45to the claw.mmd. It's all natural

58:47language and it just tells Claude code

58:49what to do in this specific skill. So,

58:51that's the exact same way that it works

58:53in here with our sub agents. And our

58:55settings is basically just a JSON file

58:57that shows Claude what it's able to do

59:00and what it's not able to do. You can

59:02see we have allowed permissions. We have

59:03some environment variables up here.

59:05We've got things that we've denied. That

59:07is basically how this settings file

59:09works and you don't need to know how to

59:11build this. It will build it

59:12automatically and you can use natural

59:13language once again. So every single

59:15project will have these two things but

59:16these two things also live on a global

59:18level because once again if you wanted

59:21to have you know you have your global

59:22cloudmd but then you also have global

59:24skills potentially. I only have one

59:26right now that's global on this machine

59:28but you can have skills that apply to

59:30every project. You can have sub agents

59:31that apply to every project. So that's

59:33the difference between global and

59:35project. But these are two things that

59:36you always want to get set up when

59:38you're building a new project. Okay,

59:40anyways, let's keep on moving here. Um,

59:42one thing I do want to call out is you

59:44might see my claude here switch over to

59:46usage credits. And once again, that's

59:48because I'm about to go past my 5 hour

59:50limit, in which case, if I want to keep

59:52going, I will then be paying per token.

59:55So, because I'm on the 200 bucks a month

59:56plan, that doesn't happen too often. But

59:57when I am pushing this thing to its

59:59limits, I do hit that. And so if you

1:00:00guys see that pop up on my screen

1:00:02somewhere else in this course in a few

1:00:03minutes, don't be concerned. That's all

1:00:05that that means. So anyways, the next

1:00:07thing I want to talk about API keys and

1:00:10env. So first of all, what is an API? It

1:00:13stands for application programming

1:00:15interface. And all of that really means

1:00:17is it's a method to allow one software

1:00:19to talk to another software. So imagine

1:00:22Gmail sending a letter to claude code.

1:00:25That's via API. So the point I'm trying

1:00:27to make here is remember earlier in this

1:00:30course when I showed you guys this demo

1:00:32where I opened up, you know, this

1:00:34YouTube thing and I said, "Hey, go to

1:00:36YouTube and pull my data." The only

1:00:38reason that it was able to go pull my

1:00:40data from YouTube is because I gave it

1:00:42my API key, which is essentially a

1:00:45password. So if I said, "Hey, go pull

1:00:47Mr. Beast's YouTube data," it could only

1:00:49pull publicly accessible YouTube data.

1:00:51it couldn't pull some of those more, you

1:00:54know, detailed stats that I saw from my

1:00:56channel because I don't have his API

1:00:57key. So, what we had to do was we had to

1:01:00give it access to actually pull that

1:01:02information. And that's when it used

1:01:04this fetch YouTube data py, which py

1:01:06stands for a Python file, and it used

1:01:08that in combination with my API key. As

1:01:10you can see here, YouTube data API. So,

1:01:13that's what I'm going to show you guys

1:01:14now in our knowledge work project. We're

1:01:16going to get set up with an API. So, the

1:01:18example that I'm going to show you guys

1:01:19is Tavi. Tavi lets agents search the web

1:01:22better. You can see here they even have

1:01:24an official Tavi agent skills for Cloud

1:01:26Code, which I'm not going to dive into

1:01:27right now, but the reason I wanted to

1:01:29show you guys Tavi is because you can

1:01:30sign up here and you get a,000 free

1:01:32credits right away, no matter what. So,

1:01:34Cloud Code does have built-in tools for

1:01:36research. So, if I say, "Hey, can you

1:01:39please just research what is context

1:01:41engineering?" And just give me like a

1:01:42one-s sentence example or sorry, a one-s

1:01:44sentence definition. So it will

1:01:47basically because I'm prompting it to

1:01:49research this. What it's going to do is

1:01:50it will probably you see it says finding

1:01:52tools. It's looking through different

1:01:54tools and then it's going to actually

1:01:55search the web using a search the web

1:01:58tool. Query select web search. So web

1:02:01search is one of the tools that it can

1:02:02use. It also has something like a web

1:02:03fetch. But anyways the point I'm trying

1:02:06to make here is cloud code can natively

1:02:08search the web. But if we give it

1:02:09something like tavally it's always good

1:02:11to just get other sources right. So what

1:02:13I'm going to do here is go back into

1:02:14tavi. You can see right here I have an

1:02:16API key which I'm going to go ahead and

1:02:18copy. Now API keys because they're

1:02:20passwords once again do not share them

1:02:23with anybody because let's say I am on a

1:02:26paid plan of Tablety and let's say I

1:02:27paid for 5,000 credits. If I gave you

1:02:29guys my API key, you guys could all take

1:02:31that and spend my money and I don't want

1:02:34to, you know, I don't want you guys to

1:02:35spend my money. So I'm not going to show

1:02:36you my API key in this video. I will

1:02:38because, you know, it's a demo and I'm

1:02:40showing you and I'm going to delete it.

1:02:41But that's how you should treat API

1:02:43keys. You shouldn't be posting them

1:02:44online. and you shouldn't be sharing

1:02:45them across your team unless you know

1:02:47your team is supposed to. So anyways,

1:02:49what I'm going to do is say well not say

1:02:52I'm going to go into my files and you

1:02:53can see that once again we only have

1:02:55these two. So what we need is we need a

1:02:57file called aenv

1:02:59which basically just stands for like

1:03:01environment. So environment variables

1:03:03hey cloud code I want to connect you to

1:03:05tavi and in order to do that I need to

1:03:07give you my API key for tavi. So can you

1:03:09please create in this project aenv file

1:03:12so that I can upload my secret. Now the

1:03:14reason we want to create thisv file is

1:03:16it just it's a safe place to put secrets

1:03:19because they won't get pushed to GitHub.

1:03:21You can see here it said let me also add

1:03:23a dot getit ignore so the secret never

1:03:25gets committed. Now, I'll talk about

1:03:27GitHub more later on in this course, but

1:03:29basically what that is is think about

1:03:31this. When you make a Word doc on your

1:03:34computer, no one else can get that,

1:03:36right? But if you push that up to like a

1:03:38shared drive, then other people can

1:03:39collaborate on it and work on it if you

1:03:41make that public. At least you can still

1:03:42push something to the cloud or push

1:03:43something to GitHub and keep it private.

1:03:45So anyways, the point being if you put

1:03:47something in yourv that will never get

1:03:49pushed to GitHub ever. So basically

1:03:52things that are secret and sensitive put

1:03:53in yourv. So anyways, it went ahead and

1:03:56it created that env as you can see right

1:03:58here. And there's currently nothing in

1:04:00there, but it did give us this

1:04:01placeholder. It says Tableau API key.

1:04:04This is where you would put your Tableau

1:04:05API key. So I'm just going to go ahead

1:04:07and delete the placeholder. Go back into

1:04:09Tavi. Copy this value. Go back into

1:04:12Claude, paste that in there, and then

1:04:14hit save. So now that our API key is in

1:04:16there and it's saved, I can say, cool.

1:04:18So I just gave you my Tavly API key. Go

1:04:20ahead and make a test request to Tavi to

1:04:22make sure that that works.

1:04:25So, I'll go ahead and shoot that off.

1:04:26The dictation tool spelled Tavly wrong.

1:04:28So, let me just go into here and add a

1:04:31correction in the dictionary for the

1:04:32real way that you're going to spell

1:04:34Tavi.

1:04:36Okay, there we go. Anyways, it's going

1:04:39to make a test search request. As you

1:04:42can see here, it made a post request to

1:04:43this endpoint. It searched for what is

1:04:45Claude Code by Enanthropic, pulled the

1:04:47key from the ENV, and got a 200

1:04:50response. So, all of that means it

1:04:52worked. Here's what I want you guys to

1:04:53notice. I didn't tell it how to do any

1:04:55of that. It went off, it researched

1:04:56Tavi, it figured out the endpoint, which

1:04:58is this thing right here, and then it

1:05:00just made the request, which is awesome.

1:05:02Before, you know, a year ago, if you

1:05:03were still building with Naden or

1:05:05something like that, we had to basically

1:05:07by hand research the API endpoints and,

1:05:10you know, plug all this in by ourselves.

1:05:11But now, Agentic AI is getting so

1:05:13powerful that my simple request saying,

1:05:15"Hey, just use Tavi turns into the agent

1:05:18reasoning, researching, testing, and

1:05:21verifying." So super super cool. Now

1:05:23what would happen if I went into my

1:05:24files and I went into thev and then I

1:05:27basically just changed the API key. So

1:05:29this is no longer a valid API key.

1:05:30That's not correct. I'm going to go

1:05:32ahead and clear out the conversation. So

1:05:34I can do a slashclear command right

1:05:36there. And now the conversation's reset.

1:05:38And now if I say, hey, can you try to

1:05:40use Tavi to search for Lenol Messi?

1:05:44When I shoot this off, we're going to

1:05:46see that it's going to try to use Tavi,

1:05:48but it should come back with like a 400

1:05:50error. and it's gonna say, hey, you

1:05:51know, like something's wrong with the

1:05:52API key or something like that. Okay, so

1:05:54now it just defaulted to the search, you

1:05:56know, the the default cloud code web

1:05:57search tool, which I didn't want it to

1:05:58do. So, let's try this again. No, we do

1:06:02have a Tavi API key that I've given you.

1:06:04Try to use that.

1:06:06Don't use the clawed native web search

1:06:09tool. Okay, so let's analyze what just

1:06:11happened here because there's a lot of

1:06:12lessons inside. So, let me close this

1:06:14stuff out.

1:06:15We used Tavi, right? We used it with the

1:06:18correct API key and it worked. And then

1:06:19I cleared the session. When I cleared

1:06:21the session and asked for it to use

1:06:23Tavi, it said there's no Tavly tool

1:06:25connected in this session, so I can't

1:06:27use it specifically. Now, what does that

1:06:29tell us? That tells us that Cloud Code

1:06:31just forgot what we just did. It forgot

1:06:33that we just successfully used Tavi. We

1:06:36also see that again when it said, "Okay,

1:06:38I found the key, but there's no MCP tool

1:06:40wired up, but let me try to call Tavi's

1:06:42REST API."

1:06:44It tried. It didn't work. So, it said,

1:06:46"Okay, let me try a different method."

1:06:48that also didn't work and then what it

1:06:49did is it said okay confirm that the you

1:06:52know the API key is correct so that just

1:06:54shows you that it didn't work because

1:06:55the API key was wrong but the other

1:06:57lesson here is that we just used Tavly

1:07:00successfully so why did it have to do

1:07:02all this research once again you know

1:07:04what we would do now is save that

1:07:06somewhere in our project so what I'm

1:07:08going to do is I'm going to fix the API

1:07:10key real quick so I'm going to go back

1:07:11into tablet copy this go back into

1:07:13claude we're going to open up our files

1:07:15go to thev and we're going to replace

1:07:17this once again with the true API key,

1:07:19which is correct. And then I'm going to

1:07:20go back in here and say, "Okay, thanks.

1:07:23You were right. Our API key was wrong. I

1:07:25just fixed it. So, go ahead and test it

1:07:27again." But then the other thing I want

1:07:29you to do is somewhere in this project,

1:07:32save this as, you know, a skill or save

1:07:36this as something because we will

1:07:37probably be using Tavi frequently. And

1:07:40when I want you to do research, I want

1:07:41you to default first to Tavali and then

1:07:44to the default clawed web search tool.

1:07:47So actually don't create a skill. Just

1:07:49put this in the cloud. Denmd

1:07:51save the endpoint so you understand like

1:07:52we've done this before. I don't want you

1:07:54to research it every time and that's how

1:07:56you can keep getting smarter every time

1:07:57I interact with you. So that was

1:08:00obviously kind of a a very messy casual

1:08:03version of my prompt, but that's the way

1:08:05I do it, right? I identified something.

1:08:08I found myself repeating something that

1:08:09I didn't want to and then I feed it back

1:08:11in because here's the thing that may not

1:08:13seem like a big deal allowing it to

1:08:15search and remake the request. But what

1:08:17happens is this every time costs you

1:08:19tokens and tokens cost money, right? Cuz

1:08:22they go against your session limit. So

1:08:24the more efficient you can be with

1:08:25memory and with things like that, the

1:08:28more you're also going to be saving

1:08:29tokens. So it tried it again. It worked

1:08:32now that the API is correct. And then it

1:08:35said, "Let me save this setup to

1:08:36cloud.MD MD so I don't have to figure

1:08:37out again and now that's all saved. If I

1:08:39go back into our files and we open up

1:08:41our cloudmd we can now see here that it

1:08:44says research and web search default

1:08:46tavly first the API key lives in thev

1:08:49here is the off method here is the

1:08:51endpoint and here is how all that works.

1:08:53So if we were to clear the memory right

1:08:55now and then ask it to search the web it

1:08:57would actually use tabi and that is how

1:08:58you iterate on your system. Now, the

1:09:00other thing I wanted to talk about real

1:09:01quick is that we right here saw the term

1:09:04MCP, which stands for model context

1:09:06protocol. If you guys have never heard

1:09:08of that, it's basically it's very

1:09:10similar to an API. APIs have lots of

1:09:12different endpoints that can be hit. And

1:09:13MCPs have a lot of different tools that

1:09:15can be hit, but essentially it's the

1:09:17same theory of how can we connect cloud

1:09:19code to QuickBooks or Google Sheets or

1:09:21Gmail or SharePoint. It's all about

1:09:24connecting to other tools via MCP or API

1:09:27or you might even hear later on in this

1:09:29video something called a CLI. They're

1:09:31all basically just methods of connecting

1:09:33to different tools. They all have little

1:09:34bit of different pros and cons, but I

1:09:35don't want to get into the weeds of that

1:09:36right now. There's no reason getting

1:09:38overwhelmed about that. We're just

1:09:40focused on let's connect to the tools

1:09:41that we use every day to make ourselves

1:09:43faster. So, that is going to do it right

1:09:45now for number 10. Let's move on to

1:09:48permissions and settings. So back in

1:09:51this example, as you guys saw, we have a

1:09:54couple things to think about. The first

1:09:55thing is the actual permission mode that

1:09:57we use inside of cloud code. So if I go

1:10:00into here, you can see that we have

1:10:02different modes. We have auto mode,

1:10:03which is the default. We have manual

1:10:05permissions, we have accept edits, we

1:10:07have plan mode, and then we have bypass

1:10:09permissions. And if you don't see bypass

1:10:11permissions, you would go into here,

1:10:13you'd go to your settings, and then you

1:10:14would go to cloud code. And if you keep

1:10:17scrolling down, there's going to be

1:10:18something right here, allow bypass

1:10:20permissions mode. This allows Claude to

1:10:23just do everything. It will never stop

1:10:25and say, "Hey, is this okay? Can I do

1:10:27this?" It will just do everything. And

1:10:29um

1:10:31hence the name bypass permissions,

1:10:33dangerously skip permissions. So,

1:10:35typically being on auto mode works just

1:10:37fine, but there might be some times

1:10:38where you do want it to be able to

1:10:39bypass, but there are some things that

1:10:41you want to be explicitly very careful

1:10:43of. We've all heard those horror stories

1:10:45of agents deleting databases. We

1:10:47actually had something internally where

1:10:48an agent accidentally sent out an email

1:10:50to like 150,000 people with a discount

1:10:52code that wasn't supposed to go out. The

1:10:54reason that happened though wasn't

1:10:55because of a permissions mode like down

1:10:57here. That was because the agent had

1:11:00access to so many tools. And really what

1:11:03you should be doing is like let's say

1:11:04you connect your agent to your CRM and

1:11:07your agent only needs to be able to read

1:11:09it. It doesn't need to be able to delete

1:11:10records or update records. So there's no

1:11:12point in giving the agent that actual

1:11:13tool to be able to do so. So that's

1:11:16where you might want to think about like

1:11:17API keys with scoped permissions and

1:11:19things like that. And what's nice about

1:11:21thinking about scope permissions is it's

1:11:22usually pretty userfriendly on the third

1:11:25party tool side. So here's 11 Labs for

1:11:28example. If you guys don't know what

1:11:29this is, it basically is just like AI

1:11:31voice really. And you can also do like

1:11:33you know you can create voice agents and

1:11:35phone agents and things like that. So,

1:11:36if I come in here to developers and I go

1:11:38to my API keys, you can see I've got a

1:11:40bunch of API keys here. Let's say I'm

1:11:41going to create a new one. Let's say I'm

1:11:43creating this API key and I just need it

1:11:46for sound effects. That's it. And for

1:11:48some reason, it's really high risk where

1:11:50if this API key does other things, it

1:11:52would be bad for the business. So, I

1:11:54would name this key so I am aware of

1:11:56like what it does. And it's always good

1:11:57to name your keys specifically because

1:11:59if you're giving keys to different

1:12:00people on your team or for different

1:12:01agents, you want to see what agents are

1:12:03using what keys, how often, and how much

1:12:05they're spending and how much people are

1:12:06using it. So anyways, that's what we're

1:12:09doing here. Demo sound effect. Right

1:12:11here, you can see that there's an option

1:12:12to restrict this key, which means I can

1:12:14restrict it on credits. So it can only

1:12:16spend a certain amount. So let's say I

1:12:18wanted to only let it spend 10 credits

1:12:21um on a certain, you know, time period,

1:12:23then I could do that. We can also

1:12:25restrict the endpoints. Endpoints is

1:12:28just a fancy word for like what it can

1:12:30do, capabilities, tools. So let's say

1:12:32for this key, I only wanted it to be

1:12:35able to do sound effects. So I would

1:12:36say, okay, you can access that. But for

1:12:38everything else, no access. You know,

1:12:41maybe I want it to be able to read all

1:12:43of this so it can read dubbing, read

1:12:44agents, read projects, read all this.

1:12:47But besides that, the only thing it can

1:12:49actually physically touch and manipulate

1:12:51and do is sound effects. And that's how

1:12:53you give your agents a key that you can

1:12:56actually sleep at night and feel 100%

1:12:57confident that nothing wrong will

1:12:59happen. Because if those horror stories

1:13:01where people delete a database with an

1:13:03agent, if they would have just

1:13:04restricted the ability to delete, then

1:13:06that never would have happened. So

1:13:08that's what I want you guys to be

1:13:09thinking about like this. Would you give

1:13:11your a new hire a credit card and say,

1:13:13"Hey, you can like don't spend anything,

1:13:15but with this card you could." No. That

1:13:18just makes no sense. So treat this once

1:13:19again, what did I say earlier in this

1:13:21video? You are a manager. Just think

1:13:23about this as this is a human. How would

1:13:26I interact with them? What access would

1:13:27I give them? What permissions would I

1:13:29give them? And a lot of the overwhelm

1:13:31about how you think about these agents

1:13:32might just disappear when you shift your

1:13:34mind to think like that. Okay. Now, the

1:13:36other thing we have are some other

1:13:37settings like local settings. So, I'll

1:13:39show you guys a quick example of that.

1:13:41If I go to my back to my Herku project,

1:13:43which is like I said is my main one that

1:13:45I kind of operate in and I go to my

1:13:48umcloud and we talked about our settings

1:13:50living inside of this.cloud folder. So

1:13:52I'm going to open up my settings. What

1:13:53we see here is some environment

1:13:55variables. So cloud code agent teams

1:13:58one. I'll talk about this later in the

1:13:59video during the agent team section, but

1:14:00that's an environment variable. I've

1:14:02also got some other things here that I'm

1:14:04going to blur out. But then I have

1:14:05permissions. And this is me saying

1:14:07here's what you're allowed to do cloud

1:14:09code. You're allowed to do bashes. So

1:14:11just like kind of running these

1:14:12commands. Web search is allowed. Web

1:14:14fetch, edit, write, mcps, mcps, glob,

1:14:17skills, all of this is allowed. But

1:14:19here's what's important. Here are the

1:14:21things you can never do. I I have this

1:14:23project set up so it can never remove

1:14:24anything or delete anything or change

1:14:26these directories or anything here that

1:14:28I would consider risky. And so what I

1:14:30would do is I would put my cloud code on

1:14:32bypass permissions mode. But I felt

1:14:34comfortable about it because it could

1:14:36never do anything that was actually

1:14:37risky. So, that's how I played with the

1:14:39settings in order to actually help me

1:14:40out a little bit with, you know, access.

1:14:42Now, the cool thing about that is I

1:14:44don't understand like a lot of those

1:14:46words, but I just said, "Hey, I'm trying

1:14:47to change my permissions and my settings

1:14:49inside of this project. Here's X, Y, and

1:14:52Z things I want to never happen. Can you

1:14:53help me update the settings file so that

1:14:55you physically cannot do those things?"

1:14:57And then it just worked for me.

1:14:59Obviously, I wanted to test it out a

1:15:00little bit cuz you don't always just

1:15:01take the output, like I said, and just

1:15:03fully blindly trust it, but that's what

1:15:05I did. Now, I will say though, pretty

1:15:07much if you come in here and you go to

1:15:09auto mode, it's going to be really

1:15:10solid. Auto mode was new. So, when I

1:15:13designed that settings file, auto mode

1:15:15didn't yet exist, but auto mode's pretty

1:15:16solid for you guys. When you've opened

1:15:18up Cloud Code and you've been following

1:15:19along with this video so far, you've

1:15:21probably been using auto mode, and

1:15:22that's going to be just fine. So, the

1:15:24settings thing is something to just be

1:15:25aware of, and later you might want to

1:15:27tweak that as you get a little more

1:15:28advanced, but right now, you're probably

1:15:29fine just sticking on auto mode. And

1:15:31then another setting that I want to

1:15:33bring to your guys' attention is down

1:15:35here. So obviously we have the model,

1:15:37right? And you can enable fast mode,

1:15:38which I basically never touch. It costs

1:15:40more and it's faster and I typically

1:15:42don't care about speed too much. We have

1:15:44the different models to choose between,

1:15:45right? So sonnet, haiku, opus, fable.

1:15:48And then what we can also do is the

1:15:50effort, which is pretty interesting. So

1:15:52effort lets you choose between faster

1:15:54and smarter. So, if we move the effort

1:15:56down to medium or low or extra high or

1:15:59max or ultra code, we can play with the

1:16:01effort levels. Now, I will be honest

1:16:03with you guys, I pretty much keep all my

1:16:06models just on high. I don't really like

1:16:08to play with it too much. Typically,

1:16:09what I like to do is switch between

1:16:11models rather than switch between

1:16:12effort. I think that for the most of the

1:16:14knowledge work, you really don't need to

1:16:16tweak it too much. But I will say the

1:16:19unit economics of understanding what is

1:16:22the right model for the specific task in

1:16:24front of you because you're also

1:16:25optimizing for cost is a very important

1:16:27thing to be thinking about as you get

1:16:29more advanced. Right now we're learning

1:16:30the fundamentals. It's not a big deal

1:16:32but as you get more advanced it is

1:16:33interesting to think about this kind of

1:16:34stuff. Look at this for example. We have

1:16:36this chart with GBT 5.5 which is

1:16:38currently OpenAI's best model but you

1:16:40know things move quick. We have Opus 4.8

1:16:42and we have Fable 5. We're showing all

1:16:44of these models on different effort

1:16:46levels. And on the y- axis, we see the

1:16:49score. On the x-axis, we see the average

1:16:51cost per task. Now, what I want you to

1:16:53pay attention to here is look at GBT

1:16:555.5. As effort increases, quality

1:16:59doesn't increase. Score doesn't

1:17:00increase, but cost certainly does. So,

1:17:02that's one where it's like, okay, why

1:17:03would you ever increase the effort? But

1:17:05here on Fable, as you increase the

1:17:07effort, the benchmarks show that your

1:17:10score meaning improves. Now, me

1:17:12personally, when I use Fable on high or

1:17:14max, what I've actually found is that

1:17:16it's slower and it overthinks and it

1:17:18overreasons for no reason. So, I like

1:17:20Fable on high. Opus 4.8 on extra high

1:17:23does significantly feel better, but it's

1:17:25slower and more expensive. So, it's it's

1:17:27kind of a balancing act to play, but

1:17:29looking at these benchmarks is

1:17:30interesting and it's good to know that

1:17:31you have that lever to tweak. But, like

1:17:34I said, for the majority of my knowledge

1:17:35work that I'm doing, I'm just leaving

1:17:37the models on high and that has been

1:17:39working pretty well for me. Okay, now

1:17:40let's talk about something really fun,

1:17:42which is privacy and your data. So, hey

1:17:46Claude Code, can you just do some quick

1:17:47research for me? What does Enthropic say

1:17:49about our privacy? You know, when I'm

1:17:51talking to Claude and the data goes to

1:17:53their servers, like what are they doing

1:17:54with it? Are they training on me? Should

1:17:56I be what should I be careful of? You

1:17:58know, how do I stay safe, especially

1:18:00inside of my own organization and at

1:18:01work? So, the reason I wanted to bring

1:18:04this up is because it is a big question,

1:18:05right? because these closed source

1:18:09models so basically meaning you know

1:18:11OpenAI Enthropic Google those are closed

1:18:14source models because we as consumers

1:18:16don't actually get to own the weights of

1:18:19those models you know we can't install

1:18:21them locally and we can't tune them or

1:18:23anything like that. Now open source

1:18:24models are the ones that you can

1:18:26download locally and tune them but

1:18:28they're not nearly as good as the closed

1:18:30source models. Now the benefit with open

1:18:32source models is that you can own them

1:18:34locally. So your data, your conversation

1:18:38never leaves your home because it stays

1:18:40on your computer. But every single time

1:18:41that I send a message to Claude or

1:18:43OpenAI or Google, it goes to their

1:18:46servers wherever they're running all

1:18:47their compute and then it gets processed

1:18:49and then an answer comes back. And

1:18:51that's why they have these massive, you

1:18:52know, data centers. But the point I'm

1:18:54trying to make here is for the company

1:18:55that you work for or your own business,

1:18:57you should not be sending over private

1:18:59sensitive data. For my case, it's really

1:19:02not that much, right? because I'm making

1:19:03content and I am doing stuff like that.

1:19:05I'm not directly handling people's

1:19:07credit card numbers or their personal

1:19:08identifiable information. But I wanted

1:19:11to bring this up because for some of the

1:19:13clients that I've worked with, they had

1:19:14very strict requirements. You know, like

1:19:16we couldn't do anthropic stuff for them.

1:19:19We had to do on-remise deployments and

1:19:20we had to think about security in a much

1:19:22different way as far as like encryption

1:19:23and stuff like that. But that's why I

1:19:26wanted to bring this up. I'm not going

1:19:27to dive into deep deep deep deep about

1:19:29like encryption and on-rem right now in

1:19:30this course at least, but I wanted to

1:19:32bring this up so that nobody here gets

1:19:34in trouble because they watch this

1:19:35course and then they're all of a sudden

1:19:37putting a bunch of company documents and

1:19:38legal contracts into AI when their

1:19:41company would get them in big-time

1:19:43trouble for doing something like that.

1:19:44So, be safe. Think about what's allowed

1:19:47inside of your industry, inside of your

1:19:49organization before you start chucking

1:19:50in documents into something like Claude.

1:19:53Aha. And look at this. I'll research

1:19:55this with Tavly first per hour clawmd

1:19:57setup. But anyways, I'll just go over

1:19:59real quick what this says. It depends

1:20:00entirely on which cloud you're using on

1:20:02consumer plans, which is, you know, the

1:20:04free pro max, not a team plan. As of the

1:20:07policy change effective October 8,

1:20:09training is opt-in, but it's a forced

1:20:11choice. So, you basically had to toggle

1:20:12this on or off. You can check this right

1:20:14now by going to your settings, going to

1:20:16privacy, going to help improve Claude,

1:20:18and then you can toggle it on or off.

1:20:19Now, just because they claim that

1:20:21they're not going to train models on

1:20:22your data, they still have that data.

1:20:25So, you know, that's why even if you

1:20:27have that toggled off, check with your

1:20:29own organization. Cool. So, now that

1:20:31we've covered probably, of course, the

1:20:33most fun and exciting thing of this

1:20:34course, let's just talk more about

1:20:36connecting your own tools because that's

1:20:37how this stuff gets super super super

1:20:40powerful. Okay, so in my Herk 2 project,

1:20:43which I know I've mentioned a lot, this

1:20:45is my AI operating system. So, it's

1:20:47connected to every tool that I actually