Give Me 50 Minutes, I'll Give You 1000+ Hours Of Claude Code Knowledge (2026 Guide)
CChase AI
컴퓨터/소프트웨어AI/미래기술
스크립트
00:00:00This video is going to replace over 1,000 hours of trial and error inside of Cloud Code
00:00:04and tell you what you need to focus on, whether you're at the beginning stage,
00:00:08intermediate stage, or pro. I'm going to cover everything from the desktop app
00:00:12to how to prompt, MCPs, CLIs, and even more advanced topics like graph engineering and
00:00:18creating your own agentic OS. And by the end, you're going to have a complete roadmap on how
00:00:22to master the most powerful AI tool in the game today. There is a ton to cover, so let's get
00:00:27started. Now, the first thing we need to talk about in the beginner section is where do we run
00:00:32Cloud Code? Because this can actually be a confusing question. You technically can run Cloud Code from
00:00:37the web app in the cloud. We also have the desktop version of Cloud Code. And of course, we have the
00:00:43terminal. Which of these three should you be using, especially if you're just getting started? Well,
00:00:48if you'd asked me this question a few months ago, I would have pushed you to the terminal or something
00:00:51like VS Code with the Cloud Code extension. But these days, the desktop app has gotten a
00:00:56lot better. And if you are someone who is not coming from a technical background, this is all
00:01:01brand new to you, I would suggest using the Cloud Code desktop app. Yes, the terminal will always be a
00:01:07powerful option for those of you who are more technically inclined. But if that's not you, don't
00:01:11feel pressured into using it if you're not comfortable. You really aren't missing out these
00:01:16days. And the desktop app is getting features that just aren't available inside of the terminal.
00:01:21Things like voice mode, things like browser automations. And there's a lot of quality of life
00:01:25things when you work inside the desktop app, like inline artifacts, and generally a better user
00:01:31experience for those of you who have never touched the terminal before. Not to mention, we can also use
00:01:36the terminal when we're inside the desktop app. So it's not like it's an either or thing. Now to
00:01:40download and install the Cloud Code desktop app is super easy. Just search for Cloud desktop app,
00:01:44you'll hit the first link, and then you're going to download the installer and run it. So now you have
00:01:48the desktop app installed. And let's do a speed run through this thing and what you need to actually care
00:01:53about. Now in terms of settings, you come over here to the left and hit customize. Let's then go to the
00:01:57general tab. And I want you to take a look at instructions for Cloud. Take a look at mine. It's
00:02:01blank. Yours probably should be blank too. These are global instructions. By global, this means it applies
00:02:06to everything, all your projects and all of your prompts. So if I put something in there, like I want
00:02:11all of your responses to me to rhyme, well, guess what's going to happen? Every single response is going
00:02:16to rhyme. Stupid example. But the point is, do you actually have something that's relevant enough to
00:02:21every single chat you're ever going to have with Cloud that we should spell it out here? Maybe,
00:02:26but that's a very high bar. And if what you're thinking about adding here doesn't meet that bar,
00:02:31I would leave it blank. Next, go to capabilities. Make sure tool access mode is load tools when needed
00:02:36and turn everything else on this page on. In the Cloud Code tab, I would keep everything in general turned
00:02:41on. It has some settings that are purely personal preference. And in terms of local sessions, I would
00:02:47turn all of these on as well. Now, the exception is when we come down to pull requests. If you don't
00:02:52know what a pull request is, just leave this off. It's a little more advanced. And for Cloud in Chrome,
00:02:58which is a Google Chrome extension where Cloud can control things in your browser, I turn this on as
00:03:03well, but understand you need to download the Google extension if you want to do that. Now we'll go into more
00:03:08detail when it comes to skills, connectors and plugins and memory later. So don't worry about that just yet.
00:03:14Now over here, we have artifacts. You don't really need to worry about artifacts, honestly, inside of
00:03:18Cloud Code. Routines have to do with automations we run and we'll go deeper into automations later.
00:03:23And over here, this is just previous chats we've had with Cloud Code. Now if you go ahead and hit new,
00:03:29it should bring up a page that looks like this. And let's break down what we're looking at. So obviously,
00:03:33right here, we have our little chat window and then we have these four things up here.
00:03:37Local, something that says 1000, main, work tree, and then a little plus button.
00:03:42Local is just you telling Cloud where it's actually going to be running. If you don't know what any of
00:03:47these things are, Cloud, remote control, WSL, SSH, you should be on local 99.99% of the time.
00:03:53Everything else here besides WSL is all about being able to use Cloud when you're away from your computer.
00:03:58So if you're in the beginner stages, that's not going to be you. Next, right here where it says 1000,
00:04:03that is just the folder I am working in. So if I click on this, I can open a new folder and I can
00:04:08pick any one inside of Cloud, well, inside of my computer that I want to work within. So you could
00:04:13create a folder on your desktop that just says Cloud Code projects. And that's what you work in.
00:04:18Everything you do here inside of Cloud Code will live in that folder. So you just have to pick one.
00:04:23Next, we have main and work tree. This has to do with Git, a little bit more of an advanced
00:04:26topic. So if you're a beginner to oversimplify this completely, Git just has to do with saving your
00:04:32work. So if you don't know what Git is, I'm not going to turn this into a Git lesson.
00:04:36Just keep it on main and don't check work tree. And then we have this plus button. This allows you
00:04:40to add an additional folder. So your work essentially gets copied in two places.
00:04:44Next, we have permissions. If I click on auto, I will see five different modes. What are these modes?
00:04:48This has to do with us telling Cloud Code what it has permission to do with or without our consent. So
00:04:55on one end of the spectrum, we have manual. This means it's going to ask you all the time. Can I do
00:04:58this? Can I do that? Can I edit things? On the other end of the spectrum, we bypass permissions.
00:05:03It can do whatever it wants, download things, install things, delete things, edit things. Kind of scary.
00:05:07Now in between those, we have auto, which is essentially bypass permissions, except there is
00:05:12a classifier that takes a look at the commands that Cloud Code is running and decides if they're dangerous
00:05:17or not. And if they're dangerous, it will stop them. So this is the default for a reason. You should be
00:05:22sitting here all the time. The only other one we're going to play around with is plan and we'll go into
00:05:26that more later. This plus button lets you add things so you can add screenshots, that sort of
00:05:30thing. And then you have a microphone. And then over here on the right, we have the model, we have
00:05:34the effort level, and then we have our context window. What model should we be using? Well, it depends
00:05:39on what plan you're on. If you're on the $20 a month plan, you really aren't going to be able to
00:05:42use Fable. It's just going to burn too much usage. And so you're going to have to sit on Opus.
00:05:46If you are on a max plan, 5X, 20X, so $100, 200 bucks a month, I would suggest working in Fable
00:05:51most of the time. It is the best model by far. Now, the problem is the usage though,
00:05:58because if we click this little thing over here, you know, you see context window. We'll talk about
00:06:03that more later. We have a number of different limits. We have a five hour limit, a weekly limit,
00:06:07and a Fable limit. So only half of our usage per week can be dedicated to Fable. So we don't want to
00:06:14burn too much Fable too early. So what we need to really think about is our effort level, which can
00:06:21be anywhere from low all the way to ultra code. The more it thinks, the better it performs, but it's
00:06:27not linear. If I go from extra high to ultra code, does that mean I'm going to get like a 10X increase in
00:06:35the performance? No. It might be like a 1% increase, but you might pay 5X more. In fact,
00:06:39most of the problems you're trying to solve, especially if you're more in the beginner stage,
00:06:43don't require anything beyond medium. In fact, you could probably get away with low and just do fine.
00:06:48Me personally, I sit on Fable medium most of the time, unless I'm dealing with something rather
00:06:52complex or I'm about to hit a usage reset and I know I can just burn it. So for today, we're going to
00:06:57sit on Fable 5 medium because it's a nice, happy middle ground. So lower effort means less usage,
00:07:03doesn't perform quite as well, but generally it does more than enough.
00:07:07Now let's talk about prompting. Anytime we are going to start prompting some new project,
00:07:12I highly, highly suggest you go into plan mode. Now, why is that? Well, plan mode is going to allow
00:07:18Claude and I to have a conversation to make sure we are on the same page before it goes and execute
00:07:22something. And more importantly, not only are we going to have a conversation, it's going to ask you
00:07:27questions. Because many times, and this is very true if you are coming, A, from a non-technical
00:07:33background, and B, if you're trying to do some sort of project that is not in your typical domain of
00:07:38expertise, the issue you're going to run into when using AI is that there's just so much that you don't
00:07:43know. And there is so much that you don't know that you don't know. These unknown unknowns are a real
00:07:49problem. And the only way you're going to be able to figure those out is by having Claude code pretty much
00:07:54bring them up for you to shine a light in these dark spaces that you didn't even know exist.
00:07:58And plan mode is the simplest way to solve that problem, because it's going to force it to ask
00:08:02us questions. So anytime you're like, have a fuzzy idea of I'm on step A, I want to get to step Z,
00:08:07and I don't know what to do, we're going to go into plan mode. Now, when I prompt Claude code,
00:08:12this used to be a big thing, kind of back in the day, the last like year or two, and this has kind
00:08:15of gone away a little bit. But you still have people out there who think there's some like magic prompt
00:08:19you need to do, it needs to go in this specific format. We're like, here's the goal,
00:08:22and here's the context. And here's how I want you to act. You don't need to do that.
00:08:25What you need to do is you need to buy yourself a microphone, you need to turn the microphone on,
00:08:29and you just need to give Claude code a stream of consciousness. So for this plan for this website,
00:08:33we're going to build, we're going to say it's just a website for this fake AI analytics company,
00:08:41and we'll call it Lighthouse. And so that's all I'm going to do. And I'm just going to ramble.
00:08:46Like there's no plan here. It's gonna sound like this. So I want to create a website for a fake AI
00:08:54analytics company called Lighthouse. I don't really know what I want to be on the website,
00:09:02although I know I want sort of the call to action at the end for them to book a call with us. So that's
00:09:07going to be kind of the call to action. I think in terms of who the target audience is, let's say it's
00:09:12going for like small startups. So other than that, I don't really know what I'm missing. Just go ahead
00:09:18and ask me whatever questions you think are relevant that I haven't thought of. Now what I want you to pay
00:09:22attention to is that last bit where I said pretty much, just start asking me questions and things I
00:09:27haven't thought about. Now, since it's in plan mode, it's kind of already going to do that. But anytime you
00:09:31prompt Claude, you can always add that at the end of your prompt. Like, what am I not thinking about?
00:09:37What sort of questions do you have for me? You know, and this is going to get you again,
00:09:41and sort of that back and forth with Claude code, because at any point it can bring up something like
00:09:44this where it asks you questions. So what's the purpose of this fake site? Well, we'll say it's for
00:09:49design slash dev practice. What does Lighthouse actually do? We're going to say product analytics and AI
00:09:56insights. How big should it be? Let's do a landing page like that. And then what does vibe should the
00:10:06design have? We'll go with clean light SAS. Next, it asks us what tech stack should we want the site
00:10:12to be built with plain HTML, CSS slash JS, next JS plus tailwind or ash plus tailwind? Do you know what
00:10:18any of those are? Genuinely, do you have any idea what those are? Do you even know what a tech stack
00:10:23is? If your answer is no, then what should we do? Should we just go with the recommended?
00:10:28Yes, but no. So here's a problem a lot of people run into is they're just going to be like, I don't
00:10:33even care what a tech stack is. Sure, plain HTML, go recommend it. And they just click this.
00:10:38And the thing is, these models are so good is that you're still going to get a pretty good output.
00:10:41The problem is, is when you repeat that behavior of not understanding the question and just hitting
00:10:45recommended again and again and again and again. The main problem is there's no differentiation
00:10:50between you and any guy on the street who I could put in front of your computer and have
00:10:54them do the exact same thing. Like where is any moat whatsoever for what you do? Like you're very
00:10:59replaceable. But even more importantly, you're not learning anything. And as good as these models are,
00:11:04eventually you're going to have a pet project that's very unique that maybe Claude doesn't know the
00:11:08best way to attack it. If you have spent all of your time learning Claude code by never actually
00:11:13learning anything and just hitting recommended, recommended, recommended, you're going to have
00:11:17zero idea of what's actually happening. Now you don't ever need to learn code again, but you do need to
00:11:23begin to learn AI software engineering fundamentals, like big picture stuff, like how these building
00:11:30blocks come together. And the only way you're going to do that is when you hit questions like this,
00:11:34you don't just hit recommended, you say something like this. Can you explain what a tech stack actually
00:11:40is? I don't really know. I don't really understand these options as well. So can you just give me a
00:11:44quick breakdown of what I'm looking at. And that's it, you're just going to tell it to explain the
00:11:49question a little more detail. And if you do that over and over and over again, for weeks, months and
00:11:53years, you're eventually going to build an actual foundation, you're not going to be this like,
00:11:58caricature of a vibe coder. This is extremely important. And this is the sort of mentality of the
00:12:02take with prompting and talking with Claude code if you actually want to like, learn anything,
00:12:07because it's going to make you better. So we'll do pricing product features. Sure.
00:12:12What should the book call CTA work? We'll do a fake booking form.
00:12:21It's off to the races. And so we can see here, it broke down the what is a tech stack question,
00:12:27and then goes into a little more detail about what everything is. Again, you don't need to become an
00:12:31expert at all these things. But as you do this again and again, and you kind of go down the rabbit
00:12:35holes when they make sense for you, you generally are going to start like putting the pieces together.
00:12:39Like, this isn't that complicated. Like coding is difficult. No one's asking you to become like a
00:12:47true software engineer in that sense. But you can learn what a tech stack is, you can learn what these
00:12:52different languages sort of are and like, what we should do in each sort of use case. Now once it
00:12:56proposes the plan, you will see it populate over here on the right hand side. Now what's cool about
00:13:01plan mode inside the desktop app is when it brings us up at any time, I can sort of select something.
00:13:07So let's say the audience, let's say I didn't just want small startups. Let's say I was also looking
00:13:13for add, I wanted to add medium sized companies as well. So if I do that and I hit comments, it now
00:13:22starts adding sort of like comments here. So I could add more and more comments and go down the line and
00:13:27kind of make these like almost little notes to the plan it's created. I also have the ability to add
00:13:31any prompts I want. And then I can at any point say, hey, let's go ahead and revise this. So now it's
00:13:37added medium sized companies to the audience as well. And once I like the plan, I can either hit accept
00:13:43or accept with auto mode. So make sure you hit accept in auto mode or else it's going to start running it
00:13:48inside of manual mode and we don't want to do that. So we'll do accept in auto mode. And now it has gone
00:13:52ahead and built a webpage for us. And it's at this point, we are going to move into the intermediate
00:13:57sort of skills and tips and tricks of when it comes to cloud code. But before we do that,
00:14:01a quick word from today's sponsor me. So just yesterday, I released a completely updated version
00:14:06of my cloud code masterclass. We go way deeper on all the topics we sort of touch on at a surface level
00:14:12here in today's video. And it is a perfect place for you if you're someone who is non technical and
00:14:17really just wants to learn how to master this amazing tool. So if you want to get your hands
00:14:22on it, you can find it inside of chase AI plus there's a link in the pin comment.
00:14:27So let's build our website. But what I actually want to focus now is on this guy down here,
00:14:31this little circle. Remember this that showed our usage? Well, it also shows our context window now.
00:14:37And if we click on our context window, it gives us a very specific breakdown of what is actually
00:14:41filling it up. Now, the context window is a very important metric we always need to keep track of
00:14:47for a few reasons. Now, the first reason has to do with performance. But to understand this,
00:14:51you need to understand tokens and context. So to keep this oversimplified, every single word you send
00:14:57to cloud code and every single word you get back is considered a token. A token is the currency of large
00:15:03language models. And the context window is the budget. So we have a budget of 1 million tokens we can
00:15:09essentially spend in each session. And so far, we have spent 156k. Now, that sounds great. We've
00:15:16only used 15%. I can use another 844,000. Well, sort of. The issue is as this context window fills up,
00:15:25in fact, the performance of cloud code gets worse. Think of it as it just having too much stuff in its
00:15:31brain. If we are sitting at 800,000 tokens over here, out of 1 million, and I ask it questions about
00:15:38what's been going on in the last 800,000 tokens, it's going to struggle, especially if we're asking
00:15:43it questions that sort of happened in the middle. So because of this, we always want to keep an eye on
00:15:49our context window, because we don't want the performance to decrease. And this decrease is
00:15:53somewhat linear, and there isn't an exact science to it. So rule of thumb, as we sort of hit like 30%,
00:16:0040%, certainly 50%, 500k tokens, you definitely want to sit there and ask, do I need to continue
00:16:06the session? And really, for me, that's at like 30%. And while you can see here, the context window
00:16:11does get filled up with things besides our messages, including things like system tools and skills,
00:16:15the messages are the big thing. Let's say you hit 30% of your context window, or even 50%. What are your
00:16:22options? Well, we really have one option, and that's just to start a new chat. Okay, we're just going to
00:16:31start a new chat. Now, there's a couple ways you can do this, we can do commands like forward slash
00:16:39clear. If I do forward slash clear, this is going to get rid of everything, and we're going to start
00:16:43completely fresh, brand new context window, and we're going to have top performance. Now, the other option
00:16:48is to do slash compact. If I do slash compact, what Cloud Code is going to do is going to take a look
00:16:53at the entire conversation history we've had, it's going to create a new summary, and then it's going
00:16:57to start a new chat with that summary. Now, your other option is just to go over here and hit the
00:17:02plus button. And this will also start a new chat inside of that same folder. And then I can reference
00:17:06the old chat at any time. Now, the scary thing is, especially if you've come from primarily the web app,
00:17:11where the conversation you have is sort of all you have. And if you get rid of the conversation,
00:17:16forgets everything, remember, what are we doing here, we are working inside a specific folder,
00:17:20we're inside folder 1000 creating our website. So if I get rid of this entire conversation,
00:17:26it can still take a look at the files at all the code we wrote and understand what's happening.
00:17:31So starting a new chat isn't really starting from zero. And in that case, you really have nothing
00:17:36to fear. If you're filling up the context window, and you're scared of starting over, it's okay,
00:17:40just start over and worst case, just have it create that summary with slash compact. But at this point,
00:17:45we're only at 16%. So we are okay. Now let's talk a little bit about this website. This website
00:17:53is ugly. This website is actually pretty lame and generic. And by the way, I'm looking at this inside
00:18:00the browser pane inside of Claude Code desktop app. So I can actually do a lot of stuff here. Like if I
00:18:05open this up, I can select certain things, I can put comments on these just like in plan mode,
00:18:11which then go into a prompt, I can actually annotate things and say like, Hey, trash, you know, and it
00:18:17will also add it to a comment. So really easy to sort of do like micro edits here. If you want to, but big
00:18:24thing we need to solve for just like this website, why does this look so terrible? Well, this looks pretty
00:18:28awful. Because a we didn't give it enough context for what we wanted to look like, we didn't give it any sort of
00:18:35inspiration, we didn't give it screenshots. All we said was we wanted like a clean SaaS product. And
00:18:39one of the themes of sort of the intermediate section is context engineering, your ability to
00:18:44give Claude Code access, not just to sort of your thoughts and your vision, but for external tools and
00:18:50skills so that it can do a better job. So sort of a two part process here for solving this problem.
00:18:55This is where we're going to dive into the idea of skills. Skills are probably the most important
00:19:00thing you need to understand and master when it comes to leveling up your Claude Code performance.
00:19:06Now skills at their most reductive state and it gets a little bit more complicated are simply prompts
00:19:11that tell Claude Code to do a specific thing in a specific way. For example, there are a ton of front
00:19:17end design skills that are all about making better websites that are essentially just prompts that tell
00:19:22Claude Code, hey, when you're creating this website, avoid certain gradients, avoid things that look like
00:19:27AI slop, do this, do that, right? It's just giving it specific instructions. That's all skills are.
00:19:32So how do we actually get skills? Well, we can find them inside of the Claude app. So if I go to customize,
00:19:37move this over here, we go down to skills. And you can see some of the skills that I have right here,
00:19:44we also have plugins, which also can kind of be skills, it's sort of a gray area, especially app,
00:19:49we talk about skills versus plugins, you can kind of think of them as the same thing. Plugins can
00:19:54include multiple skills, but it's kind of arbitrary. So for example, if I go to plugins, and I go to
00:19:59browse, first of all, what am I going to see, I'm going to see the anthropic official plugins, this
00:20:04includes the front end design plugin, which is simply the front end design skill. So if I install this,
00:20:10mine's already installed, it will then add the front of design skill to Claude Code. And this is the
00:20:15actual prompt for the exact front of design skill. This is an official skill, like you can just go see this,
00:20:19this is on the official Claude Code GitHub. If I copy this whole thing, and I go back inside here,
00:20:24and I paste this into the prompt, that's just like I'm using the skill. But obviously, you wouldn't do
00:20:30that every single time you want to do something related to front of design. So instead, we simply
00:20:34add the skill, like I showed you in the UI, and we just do front end design, right, I can do forward
00:20:40slash, and now this is invoked. Now that's the same as if I just copied and pasted that whole thing.
00:20:44Now I don't only have to do the forward slash, I could just use natural language and say like use
00:20:49front end design skill. And it's smart enough to actually know it needs to call that. Now the
00:20:53confusion can happen is if you have multiple front end design related skills. And if you do that, then
00:20:58if you just say like, hey, I'm building a website that it might not know which one to pick. So if you
00:21:02have multiple skills that all kind of do the same thing, you need to nudge Claude in the right
00:21:05direction. Now, the most important skill you can actually add right now, and this goes beyond web design,
00:21:10is if you go back to plugins, and you go to browse, and you go to skill creator, this is the most
00:21:16important skill you can add, because this is a skill that allows you to create other skills and includes
00:21:21things like measuring skill performance, it runs tests, it runs evals, it does benchmarks, and we'll
00:21:27talk about this more in a little bit. But obviously, as we look through here, there isn't that many skills
00:21:31to choose from. And we all know there's a billion in one skills floating out there in the world. And so where
00:21:35you will normally find skills is GitHub, kind of like you see here. So let's say for example, I wanted
00:21:40to use another front end design related skill. And let's say I was looking for the impeccable skill,
00:21:45and I found it on GitHub. Well, how do I actually install this thing? So it will tell you in the
00:21:49description how to do it step by step. But oftentimes, it's kind of a pain in the butt. What you need to do
00:21:53is when you find a skill you like is just copy the URL from GitHub, go inside of Claude code,
00:22:01paste that skill in there and then say something like add this skill. From there, it will literally
00:22:07add the skill to your repertoire. And from there, you just invoke it like I showed you.
00:22:12Now you can get pretty advanced when it comes to skills, especially when you have the skill
00:22:16creator skill. For example, let's say we finish out this video, I add a bunch of new stuff to this
00:22:21web page, I could at the end of doing all this of like, prompting it, creating the website,
00:22:26doing the additions, adding whatever I want, I could then do something like,
00:22:30let's use the skill creator skill and then say, take a look at the entire message history of the
00:22:35session, take a look at everything we've done today and turn that into a skill. So if there's
00:22:39things that you do over and over and over again, you can turn those into skills. And later, I'll even
00:22:44show you how to then turn those skills into automations. So skills are very powerful because
00:22:48they allow you to codify things that AI does. You know, one of the issues with AI is that it's
00:22:53somewhat non deterministic, right? If you ask it to do something 10 times, it might do it 10 different
00:22:58ways. It's not deterministic. However, skills allow us to be somewhat deterministic and give us more
00:23:04control on how cloud does things. So that's why they're so important. Now, like I mentioned before,
00:23:08beyond skills, we also need to add more context here. So what I'm going to do is I'm just going
00:23:12to search for some screenshots that I can add to this website to make it look a bit better. So I went
00:23:16on Pinterest and I found this image when I put in SAS landing page, thought it looked kind of cool.
00:23:20So what we're going to do is we're just going to drop this screenshot into here. And we're going to say,
00:23:25use the front end design skill to redesign this webpage. And in fact, I want you to do three
00:23:30versions of it and show me all three versions inside the browser pane that I can choose from.
00:23:37All of them should kind of be in the style, but I want it to be divergent enough that I can kind
00:23:40of see some differences. So it did exactly what we asked. If we look at this here, we can now see the
00:23:45three different versions of the website. You can see what a departure these are from what we were just
00:23:50looking at. And that was with a single skill, which was just the generic front of design skill from
00:23:55Anthropic and a pretty basic prompt alongside the screenshot. So I can take a look at this one,
00:24:03V1 full size. This one looks pretty cool. We have this V2, not a huge fan of the colors,
00:24:11although the radar looks kind of cool. And then lastly, we have V3, which kind of looks like your
00:24:17typical AI slop, to be totally honest. I really like V1. I think this is something that looks
00:24:24pretty cool. So what we're going to do is we're going to say, hey, we're just going to go ahead
00:24:26with V1. What that's meant to demonstrate is really the power of just injecting context,
00:24:31literally one screenshot, one skill, infinitely different end result. So now let's talk about
00:24:36how we can supercharge cloud code by bringing in outside tools, by connecting outside applications
00:24:42to cloud code itself so that cloud can control them. And we never even have to really leave
00:24:47the app. Now there's really three different ways to do this. The first one is by going into customize
00:24:53and heading to connectors. Some of these are super easy to connect and you might have done already
00:24:57done it already. That's something like Gmail or Google Calendar or Google Drive. This allows cloud
00:25:02code to talk with these applications, control these applications, usually with some guardrails,
00:25:06and it just happens via prompting. So if I tell cloud code, hey, go read my Gmail, it does that because
00:25:11it's been connected. Now, most of the big apps out there have some sort of connector. So if you just
00:25:16go to add and you browse connectors, chances are you will find what you are looking for. The second way
00:25:22is via plugins. And like I said, there's very much like a fuzzy line between all these things. Similar to
00:25:28connectors, if it's big, if it's popular, there's probably a plugin for it. So if I go to browse,
00:25:34you're first going to see a bunch of anthropic plugins, which really are just skills. If I go to
00:25:39partners, though, I can find something like GitHub or Superbase. I simply click on it and you'll see
00:25:44what's going on under the hood. In this case, this is the GitHub MCP. Just like with connectors,
00:25:50if I add some sort of plugin, it's going to allow me to talk to control some sort of outside application
00:25:55via cloud code. But there is a third thing that is not an MCP or plugin or connector, and those are things
00:26:01like CLIs. So we have right here the GitHub CLI. So we have a GitHub CLI and we have a GitHub MCP.
00:26:08What is the difference? Well, the difference is a little bit technical in terms of the practical
00:26:13application, what you are going to care about. In many cases, there's not a huge difference. In
00:26:19general, the CLI tends to give you more functionality than an MCP. And oftentimes the CLIs also include
00:26:26skills. So when you are dealing with some sort of outside application, AKA, you're working inside of
00:26:31cloud code and you need to talk to something else, you need to figure out if you can add it through one
00:26:36of those three ways, connectors, plugins, or CLI. Like you saw connectors and plugins, we can do it through
00:26:42here via the CLI. It's also as simple as simply telling cloud code to add the CLI. So like the GitHub
00:26:49CLI, there's an actual command if you're inside the terminal, but I could just like copy the URL.
00:26:54I can go inside of cloud code and say like, "Hey, here's the CLI for GitHub. Go ahead
00:27:01and add this CLI." And it's going to do exactly that. From there, you've pretty much given cloud code.
00:27:08Think of it almost as a skill to call on that CLI and have it do whatever that CLI is of the ability
00:27:14to do. For GitHub, that means I can create a repository. I can upload all this code we just
00:27:19created to that repository. I can edit the repository. And if we take it a step further and we think of
00:27:25something like Vercel, and Vercel, if you are unaware, is a web application that allows us to host
00:27:31our actual website. So we just created a website and we want to put it on the web and have a true
00:27:35URL. Vercel will allow us to do that. Well, instead of going to a dashboard like this inside of Vercel
00:27:40and handling all ourselves, why don't I just search for the Vercel CLI? Oh, look at that. Vercel has a
00:27:46CLI as well. And so what we could do with just those two applications is I can take a look to see if
00:27:52there's a GitHub connector. There is. I can take a look to see if there's a GitHub plugin. There is. I can
00:27:57take a look to see if there's a GitHub CLI. There is. Add any one of those. Add it with Vercel. And I
00:28:04now have a pipeline where I can take the website I've created. I can create the repo for it inside
00:28:09of GitHub and then automatically connect that to Vercel. I've essentially created an entire deployment
00:28:14pipeline from Cloud Code that I just have to talk to in plain language. It'll do everything for me.
00:28:19And the big takeaway here is that anytime you are working with anything outside of Cloud, you need
00:28:24to ask yourself, can I actually just control it with Cloud? Because chances are Cloud can actually
00:28:28control it better than you can, especially if you aren't intimately familiar with how that application
00:28:33works. Whether it's a CLI, an MCP or a connector, it doesn't really matter. You just need to pick one.
00:28:38And you also don't even have to go out and find it. For example, if I asked Cloud Code a question like
00:28:44this, "Hey, so I'm thinking about deploying this website. I've heard of things like GitHub and Vercel.
00:28:51I'm not super familiar with those. I've also heard there's CLIs or perhaps MCPs we could use. So do you
00:28:58think you can go look and see if that would make sense for hosting our website? If they do, can you
00:29:03add those CLIs if we need them? And then once they're added, can you go ahead and just set up
00:29:08that deployment pipeline and get it all properly connected?" So we prompt like that where you're
00:29:12just saying like, "Hey, I heard there's some tools out there. Maybe they have CLIs. If they have them,
00:29:16add them, run them." That's all you got to do, right? You don't even really have to be a pro.
00:29:21When in doubt, just ask Cloud Code what the best practice is and if it has a CLI. It'll go ahead and search for it,
00:29:26right? So if I run that, and by the way, I already have these installed, so it's probably going to come
00:29:29back and say, "Hey, I already installed it." All you have to do, if you've never used them before,
00:29:33is create an account and then we'll walk you through the setup. And as these agentic coding
00:29:37harnesses like Cloud Code only become more and more ubiquitous, you're going to see pretty much every
00:29:42app out there come up with some version of a CLI, a connector, and an MCP, which means again,
00:29:47Cloud runs the show. And so with that prompt, obviously it's going to walk you through how to log in.
00:29:51If you haven't done that before, it created the GitHub repo. So all the code from our website is
00:29:55essentially living in the cloud. And it also set up the Vercel connection. So I now have a live URL I
00:30:02can go to, and we can also see it right here. So this has a real URL, Lighthouse site to Vercel app,
00:30:07and I could share this with anybody. And again, did I have to go into GitHub? No. Did I have to go
00:30:11into Vercel? No. All control from Cloud Code. And because it is a GitHub and Vercel connection,
00:30:17any changes I make to the website inside of Cloud Code here, if I want those to be reflected on the
00:30:22live website, I just tell it and it does it. So this deployment pipeline is just one example of bringing
00:30:28outside tools into the Cloud Code fold. Now it's time to move on to some more advanced topics. We're
00:30:34going to touch on things like automations. We're going to talk about how we can best approach long
00:30:39horizon tasks. We'll do that by discussing things like slash goal, loop engineering, graph engineering.
00:30:44We're going to talk a little bit about model routing, how we can bring in other models like
00:30:48codecs and the GPT models into our workflow. And then we'll finish it all off by talking about
00:30:52some more custom type harnesses and UIs we can layer on top of Cloud Code, whether that's something like
00:30:57this or going with an obsidian command center type approach. So let's begin by talking about long
00:31:02horizon tasks and loop engineering. And this kind of bleeds into things like automations. So when we
00:31:07talk about long horizon tasks, we're talking about things like loop engineering and graph engineering.
00:31:11What are we really saying? What we're saying is we have some sort of task, some sort of goal we need
00:31:16to complete, but that this might be something we need Cloud Code to do an infinite amount of times.
00:31:21This might be something that runs every single day. And ideally it is something that
00:31:25not only is going to run every single day, but something that we want to be self improving.
00:31:29So for these long horizon tasks, there's really three parts. We are going to have a trigger,
00:31:37a task, and then some sort of success criteria. Now, not all long horizon tasks are loops. This
00:31:43could simply be some sort of task that you think is going to take two hours, four hours, 12 hours,
00:31:48days for Cloud Code to complete. And you don't want it to just stop every single time it fills up its
00:31:53context window. You want it to keep going and going and going until it completes that task.
00:31:57There is a built in command inside of Cloud Code that does this. It is called slash goal. Now,
00:32:03slash goal is perfect if you have a complex project that you want Cloud Code to complete and you don't
00:32:07want to babysit it throughout the entire process. However, there is one specific thing you need and that
00:32:12has to do with success. You need to be able to define success because slash goal isn't the only thing you
00:32:20pass. You also have to pass it a prompt. And that prompt needs to be what the success criteria is.
00:32:28Okay. Like you have a goal for Cloud Code to do. What do you want it to do? It's not enough for you
00:32:32to explain, Hey, I want you to do X, Y, and Z. No, like what is the end state? Because what's going to
00:32:38happen when we run forward slash goal is it's going to attempt to complete that goal with code or whatever
00:32:44it's going to do. It's going to run its first iteration. It's going to compare that iteration
00:32:49to the success criteria you defined. If it meets the success criteria, cool. It's all done. If not,
00:32:55it's going to pull up a second session, run it again. It's going to go ahead and check the success
00:33:00criteria. Did it work? No. Then it's going to run it again. Now, each time it runs, it's going to take
00:33:05a look at its previous iterations to see, Hey, what worked, what didn't, but it's going to continue
00:33:10sort of this internal loop until it reaches its goal. And this is extremely powerful. It's kind
00:33:16of similar to Ralph loops, if you know what that is. And so it's important that our success criteria is as
00:33:22objective as possible. If I'm just saying, Hey, the goal is to create a cool website
00:33:28that looks cool, right? That looks neat. How does it know what cool is? How does it know what neat is?
00:33:34How can it actually take a look at the end of every single run and say, did I complete this or not?
00:33:39So the more subjective your criteria is, the sort of worse it's going to get, not even necessarily the
00:33:44worst it's going to get, but the less likely that it's going to meet your needs. But with all that being
00:33:49said, goal is definitely a form of loop engineering, but it's one that pretty much has a definite end.
00:33:57We aren't going to do forward slash goal and expect this to run for eternity. But there are things we
00:34:03may want to do where we do want it to run for eternity. And we still want it to act in the same
00:34:07way as goal. We want it to trigger on demand or perhaps on a schedule. We have a task we want it to
00:34:12complete. We have success criteria and we want it to also be self-improving because again, this is
00:34:17self-improving in an aspect because it's always taking a look at its outputs and comparing it against
00:34:23some sort of success criteria. So what happens if we want to create something that does sort of loop
00:34:28forever, right? Perhaps it's an automation that runs every single day that we want to continuously
00:34:33improve upon itself. Now we will continue with these three steps, but next we're going to have to add
00:34:37some sort of logging phase. So now let's take a look at a custom loop for an example. We have clod code
00:34:45right here. What we want clod code to do every single day is we want it to create some sort of
00:34:50morning report for us, some sort of morning brief. I want it to go out on the web. I want it to find
00:34:54AI news for me. I also want it to check my Gmail and at the end, give me some sort of document. So clod code is
00:35:00going to take a look at YouTube. It's going to take a look at Twitter, Reddit, in my Gmail. It's going to
00:35:05grab all that information, scrape all that information, consolidate it, synthesize it,
00:35:09and give it to me in a report. And I want it to do it every single day. So how could we bring loop
00:35:16engineering fundamentals into this? Well, remember, we just have to set up these four things. So what
00:35:23is the trigger going to be? Well, let's say the trigger is every day it's going to run at 7:00 AM.
00:35:29What is the task? Well, the task is just what I described. Scrape these websites, consolidate,
00:35:34create a report. So that's set up. Well, what is the success criteria? Well, this is where it gets
00:35:39difficult, correct? Because this is something that's kind of objective, like what makes a good
00:35:45report a good report. We could add some sort of subjective material there. Like, hey,
00:35:49every single report must have at least five videos from YouTube and five Twitter posts and five Reddit
00:35:54posts. And you must call out X, Y, and Z on the Gmail. So there are some things we can do here,
00:35:59but it's not a simple saying like, hey, you're doing a loop on some Python application and your goal
00:36:03is to get it to this particular speed. And then lastly, we have logging. So every
00:36:09single one of these reports, we could put it in some sort of database, right? That way,
00:36:14Claude code can always take a look at its past work and compare its upcoming work to things we
00:36:19have already done. And this in essence, from a theoretical perspective is loop engineering. Now,
00:36:24what does that actually look like in terms of a practical sense inside of Claude code?
00:36:28Well, step number one would be creating some sort of skill, because what did I just describe here?
00:36:34Well, I just described a skill. We can create a skill that Claude code runs on command
00:36:39or on a trigger, where it does all this stuff where it scrapes information and turns it into report.
00:36:44And we can include in that skill, it's sending all that information to a particular database.
00:36:48So step one to do sort of loop engineering for real would be to invoke the skill creator skill. Remember,
00:36:54I showed you how to do that earlier. And then you would just describe the skills I just did.
00:36:59From there, you would then run the skill manually over and over and over until you got it to a pretty
00:37:03good spot. Once you are happy with that skill, you would make sure you add a language about,
00:37:09hey, I want this to be logged in a database. And every time we run the skill, I want you to take a look
00:37:15at the previous iterations and see if we can do better. That's sort of where the self improving
00:37:20aspect comes in. Ideally, you would be able to score every single one of your previous reports. So it has
00:37:26some sort of objective measure it can base its outputs on, but simply you're first going to turn
00:37:31it into a skill. Once you've turned it into a skill, all you have to do now is turn it into an automation
00:37:36that runs all the time. And this is really simple inside of cloud code because we're just going to
00:37:41turn it into a routine. So if I come here to the left and hit routines and I go to new routine and I go to
00:37:46local, guess what I'm going to do? I'm just going to tell this to run that skill every day at a particular
00:37:53time. So this would be like loop skill, we are running the loop skill.
00:38:02And then the instructions would literally just be run the slash loop skill. Okay, when you're doing
00:38:11this, and you're creating this inside of cloud code with the skill creator, you would also say like,
00:38:14hey, I'm trying to do this to so it's like self improving, I want to the database loop engineering,
00:38:18fundamentals, etc, etc. The great thing about the skill creator skill inside of cloud code is that
00:38:22it's going to do all the heavy lifting for you, it understands sort of like the goal here. And then
00:38:27from there, you just schedule it, right? Ideally, it's probably just gonna be something that's daily,
00:38:31but you can do it hourly, weekdays, custom, whatever you want. And that is the practical application of
00:38:37loop engineering. And that's pretty much all you need to know. Because beyond loop engineering,
00:38:42we then start talking about graph engineering and graph engineering can get a little more complicated.
00:38:48But what's really happening is remember before, when we looked at this, we had all this loop
00:38:53engineering stuff going on. Let's actually just let's undo all this. We are all this loop engineering
00:38:59stuff going on, right? Like, hey, this one guy is scraping everything it's looking, it's creating the PDF,
00:39:04and it's being graded. Well, what if we did all that loop of trigger, of task success criteria,
00:39:12and then grading it, you know, logging the information? What if we did that at every step
00:39:16of the journey? So we had one agent that does this with scraping YouTube. So it has a trigger,
00:39:20it grabs YouTube, it scrapes the data, and then it judges how well it scraped it by looking at its
00:39:25past iterations itself improves. And then we did that with the Twitter poll, and the Reddit poll,
00:39:30and the Gmail poll, and this poll as well. So instead of having a loop for the entire thing,
00:39:36just like one huge loop, we instead had a bunch of like micro loops nested together inside of
00:39:41one run. That's graph engineering. So I did a whole video on that. It can be a little complicated. But
00:39:46at its core, that's all graph engineering really is. It's just a bunch of looping agents that also talk
00:39:52to one another. That's all it is. And for most people, this is total overkill. You don't usually need
00:39:57this. But conceptually, that's how it's working. Now shift the discussion over to dynamic workflows
00:40:02and ultra code. So what exactly is ultra code? How does this different from max effort? Well,
00:40:08ultra code, what it's going to do is it's essentially going to create a custom harness for whatever problem
00:40:14you're trying to solve. What that means in practical terms is it's probably going to spin up a bunch of
00:40:19sub agents to deal with whatever issue you have. This can be extremely effective, but this also can be
00:40:25extremely expensive. One example of a dynamic workflow is forward slash deep research. This is essentially
00:40:34like a prebuilt dynamic workflow. And it is very similar to how deep research works. If you're just
00:40:39on the web app and do deep research. So if I run deep research, what's going to happen is it's going to
00:40:44spawn a ton of sub agents and these sub agents are going to do a bunch of different tasks. So if I ask a
00:40:49question like let's deep research best use cases for dynamic workflows inside of cloud code,
00:40:59it's going to now instead of doing a standard web search where it spawns, you know, maybe like five
00:41:03sub agents to basically do a Google search, it's going to spawn several more than that. I've had it
00:41:08spawn well over 100 sub agents. And these are going to do a number of tasks, it's going to actually go out
00:41:12on the web and scrape data, it's then going to create adversarial agents that take a look at the data we
00:41:18found, and then compare and contrast it to see what actually holds up the scrutiny, then it's going to do
00:41:22synthesis, and then it's going to give me a final report. And so you can see over here, it decided that for the
00:41:28scope, it's going to decompose the question into five search angles, it says it's only going to need five
00:41:33parallel web search agents, which is nice for us since we're on fable. And then it's going to pull
00:41:38the top 15 sources, verify everything with a three vote adversarial check on each claim, and then finally
00:41:45synthesize it. And so we can see it working here over on the right since we spawned six agents and every
00:41:50single agent has like a pretty much a token cost right off the bat, we've already burned 314,000 tokens.
00:41:57So I wasn't joking about that. Now, when you run ultra code, if you don't tell it specifically, like
00:42:02let's say I'm on fable five and running ultra code, it's going to use fable for these sub-agents, which
00:42:07can be a problem. Because what if it did say, hey, I'm going to spawn 100 web search agents. Now when I
00:42:13give it the prompt to get away from something like that, you can specifically say, hey, limit it to
00:42:1720 sub-agents, limit it to 50 sub-agents. Or you can say something like, I want you to use Sonnet
00:42:22for the sub-agents or Opus for the sub-agents. So you aren't necessarily a slave to whatever model
00:42:26you're using at that time. Now Anthropic put out a pretty good blog explaining dynamic
00:42:31workflows. And so what's going on under the hood is that it's writing orchestration scripts that run
00:42:3610 to hundreds of parallel sub-agents in a single session, checking its work before anything reaches
00:42:40you. And here's some examples of different sorts of dynamic workflows. Remember, when you run dynamic
00:42:45workflows in ultra code, cloud code is going to figure out the best one that fits your problem. It might be
00:42:50one of these, it might be something completely different. So classify an act, you give it some sort of task,
00:42:54we have a classifier agent that then chooses the best sub-agent for you. Fan out and synthesize
00:42:59an adversary review. If we kind of combine these two, that's what we're doing with deep research,
00:43:02right? We have some sort of task, find this information, it fans out on the web, gets all
00:43:07the information, and then it also does adversary review to see what actually makes sense before
00:43:12synthesizing it for us. Then we got stuff like generate and filter, we have a tournament style
00:43:16thing where we try different attempts to solve some sort of problem and includes judges. And then we have
00:43:21loop until done, which again, very similar to loop engineering. If we jump back inside here,
00:43:25we can see this deep research run we started earlier included 103 agents and burned 6 million tokens.
00:43:32And this was all on Fable. So you can imagine how expensive this is. Here's a look at the actual report
00:43:38it gave us. And as you can see, pretty deep and also includes 21 different sources. And out of all
00:43:43the examples, I think deep research is the one you're going to use the most. And if you're someone who's
00:43:47about to embark on a pretty complicated project and you really want to get all your ducks in a row before
00:43:52you start even going into plan mode, I highly suggest using deep research. So Claude Co can go out there
00:43:57and see what's what before it starts building. Let's talk a little bit about model routing. And really,
00:44:01that's just how can we bring in outside models into the Claude Code fold? Because some of the big
00:44:07players like ChatGPT, their models are great. Sol 5.6 is awesome. Luna and Terra are extremely token efficient.
00:44:13And one sort of thing you need to keep in mind is AI systems in general, these models in general,
00:44:20don't do a great job of grading themselves. So if I ask Claude Code to grade its own work,
00:44:25I ask Opus to grade its own work, be able to grade its own work, it's going to pretty much always say,
00:44:30I did a great job. So how do we solve this problem, especially when we're having a great work that we
00:44:34ourselves can't grade? It's like outside our domain of expertise. I don't really know if that code is
00:44:38great. Well, why don't we bring in another frontier agent to take a look at our work? And the way we're
00:44:43going to do this is through different skills and plugins. There's actually an official Codex plugin
00:44:47for Claude Code. This is from OpenAI themselves. And this allows you to call on Codex from Claude Code.
00:44:53You simply take this URL, you paste it in Claude Code and say, I want to install this. You can.
00:44:58And from here, you can have Codex do adversary review of the code you've already created. Or you can even
00:45:04use this to have Codex work on specific features of your product. And when it comes specifically to the
00:45:09planning stage, I created a skill called Grill Me Codex, which combines Matt Pocock's Grill Me
00:45:13skill with adversarial review from Codex. What happens is you and Fable talk to one another,
00:45:20you come up with a plan, and then that plan is routed to Codex. And Codex and Fable kind of have
00:45:25a back and forth up to five rounds where Codex takes a look at what Fable created. Codex says,
00:45:29this is wrong, this is why. Claude reacts to it, says, okay, I'll fix that, or I don't agree. And
00:45:35they keep going back and forth until they reach an agreement. And so this kind of solves the problem
00:45:39of these models struggling to grade their own work and gets us a second set of eyes. And so we can feel
00:45:46pretty confident moving forward in complex cases. And this sort of model can be taken even a step
00:45:51further if you're someone who doesn't want to use something like Codex and instead want to rely on much
00:45:55cheaper models or even local models. The whole point of it is you aren't stuck just using Opus, Haiku,
00:46:02Sonnet, and Fable. We can bring in whatever we want, especially if we use it as a skill. And again,
00:46:07you would just use the skill creator skill to do that. And last but not least, we have our custom
00:46:11agentic OS structures like you see here and here. These are all about building a custom wrapper on top
00:46:17of Claude and giving us sort of a visual interface that we can't get elsewhere. And it's ultimately custom.
00:46:24Now, the real value of these things isn't the visual sort of wrapper, even though this is,
00:46:27you know, useful for me. This gives me my social media metrics. I can easily click and get a deeper
00:46:33dive on that. I get my research done every single day all in one place, showing me what's going on
00:46:37in GitHub, Hacker News, et cetera, et cetera. All these buttons are related to specific skills and
00:46:41automations that I can run on demand. And over here, I pretty much have the exact same thing,
00:46:45but it also has a voice mode. But like I said, the true value isn't in these cool visual layers.
00:46:51The real value is in the skill architecture behind it all. The whole idea of these things is turning Claude
00:46:58into essentially your personal assistant or an actual worker in your organization that can do
00:47:03everything you do. So for me, what you're looking at here is essentially all the skills I use in my
00:47:09day to day mapped across all the different domains that are important to me. So, you know,
00:47:13I have things related to memory, things like productivity, which is like Gmail, my calendar,
00:47:18all of that. I have stuff related to research, content, my community, my AI agency, sales,
00:47:22et cetera, et cetera. Every single one of these things are a task I would do on my own manually.
00:47:27Instead of doing them on my own manually, I have now mapped them to specific skills.
00:47:31And if they make sense, I turn those skills into automation. And when we look at that in a big picture
00:47:36sense, that is really what an agentic OS is. It's a series of skills and automations that you have
00:47:41mapped to your daily tasks. Now, how do you build something like this? Well, we've already kind of
00:47:44talked about it, right? This is just building skills. It's just using the skill creator skill,
00:47:49turning on your microphone and giving it a stream of consciousness about what it is you do day to day,
00:47:53week to week, and then asking Claude code, can we turn those into skills if they make sense?
00:47:58And if they do, you do it. And you do that over and over again. And eventually you create this corpus
00:48:02of skills that allow you to automate huge swaths of your life. And behind all that is Obsidian. And
00:48:08Obsidian allows us to easily track everything we do in terms of a markdown file system. Now, Obsidian
00:48:15in itself isn't giving you some like crazy upgrade to what Claude code can do, but allows us to actually
00:48:20track everything and allows it to give it some memory. And this is sort of the basis of like the
00:48:24Carpathi Obsidian RAG system you've probably heard before. Now, as a very quick review of what I mean
00:48:28when I'm talking about an Obsidian memory system, this is just a file structure. This is just a
00:48:33coherent file structure that Claude code lives in. So you understand where things go. This is again,
00:48:38often referred to as the Carpathi Obsidian thing. And it's very simple. You have some sort of folder
00:48:42that your agentic OS lives in. Mine is called the vault. Inside the vault, you have some sort of file
00:48:48structure that looks like this. It doesn't have to be exact, but the idea is in one of those folders,
00:48:52we have the raw section. This is sort of where raw data goes is where research goes.
00:48:56We then have a wiki section and we have an output section. The wiki section is where we take all this
00:49:01raw data and we essentially turn it into different reports or wiki style articles. So imagine
00:49:06I had Claude code go do a bunch of research on AI agents. We'll dump the raw information here and then
00:49:11it creates an article about AI agents under the AI agents subfolder. Now, let's say I wanted to create
00:49:17something from that AI agent type wiki. I wanted to turn it into a slide deck, for example. Well, then that
00:49:23slide deck would go to the output section. And the idea is with this simple sort of model, I am able to
00:49:29deal with a ton of files, potentially hundreds of thousands. And it is set up in a clear way that I as
00:49:35a human being can easily navigate and B, Claude can easily navigate. And if it can easily navigate this
00:49:41file structure, it's going to make it more accurate and ultimately cost less tokens. The key of this
00:49:45whole thing are these sort of like index files at every step of the journey. Every time I go deeper
00:49:50into the file structure, there's essentially an index.markdown file telling me what is going
00:49:55on inside of there. So it's essentially like a table of contents. Now, I have a ton of content that goes a
00:49:58lot deeper into this if that was confusing. But the idea of these agentic OS systems is that this visual
00:50:05wrapper sits on top of that custom skill architecture I just talked about and is sort of buoyed by that
00:50:10obsidian memory layer, which helps you navigate everything you've done and helps Claude code navigate
00:50:15it just a little bit better. And the way these systems work, because it's like, hey, I'm not actually
00:50:19using Claude code here, am I is we use Atlas Claude, which uses dash P. So essentially, a command is sent
00:50:28to the terminal, instead of running like just Claude, it uses Claude dash P, which means Claude
00:50:34essentially runs invisibly in the background. There was some drama about this for a while where Anthropic
00:50:38was saying they were going to charge different rates for that and wouldn't be usage, but that's no
00:50:41longer the case. So these sort of systems are actually just as money efficient as anything else.
00:50:47Now creating agentic or Claude OS like this isn't 100% necessary. But I think it is necessary to sort
00:50:53of create these sort of skill architectures that really is the backbone of it all. And of course,
00:50:59if you want to get my exact setup, I have that inside of Chase AI plus as well. So that is where
00:51:03I'm going to leave you for today, we covered a ton of different topics. And these are the ones that I
00:51:07felt kind of can give you the most bang for your buck in terms of what you should focus on from everywhere
00:51:12from the beginning, all the way to a more advanced level. So as always, let me know what you
00:51:17thought. Make sure to check check out Chase AI plus if you want to get your hands on my Claude
00:51:20code masterclass, which kind of covers a lot of the same stuff we did here, just in much greater detail.
00:51:26Besides that, I'll see you around.