You use AI every day. Beat. But that doesn't actually mean you're an advanced user. Right. I mean, it definitely feels advanced, but daily casual use is really just the beginning. I think about this a lot. We type a quick prompt into a chat box. We get a really solid answer. We feel incredibly productive. Oh, yeah. Totally. Beat. But we are barely scratching the surface of what is possible. Welcome to this deep dive.
Today, we're charting a very specific path. We are exploring the five levels of cloud proficiency. We're going to move step by step through this entire framework. We'll start with those simple everyday chats that will climb all the way up. We'll explore fully autonomous background workflows. Our goal is to help you pinpoint exactly where you are right now. Right. And more importantly, we want to show you how to level up safely. So let's start at the absolute baseline. Level one.
the enthusiast. Yeah, which is honestly where most people tend to live right now. You might assume you're a total power user just because you chat with the AI every single morning. But realistically, this is just the ground floor. Level one is really built for quick daily tasks. You know, you need a fast email draft. You need a short summary of a long article. Right. Or maybe you just need a simple explanation of a complex topic. It just gives you a much faster
starting point. You paste in some rough meeting notes. You ask the AI for a clean version. Yeah. A few minutes later, you have something totally useful. It helps you think faster. You create a solid first draft. And then you just finish the actual work yourself. There is a very specific tactic here that I love. One that works incredibly well when you're at level one. What's that? Uploading screenshots directly into the chat. Oh, yeah.
That is a fantastic habit to build. You really need to stop typing out every single detail. It just takes way too much time. Exactly. Because you might accidentally miss something important. Just share exactly what you see on your screen. Exactly. So imagine you get a really confusing dashboard error, or you see a weird error message during your work. Right. You panic a little. Yeah. But you don't try to transcribe the code. You just snap a quick screenshot. You upload
the image directly. Then you ask it to explain the error in simple English, and you ask for the very next step to fix it. It also works incredibly well for visual content, too. Say you're designing a new newsletter layout, you take a quick screenshot of the draft, you ask the AI to check the title length, or you ask if the call -to -action placement needs clearer wording. It's working straight from the real visual data. But there's a really massive limitation at this stage. a severe lack
of ongoing context. Oh, completely. One -off chats are perfectly fine for single isolated tasks, but repeated ongoing work is entirely different. Exactly. Repeated work requires a very specific, consistent tone. It requires deeply knowing the target audience. You have internal company rules. Yeah. You have past business decisions to consider. And in a quick chat, the AI... basically forgets all of that. It has total amnesia. You have to repeat your tone and rules every single
time. Honestly, I have a vulnerable admission to make here. Okay, let's hear it. Beep beep. I still wrestle with starting every repeated task in a blank chat. Oh, we all do it sometimes. It just feels faster in the heat of the moment. You just want an answer right now. Yeah, I do. But over a month you waste countless hours re -explaining yourself. It really makes me wonder about the true value here. Is level one basically just using Claude as a glorified short -term
search engine? Pretty much. It's a fast starter, but it forgets everything by the next day. Which perfectly highlights the core problem we need to solve. We have to fix that blank chat amnesia. Yes. So let's step up to level two, the beginner. We fix this by building a persistent workspace. Level two is entirely focused on creating continuity. Your important work continues across multiple conversations. It continues across your files
and your connected data sources. This level relies heavily on three core features, projects, memory, and past chat search. Right. Memory naturally retains your specific working preferences over time. Past chat search lets you easily find older conversations. If you can just open a chat and say, find the campaign direction from last week. Exactly. Then you ask it to prepare the final outline based on that. It seamlessly maintains the historical thread. But it gets really interesting
when we bring in connectors. You can actually connect the AI to the information you already use. Yeah, this is a huge step up. Google Drive, Gmail, Slack, Google Calendar, Microsoft 365. Wow, that's a lot. Your real... actionable context already sits inside those apps. Connectors pull that securely approved information right into your active conversation. So you don't have to manually collect every single detail anymore. You don't have to constantly switch tabs to copy
and paste. You can just ask it to review a specific client email thread. Right. Or review the latest strategic brief directly from Google Drive. Let's look at a very specific, practical example here. Creating a highly structured meeting summary. You upload your messy, raw client notes. Yeah, we've all been there. And you want a professional Word document out of it. The prompt for this needs to be very deliberate. You ask it to clearly include the meeting background. You explicitly
ask for the confirmed decisions. You ask for a list of questions that still need confirmation. And finally, you demand a next actions table. That table must include the responsible person. and the specific deadline. But here is absolutely the most important part of that workflow. You give the AI a very strict, unbreakable rule. Which is? If any information is missing, Claude must write, not confirmed. It is absolutely not allowed to guess or fill in the blanks. I like
to think about it this way. Level one is a public whiteboard that gets wiped clean every night. I like that. Level two is a dedicated office desk where your files stay exactly where you left them. That's a perfect way to picture the difference. It reliably turns messy inputs into clearly structured files of files you can actually review, trust, and use in your job. But I do have a lingering concern here. With all those connected apps, can we trust it not to just invent
information? It's safer, but you still must review the structured output before sending it anywhere. Okay, so we've got our files beautifully structured on our metaphorical desk, but I'm still the one opening the folders and doing the actual manual labor. Right. I'm still clicking and dragging. Exactly. And that bottleneck is why you have to level up again. Welcome to level three, the intermediate. We've mastered text outputs. Now we give the AI actual hands. We're talking about
a feature called Claude Cowork. This is where it actively starts operating your computer for you. It handles complex multi -step tasks directly. It moves way beyond just reading your text files. It actually starts manipulating the files on your machine. Okay. So imagine you have a wildly messy downloads folder. It's just stuffed with random invoices, notes, and PDFs. You can give Cowork a very specific multi -step directive. You literally tell it to sort that folder by
file type. You tell it to rename every single file to a standard format, like year, month, day. Then you ask it to summarize the contents of all those files. And if a file is unreadable or unclear, it doesn't just crash, it neatly moves it to a dedicated folder. Like a needs review folder. Exactly. It does all the tedious heavy lifting for you. But we really need to talk about safe file access here. This is absolutely crucial. When you give an AI direct file access,
you must start small. You have to set up isolated, protected folders. Specifically, you should have three distinct folders for this. Right. Source files, working files, and outputs. Let's break that down. Source files are the read -only documents it can look at. Working files are the temporary sandbox where it actively processes data. Outputs are the final finished results for you to manually check. Exactly. You never let it loose on your main hard drive. That's just asking for a disaster.
This isolated structure leads to something really powerful, though. Reusable skills. Oh, yeah. When a tedious task repeats every single week, you save it as a skill. Let's use a weekly client report generator as a solid example. You build a custom skill to process marketing data every Friday. OK. You establish strict rules for the output. You require five specific sections every single time it runs. So you'd tell it, I need
an executive summary at the very top. It's a short, crisp summary of the week's performance. Right. And then you force it to extract the key results. It pulls the important, verified metrics directly from the uploaded data. Right. And you want to compare those numbers to the previous week. So you add a section for important changes. Yeah, exactly. You're building a reliable, repeatable template. And you'd probably want it to flag anything that's missing, too. Definitely. That's
your open questions section. It lists any unclear data points. Then it always ends with a clear next actions table. Action, owner, and deadline. Once you save that specific skill, your weekly workflow changes entirely. How so? You just upload the fresh raw data each week. You hit run on the skill. It automatically follows the exact same five -step process without you managing it. You can even schedule these simple tasks to run automatically. Say you're using a cloud
desktop application. Yeah. You could easily set up a customized briefing for 7 .30 p .m. every weekday. It quietly checks your connected sources in the background. It stands Gmail. It checks your calendar. It reads your Slack messages. It pulls all that context together. Then it prepares a short summary of tomorrow's meetings and any pending actions. It feels incredibly powerful. But I really have to ask this. Isn't giving AI direct file access a recipe for accidentally
deleting your hard drive? Only if you're reckless. Always use copy files in isolated folders to test first, sponsor. We're back! So we just covered automating desktop chores in Level 3. Now we really scale things up. We do. Welcome to Level 4, the Advanced Builder. This is a major jump. This is where we introduce Claude code. We're moving away from simple folder sorting tasks. We're getting into complex multi -file project engineering. This requires a real dedicated project
structure. And crucially, it requires a Claude .md file. Let's quickly define that jargon, a simple text file that tells the AI your project rules. It basically acts like a strict working guide for the system. It clearly defines the overarching project goal. It explicitly explains what all the main folders are actually used for. Right. It lists the specific files the AI is allowed to edit. And crucially, it lists the
files it must absolutely leave unchanged. It also tells the AI exactly how to run your internal tests. And it defines what the final report format should look like. But there is a very critical step here that you cannot skip. You must ask the AI to plan and explain its approach first. Yes. It has to do this before it makes any actual edits. It's like asking a contractor for blueprints before they knock down a wall. You prompt it to read the folder structure. You ask for a detailed
step -by -step plan. It needs to explain which files will change and exactly why. You review that plan. You approve it. Only then does the actual coding work begin. As these projects get bigger, things naturally get much more complicated. One single session can quickly become overloaded and confused. Yeah, for sure. So we have to deliberately split the workout. We split big work into parallel sessions. We use specialized tools like sub -agents and work trees. One stream handles the front
-end layout. Another stream prepares the backend tests. A third stream carefully reviews the overall logic. This keeps the different work streams entirely separate and clean. They happen at the exact same time. but they don't accidentally interfere with each other's code. And you always have a mandatory non -negotiable review step built in. The AI absolutely must check the result before you accept the files. It must run all the available system checks. If a check fails,
it has to explicitly explain the issue. It fixes the error itself. It runs the check again. Finally, it prints out a list of every single changed file. You carefully look at that list. You verify the changes. Then you formally consider the task complete. But I'm genuinely curious about the mechanics of this process. Why bother splitting tasks into parallel sessions instead of just one giant prompt? To keep the AI focused, preventing it from getting confused, and mixing up changes.
That makes perfect sense. Clear rules. Clear inputs. Clear safety checks. Which finally brings us to the absolute summit of this framework. Level five, the architect. This is fully autonomous infrastructure. We're completely removing the human from the active day -to -day loop. We're finally entering the realm of true background automation. You've already set the strict rules in Level 4. Now, the complex workflow runs without you even sitting at the screen. But the hardest
part of Level 5 isn't actually technical. Beat. It's trust. Trust is incredibly difficult here. You're letting an AI touch real files and critical business data in the background. Giving up that control is scary. You have to build this autonomy very, very slowly. At this advanced stage, tasks start from automatic triggers. a time schedule, a brand new email arriving, a specific GitHub event, or an external API call. We use a system called Cloud Routines for this. These are secure
cloud -based workflows. They run entirely on Anthropic's own back -end infrastructure. They don't rely on your local desktop being turned on. So you can set a routine to automatically review changed files when a pull request opens. Or you could have it create a massive weekly project summary every Friday afternoon. But we desperately need safety mechanisms here. This is exactly where hooks come in. Let's define that term clearly. Safety gates that check the
AI's actions before and after. Right. There's a pre -tool use hook. It acts just like a strict security gate. Before the AI actually runs a command, the hook checks it. If the proposed action is risky, like deleting important files, it blocks it instantly. It asks the AI to rethink and correct the action. Then there's the post tool use hook. After the tool successfully finishes, this hook handles the necessary cleanup. It might
automatically format a text file. Or it might send you a quick Slack notification that the background task is done. The entire workflow is fully automated. But those precise hooks keep it tightly and safely controlled. You still want to start this process very safely though. We highly recommend starting with a daily private morning briefing. Yes. Just let it run quietly every morning at 6 a .m. It checks your upcoming calendar. It safely scans your important emails.
Right. It reviews your missed slack threads. It prepares a comprehensive summary. But it sends that summary only to you. It doesn't email your boss. You review it over coffee before taking any action. Yeah, you let it run like this for a full week. You check if it misses nuances or messes up your actual priorities. You carefully adjust the background instructions. Only after definitively proved reliable do you give it a more important workflow. Exactly. Beat. Whoa.
I mean, imagine truly scaling this up. Imagine an AI autonomously running your complex workflows in the background while you sleep. It's wild. Perfect checks. Perfect balance. Waking up to finished work. It's an absolutely amazing thought, but it clearly demands immense caution in planning. Which naturally raises a really big question for me. How do we stop an autonomous system from going rogue and sending a bad email? You use those hooks to block risky actions and strictly
require human review first. Okay, let's bring this all back down to earth for a minute. We need a big idea recap to tie this together. The core insight from all of this source material is surprisingly clear. Advanced use isn't really about writing better, more clever prompts. It's entirely about clearer workflow design. You don't just magically jump to level five. You don't blindly rush into full automation. You have to
build the foundational structure first. Yeah, you add proper folder structure, you add firm safety limits, you add mandatory review steps. That's how this technology becomes truly, reliably useful. So here is your practical takeaway for today. Look closely at your own workflow. Pick just one repeated week task that annoys you. Just one task and upgrade it by exactly one single level. If you're stuck at level one, make a project. If you already have a project, connect one live
data source. If you have connected sources, try a basic co -work task. Build a safer, more structured workflow. Add a mandatory review step. One single intentional upgrade will teach you way more than trying to build a massive autonomous system on day one. It forces you to truly understand the underlying mechanics. It forces you to actually build trust with the tool. Step by step, level by level. That's how you actually get advanced.
It's been a truly fascinating journey through these five levels today, from the eager enthusiast all the way up to the architect. But I want to leave you with a final slightly provocative thought to mull over. Beat. Let's say you successfully level up. You reach a point where AI flawlessly remembers your context, where it perfectly plans your complex projects, where it autonomously executes your daily routines with perfect hooks and safety checks.