WEBVTT

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Imagine an AI that doesn't just talk, but actually,

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you know, works while you sleep. Oh, yeah. That

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is the dream, right? Think about that for a second.

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It's the fundamental difference between having

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a simple chat with an AI and building a functioning

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independent system. If you want a digital employee,

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you basically have to build it a workspace. Precisely.

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And that's exactly what we are doing today with

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Claude Cowork. Right. Welcome back to the Deep

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Dive. Today, we are exploring building your first

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Claude Cowork Agent. We're going to cover the

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setup, the folder logic, and the automation steps.

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Yeah, you probably use Claude every day. But

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honestly, you're likely using it wrong, or at

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least you're only scratching the surface. Totally.

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Today is a literal roadmap for moving beyond

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that traditional chat box. We're going to explore

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how you can use Cowork to build a real, functional,

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daily AI news dashboard. OK, let's unpack this.

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Because when I first heard about Cowork, I honestly

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thought it was just I don't know, another UI

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update. Right, like just a visual thing. Yep,

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exactly. Another place to tap a question. But

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Cowork is totally different. It's built for repeatable

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file -based jobs. It lives inside your folders,

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it reads, it writes, and it saves documents rather

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than just existing in a fleeting conversation

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window where you have to constantly copy and

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paste everything. What's fascinating here is

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the shift in computing behavior. We are all used

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to standard cloud chat. Which is great, obviously.

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Oh, it's fantastic for quick brainstorming, drafting

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a single email, or just answering a one -off

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question. To transition from getting one -time

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help to building automated business workflows,

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you are no longer a conversational partner. Right.

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You are a systems architect. I love that. A systems

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architect. So if I'm playing that role, what

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are my actual building materials? Let's talk

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prerequisites for you guys listening. First,

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you need the cloud desktop app installed. Yes.

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This isn't running in a browser tab. Second,

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you need a paid plan. Pro is the bare minimum

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here. And we should probably explain exactly

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why that free tier simply won't cut it. Yeah,

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please do. Core workflows inherently require

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much higher usage limits and significantly more

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processing context. Right. When you run a dashboard

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workflow, the AI is reading multiple files. It's

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cross -referencing your instructions against

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several external articles, and it's generating

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heavily formatted outputs. It's doing a lot at

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once. Exactly. It needs a massive working memory

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to hold all that data simultaneously. That makes

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total sense. And that actually brings up what

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I like to call the work Golden Roll, you need

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a dedicated workspace folder on your actual computer.

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Yes, absolutely. You might name it something

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like Claude Cookework News Dashboard, but I want

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to make a hard stop right here because the temptation

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is just to give the AI access to everything.

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Oh, big mistake. Do not just point Claude at

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your whole computer or your entire documents

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folder. That is like letting a brand new intern

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rifle through all your personal filing cabinets,

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your tax returns, your family photos, just to

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find a single press release. Yeah, that is an

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incredibly dangerous trap for new users. It really

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is. If we connect this to the bigger picture

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of how language models operate, giving the AI

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too much access just creates massive confusion.

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Right, because it's reading everything. Exactly.

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Every time it reads an irrelevant file, it eats

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up its processing context, its working memory.

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It has to sift through your grocery lists and

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old spreadsheets just to figure out what a news

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update means. Wow. That dilutes its focus. You

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want to isolate the work space so the AI only

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sees exactly what it needs to execute the task.

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Nothing more, nothing less. And speaking of focus,

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you also have to dictate which underlying model

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is actually doing the processing. The sources

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are very clear about using the strongest model

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available for research tasks. So we are talking

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about using something like Claude 3 .5 Opus or

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Sonnet rather than the faster, lighter Haiku

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model, right? Yes. Precision matters immensely

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here. When you ask an AI to analyze multiple

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sources, rank them, and synthesize the information,

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you are fighting against a phenomenon called

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prompt drift. Wait, prompt drift. Explain prompt

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drift for me. Sure. Think of it like a game of

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telephone happening inside the AI's own head.

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Over the course of a complex multi -step task,

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a weaker model slowly forgets or misinterprets

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the original instructions. Oh, I've seen that

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happen. Right. It might start out strong on the

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first two articles, perfectly applying your formatting.

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But by the time it reads the 10th article, its

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working memory is stretched, and it starts hallucinating,

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summarizing things in the wrong tone, or just

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completely ignoring your formatting rules. That's

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so frustrating. It is. So using the strongest

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model, provides the immense cognitive stamina

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required to hold onto your rules from start to

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finish. Got it. So you've restricted the AI's

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access to your tax returns, and you've selected

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a model with enough stamina to avoid drifting

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off course. Right. Now you actually have to build

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the physical structure inside that isolated folder.

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Exactly. Think of the folder structure like an

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assembly line in a factory. To get this digital

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employee working smoothly, you need three main

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folders on that assembly line. Okay, what are

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they? about, outputs, and templates. I have to

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admit, creating three separate folders and a

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bunch of markdown files just to get a new summary

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feels incredibly tedious. It sounds like a lot

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of work. It really does. Why can't I just paste

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a massive three page prompt into the regular

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clawed chat window every morning and get the

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exact same result? Well, because copying and

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pasting a massive prompt every day isn't a system,

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it's a chore. Fair point. If you put all your

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rules into a chat prompt, that knowledge vanishes

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the moment you close the window. Yeah. By building

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the structure on your hard drive, you are creating

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permanence. The about folder serves as the permanent

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blueprint for the assembly line. Yeah. The templates

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folder is the mold. If you don't build the track

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correctly and permanently, the robotic arm claw

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just flails around in the dark every single time

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you start a new session. OK, that clarifies it.

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We are trading upfront effort for long term automation.

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I can get behind that. Exactly. So let's look

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at that blueprint, the ABOUT folder. Our sources

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say this is where you store the high signal rules

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that filter out the noise before the AI even

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begins to work. Yes. The internet is filled with

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garbage data. So much garbage. Left to its own

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devices, and AI will try to summarize all of

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it. The files in your about folder are your hard

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filters to prevent that. Let's walk through those

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filters. First up is your source list dot dash

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MD file. And in this file, you explicitly limit

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the AI to five to eight trusted sources. You

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organize them by priority. Priority 1 is official

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company blogs like Antropic or OpenAI. Priority

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2 is research repositories like Hugging Face.

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Priority 3 is general business and tech news.

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Right. But I'm looking at this limitation and

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wondering why handcuff the AI? Why not just say,

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search the whole internet for AI news today?

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Because a highly curated source list produces

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dramatically cleaner reports. If you tell an

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AI to search the entire web, it will pull from

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SEO, spam sites, unverified Reddit threads, and

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out -of -date opinion blogs. That's true. By

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restricting it to, say, five official URLs, you

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are guaranteeing the data quality. You are also

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explicitly instructing it, do not treat rumors

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as facts. OK, so we've restricted the sources.

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But even within five trusted official sites,

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there's a ton of garbage. Oh, absolutely. Companies

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published market. fluff all the time. How do

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we stop the AI from summarizing a minor interface

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tweak like it's a major technological breakthrough?

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That brings us to the second file in your About

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folder, the DashboardRules .md file. Okay. This

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is where you define consequence. You instruct

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the AI to rank every single news item on a strict

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scale of one to five based on practical impact

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and urgency. So how does that play out in a real

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scenario? Let's say OpenAI drops a massive new

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model upgrade, but on the exact same day, they

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also release a minor bug fix for their billing

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page. In a standard chat prompt, the AI might

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just give you two bullet points of equal length,

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treating both events as equal news. Right. But

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with your dashboard rules file, the AI is forced

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to pause and evaluate. It looks at the massive

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model upgrade, sees that it alters capabilities,

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and scores it a 5. OK. It looks at the billing

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bug fix, realizes it has zero impact on output

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quality, and scores it a 1. Your rules can then

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dictate only include items that score a three

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or higher in the final report. You've just built

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an automated editorial filter. You're forcing

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the AI to have editorial judgment based on your

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criteria. I see. But even if it picks the right

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stories, there's still the problem of how it

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actually talks. Yes, the tone. Which leads to

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the third file in the blueprint, the writing

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style .md file. Yes, and this might be the most

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crucial file for long -term readability. You

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use this strict writing style file to enforce

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clear, direct, human -sounding English. Right.

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You demand short sentences. You demand factual

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reporting. And the absolute most fascinating

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part of this file is the banned word list. Oh,

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it's so good. You explicitly ban those cliché,

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exhausting AI buzzwords. You tell it under no

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circumstances are you allowed to use the words

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revolutionary, game -changing, unlock, unleash,

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seamless, or dive into. This is a brilliant structural

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hack. AI models naturally default to marketing

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speak. They really do. Think about their training

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data. They have ingested millions of press releases

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and marketing websites. When an AI sees a product

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update, its highest probability predictive text

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pathway is to describe it as game changing. So

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what does this all mean when we ban those words?

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Were you just making it sound less annoying?

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It goes much deeper than just tone. Banning those

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words physically forces the model's neural network

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to access different, more analytical pathways

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to complete its sentence. If the AI is literally

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prohibited from saying, a new tool will unleash

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your potential, it hits a roadblock. It is forced

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to look closer at the data and explain the actual

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mechanism. That makes a lot of sense. It has

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to say, this tool reduces rendering time by 40

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percent. It completely shifts the output from

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passive marketing fluff to active practical analysis.

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So you're saying we have to artificially constrain

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the AI to make it smarter. Yes. We take away

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its favorite adjectives. to rely on strong verbs

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and hard facts. That is a massive paradigm shift.

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OK. So we have our assembly line blueprint built.

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The About folder is locked. But how does the

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AI actually know where to find the blueprint

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and what to do with it? That is the job of global

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instructions. You find these in the Settings

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menu of Cowork. Think of global instructions

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as the permanent laws of your workspace. They

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act as the traffic director. Right, so your global

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instructions would read something like read the

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source list, the dashboard rules, and the writing

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style in the about folder. Use the template in

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the templates folder and save the final output

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to the ITPU test folder. Exactly. But here's

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my question. If we went through all the trouble

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to write these complex rules, why do the sources

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explicitly warn us to keep the global instructions

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relatively short? Why not just put the band words

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right there in the main settings? This raises

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an important point about system architecture.

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Global instructions exist only to define the

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workspace logic. If you shove your one to five

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ranking system, your band words, and your source

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URLs into the global instructions, the cognitive

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load is too heavy at the top level. I get it.

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The traffic director gets overwhelmed trying

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to build the car while directing traffic. Great

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analogy. keep the global instructions short so

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they act as a stable foundation just pointing

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to the folders. The complex, specific rules belong

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safely isolated inside those separate markdown

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files. The traffic director just points. The

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blueprint does the heavy lifting. I like that.

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So the traffic director points the AI to the

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sources, filters the news through the rules,

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and then point it to the template. Right. Inside

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your templates folder, you have a file called

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inews -template .md. Exactly. And a weak dashboard

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template just asks for a list of headlines. A

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powerful, useful dashboard demands structured,

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actionable intelligence. The sources show this

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template dictating a very specific final output.

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It must include an executive summary. It must

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display the impact scores for each item that

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we talked about earlier. But the most valuable

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part is the requirement for content angles. It

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literally forces the AI to provide a newsletter

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idea or a LinkedIn post idea based specifically

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on that news item. What's happening here is the

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transformation of passive information into active

00:12:34.950 --> 00:12:37.600
utility. You aren't just reading that Anthropic

00:12:37.600 --> 00:12:40.259
released a new feature. Your system is handing

00:12:40.259 --> 00:12:43.519
you immediate, actionable ways to use that news.

00:12:43.639 --> 00:12:46.539
Which is huge. Right. For example, if you are

00:12:46.539 --> 00:12:49.120
a developer, your template might demand a code

00:12:49.120 --> 00:12:51.639
integration angle. If you are a marketer, it

00:12:51.639 --> 00:12:54.539
demands a campaign angle. You are tailoring the

00:12:54.539 --> 00:12:57.039
news directly to your daily deliverables. It

00:12:57.039 --> 00:12:59.659
is brilliant. We have our isolated folder. We

00:12:59.659 --> 00:13:02.460
have our assembly line built. We have our traffic

00:13:02.460 --> 00:13:05.399
directing global instructions. Now it is time

00:13:05.399 --> 00:13:08.360
to flip the switch and watch this digital employee

00:13:08.360 --> 00:13:12.120
go to work. Let's talk about execution. This

00:13:12.120 --> 00:13:14.320
is where you see the magic of the architecture

00:13:14.320 --> 00:13:16.440
you've built. You open a new task in Cowork,

00:13:16.779 --> 00:13:19.580
ensure it is pointed at your specific Cloud Cowork

00:13:19.580 --> 00:13:22.460
News dashboard workspace, and give it a simple

00:13:22.460 --> 00:13:25.350
one -line instruction. Like what? Just create

00:13:25.350 --> 00:13:28.330
today's daily AI news dashboard. That's it. One

00:13:28.330 --> 00:13:30.710
sense. That's it. Because of all your setup,

00:13:31.049 --> 00:13:33.509
Claude knows exactly what to do. It checks the

00:13:33.509 --> 00:13:35.710
blogs on your approved source list. It evaluates

00:13:35.710 --> 00:13:38.389
the rumors versus the facts. It ranks the updates

00:13:38.389 --> 00:13:41.870
one to five. It forces its neural pathways to

00:13:41.870 --> 00:13:44.809
avoid the banned words. And it formats everything

00:13:44.809 --> 00:13:47.610
into your template. And along the way, it might...

00:13:47.450 --> 00:13:50.049
pause and ask you questions, right? Yes, and

00:13:50.049 --> 00:13:52.490
this is a vital part of the co -work process.

00:13:52.909 --> 00:13:55.490
It might ask clarifying questions before it starts

00:13:55.490 --> 00:13:57.690
generating the final report. Give me an example.

00:13:57.929 --> 00:14:00.289
Well, it might say, I found three updates that

00:14:00.289 --> 00:14:02.549
score a four today. Do you want me to expand

00:14:02.549 --> 00:14:05.389
the content angles for all three or just focus

00:14:05.389 --> 00:14:07.970
on the top story? OK, that's helpful. You should

00:14:07.970 --> 00:14:10.190
always answer these questions. Providing that

00:14:10.190 --> 00:14:12.990
context prevents the AI from making weak assumptions

00:14:12.990 --> 00:14:15.289
and filling in the gaps itself. But I have to

00:14:15.289 --> 00:14:17.899
push back on this step. If the ultimate goal

00:14:17.899 --> 00:14:20.720
of this entire deep dive is to have this thing

00:14:20.720 --> 00:14:23.720
work while I sleep, why am I sitting here answering

00:14:23.720 --> 00:14:26.639
clarifying questions? I know, it feels counterintuitive.

00:14:26.799 --> 00:14:29.860
Why is this first manual run so critical? Why

00:14:29.860 --> 00:14:32.440
can't I just build my folders, set my rules,

00:14:32.899 --> 00:14:34.960
and immediately automate it to run every morning?

00:14:35.279 --> 00:14:37.500
It's a very common impulse, but if you automate

00:14:37.500 --> 00:14:39.600
a flawed system, you just get flawed outputs

00:14:39.600 --> 00:14:42.200
faster. Oh, that's a good point. The manual testing

00:14:42.200 --> 00:14:45.049
phase is your safety net. It allows for early

00:14:45.049 --> 00:14:47.529
corrections before you commit to a fully automated

00:14:47.529 --> 00:14:50.710
schedule. During this first run, you watch the

00:14:50.710 --> 00:14:54.070
tool activity window. If the AI grabs a source

00:14:54.070 --> 00:14:56.629
that isn't on your list, or if it slips up and

00:14:56.629 --> 00:14:58.710
calls something game -changing, you catch it

00:14:58.710 --> 00:15:00.809
immediately. And if it does make a mistake, if

00:15:00.809 --> 00:15:04.210
it hallucinates or breaks a rule, is that just

00:15:04.210 --> 00:15:07.909
the AI being stubborn? Rarely. And this is a

00:15:07.909 --> 00:15:10.730
hard truth for a lot of users to accept. If the

00:15:10.730 --> 00:15:13.409
results feel off, it is almost always a thaw

00:15:13.409 --> 00:15:16.250
in the system design, not the AI itself. Interesting.

00:15:16.309 --> 00:15:19.070
It's an error in your instructions. Maybe your

00:15:19.070 --> 00:15:21.649
definition of a level five impact was too vague.

00:15:22.149 --> 00:15:24.710
Maybe your source list had a broken URL. The

00:15:24.710 --> 00:15:27.009
manual run lets you debug your own rules. OK,

00:15:27.049 --> 00:15:28.850
so we've debugged it. We tweaked the blueprint.

00:15:29.129 --> 00:15:31.710
We ran it manually. And the output is beautiful.

00:15:31.879 --> 00:15:34.519
It is short, it is actionable, it is verified,

00:15:34.620 --> 00:15:37.240
and there's no marketing fluff. Perfect. Now

00:15:37.240 --> 00:15:40.019
we reach the holy grail, scaling and automation.

00:15:40.279 --> 00:15:42.399
Once your manual run is perfect, you can utilize

00:15:42.399 --> 00:15:44.419
the scheduling feature. You literally just type

00:15:44.419 --> 00:15:46.799
slash schedule in the task bar. Wow. You can

00:15:46.799 --> 00:15:49.820
tell Claude to run this exact dashboard workflow

00:15:49.820 --> 00:15:53.299
every single morning at 8 0 a .m. Just typing

00:15:53.299 --> 00:15:56.220
slash schedule. That feels like magic. Yeah.

00:15:56.559 --> 00:15:59.000
But. We need to be incredibly clear about the

00:15:59.000 --> 00:16:00.759
technical reality here because this is where

00:16:00.759 --> 00:16:02.679
people get frustrated. Yeah, there's a catch.

00:16:03.080 --> 00:16:05.500
What are the strict requirements for a scheduled

00:16:05.500 --> 00:16:08.759
task to actually fire? The requirements are physical.

00:16:09.120 --> 00:16:11.740
This workflow is not running on a server somewhere

00:16:11.740 --> 00:16:13.659
out in the cloud while you are disconnected.

00:16:14.279 --> 00:16:17.639
Claude Cowork operates locally. Explain the mechanics

00:16:17.639 --> 00:16:20.240
of that. Why does my computer need to be involved

00:16:20.240 --> 00:16:23.309
if the AI lives in the cloud? Because the AI's

00:16:23.309 --> 00:16:26.129
brain is in the cloud, but your workspace, your

00:16:26.129 --> 00:16:28.370
about folder, your templates, your outputs lives

00:16:28.370 --> 00:16:30.669
on your physical hard drive. For the scheduled

00:16:30.669 --> 00:16:32.850
task to execute, it physically needs to access

00:16:32.850 --> 00:16:34.970
the files on your hard drive, read them into

00:16:34.970 --> 00:16:37.789
its memory, process the data, and save a new

00:16:37.789 --> 00:16:39.889
Markdown file back to your local drive. That

00:16:39.889 --> 00:16:42.370
makes sense. If your machine is asleep or if

00:16:42.370 --> 00:16:45.250
the desktop app is closed, the bridge between

00:16:45.250 --> 00:16:47.690
Claude's cloud brain and your local files is

00:16:47.690 --> 00:16:50.289
completely subbered. The digital employee can't

00:16:50.289 --> 00:16:53.269
get into the office, so your claw desktop app

00:16:53.269 --> 00:16:55.649
must be open and your computer must be awake.

00:16:55.830 --> 00:16:57.990
So if your laptop is closed in your backpack

00:16:57.990 --> 00:17:01.570
at 8 a .m., the scheduled task will simply fail.

00:17:02.110 --> 00:17:03.870
You have to treat it like a real machine that

00:17:03.870 --> 00:17:07.059
needs power and access. Precisely. But if we

00:17:07.059 --> 00:17:08.900
pull back and look at what we've accomplished,

00:17:09.299 --> 00:17:12.240
the core lesson here is profound. Good outputs

00:17:12.240 --> 00:17:15.059
come from good systems. For the past couple of

00:17:15.059 --> 00:17:17.160
years, the entire tech world has been obsessed

00:17:17.160 --> 00:17:20.059
with writing clever, complex prompts. We are

00:17:20.059 --> 00:17:22.319
finally moving away from simple prompts and moving

00:17:22.319 --> 00:17:25.079
toward robust processes. You are building an

00:17:25.079 --> 00:17:27.400
architecture, not just asking a question. That

00:17:27.400 --> 00:17:30.180
is the perfect summary. Let's distill the actionable

00:17:30.180 --> 00:17:32.539
advice for you to take away today. You can build

00:17:32.539 --> 00:17:34.900
this. Start your first workspace folder today.

00:17:35.400 --> 00:17:37.460
Build your assembly line. Put in the upfront

00:17:37.460 --> 00:17:39.700
time to create your source list, your ranking

00:17:39.700 --> 00:17:42.859
rules, and your strict writing style. Keep it

00:17:42.859 --> 00:17:45.019
incredibly focused. Demand that your outputs

00:17:45.019 --> 00:17:48.519
are short and verified. And above all, do not

00:17:48.519 --> 00:17:51.839
grant the AI access to unnecessary files. Keep

00:17:51.839 --> 00:17:53.839
it strictly contained to its workspace so it

00:17:53.839 --> 00:17:56.640
doesn't get confused by your personal data. Let

00:17:56.640 --> 00:17:59.319
the machine work for you, but only on the exact

00:17:59.319 --> 00:18:01.660
terms you define. And I'll leave you with a final

00:18:01.660 --> 00:18:03.980
thought to mull over. We just spent this time

00:18:03.980 --> 00:18:06.640
walking through how to build an AI agent that

00:18:06.640 --> 00:18:10.019
can independently read industry blogs, rank them

00:18:10.019 --> 00:18:12.039
by importance based on your custom criteria,

00:18:12.480 --> 00:18:15.160
and write a human sounding newsletter draft while

00:18:15.160 --> 00:18:17.980
you are asleep. Yeah. So ask yourself what other

00:18:17.980 --> 00:18:20.519
repetitive daily digital chores in your life

00:18:20.519 --> 00:18:22.700
or your business are just waiting to be turned

00:18:22.700 --> 00:18:24.900
into a scheduled co -work folder. What a question

00:18:24.900 --> 00:18:27.839
to leave on. The potential is massive. Thank

00:18:27.839 --> 00:18:29.440
you so much for joining us for this deep dive

00:18:29.440 --> 00:18:31.779
into building your digital employee. We will

00:18:31.779 --> 00:18:32.359
see you next time.
