Imagine an AI that doesn't just talk, but actually,
you know, works while you sleep. Oh, yeah. That
is the dream, right? Think about that for a second.
It's the fundamental difference between having
a simple chat with an AI and building a functioning
independent system. If you want a digital employee,
you basically have to build it a workspace. Precisely.
And that's exactly what we are doing today with
Claude Cowork. Right. Welcome back to the Deep
Dive. Today, we are exploring building your first
Claude Cowork Agent. We're going to cover the
setup, the folder logic, and the automation steps.
Yeah, you probably use Claude every day. But
honestly, you're likely using it wrong, or at
least you're only scratching the surface. Totally.
Today is a literal roadmap for moving beyond
that traditional chat box. We're going to explore
how you can use Cowork to build a real, functional,
daily AI news dashboard. OK, let's unpack this.
Because when I first heard about Cowork, I honestly
thought it was just I don't know, another UI
update. Right, like just a visual thing. Yep,
exactly. Another place to tap a question. But
Cowork is totally different. It's built for repeatable
file -based jobs. It lives inside your folders,
it reads, it writes, and it saves documents rather
than just existing in a fleeting conversation
window where you have to constantly copy and
paste everything. What's fascinating here is
the shift in computing behavior. We are all used
to standard cloud chat. Which is great, obviously.
Oh, it's fantastic for quick brainstorming, drafting
a single email, or just answering a one -off
question. To transition from getting one -time
help to building automated business workflows,
you are no longer a conversational partner. Right.
You are a systems architect. I love that. A systems
architect. So if I'm playing that role, what
are my actual building materials? Let's talk
prerequisites for you guys listening. First,
you need the cloud desktop app installed. Yes.
This isn't running in a browser tab. Second,
you need a paid plan. Pro is the bare minimum
here. And we should probably explain exactly
why that free tier simply won't cut it. Yeah,
please do. Core workflows inherently require
much higher usage limits and significantly more
processing context. Right. When you run a dashboard
workflow, the AI is reading multiple files. It's
cross -referencing your instructions against
several external articles, and it's generating
heavily formatted outputs. It's doing a lot at
once. Exactly. It needs a massive working memory
to hold all that data simultaneously. That makes
total sense. And that actually brings up what
I like to call the work Golden Roll, you need
a dedicated workspace folder on your actual computer.
Yes, absolutely. You might name it something
like Claude Cookework News Dashboard, but I want
to make a hard stop right here because the temptation
is just to give the AI access to everything.
Oh, big mistake. Do not just point Claude at
your whole computer or your entire documents
folder. That is like letting a brand new intern
rifle through all your personal filing cabinets,
your tax returns, your family photos, just to
find a single press release. Yeah, that is an
incredibly dangerous trap for new users. It really
is. If we connect this to the bigger picture
of how language models operate, giving the AI
too much access just creates massive confusion.
Right, because it's reading everything. Exactly.
Every time it reads an irrelevant file, it eats
up its processing context, its working memory.
It has to sift through your grocery lists and
old spreadsheets just to figure out what a news
update means. Wow. That dilutes its focus. You
want to isolate the work space so the AI only
sees exactly what it needs to execute the task.
Nothing more, nothing less. And speaking of focus,
you also have to dictate which underlying model
is actually doing the processing. The sources
are very clear about using the strongest model
available for research tasks. So we are talking
about using something like Claude 3 .5 Opus or
Sonnet rather than the faster, lighter Haiku
model, right? Yes. Precision matters immensely
here. When you ask an AI to analyze multiple
sources, rank them, and synthesize the information,
you are fighting against a phenomenon called
prompt drift. Wait, prompt drift. Explain prompt
drift for me. Sure. Think of it like a game of
telephone happening inside the AI's own head.
Over the course of a complex multi -step task,
a weaker model slowly forgets or misinterprets
the original instructions. Oh, I've seen that
happen. Right. It might start out strong on the
first two articles, perfectly applying your formatting.
But by the time it reads the 10th article, its
working memory is stretched, and it starts hallucinating,
summarizing things in the wrong tone, or just
completely ignoring your formatting rules. That's
so frustrating. It is. So using the strongest
model, provides the immense cognitive stamina
required to hold onto your rules from start to
finish. Got it. So you've restricted the AI's
access to your tax returns, and you've selected
a model with enough stamina to avoid drifting
off course. Right. Now you actually have to build
the physical structure inside that isolated folder.
Exactly. Think of the folder structure like an
assembly line in a factory. To get this digital
employee working smoothly, you need three main
folders on that assembly line. Okay, what are
they? about, outputs, and templates. I have to
admit, creating three separate folders and a
bunch of markdown files just to get a new summary
feels incredibly tedious. It sounds like a lot
of work. It really does. Why can't I just paste
a massive three page prompt into the regular
clawed chat window every morning and get the
exact same result? Well, because copying and
pasting a massive prompt every day isn't a system,
it's a chore. Fair point. If you put all your
rules into a chat prompt, that knowledge vanishes
the moment you close the window. Yeah. By building
the structure on your hard drive, you are creating
permanence. The about folder serves as the permanent
blueprint for the assembly line. Yeah. The templates
folder is the mold. If you don't build the track
correctly and permanently, the robotic arm claw
just flails around in the dark every single time
you start a new session. OK, that clarifies it.
We are trading upfront effort for long term automation.
I can get behind that. Exactly. So let's look
at that blueprint, the ABOUT folder. Our sources
say this is where you store the high signal rules
that filter out the noise before the AI even
begins to work. Yes. The internet is filled with
garbage data. So much garbage. Left to its own
devices, and AI will try to summarize all of
it. The files in your about folder are your hard
filters to prevent that. Let's walk through those
filters. First up is your source list dot dash
MD file. And in this file, you explicitly limit
the AI to five to eight trusted sources. You
organize them by priority. Priority 1 is official
company blogs like Antropic or OpenAI. Priority
2 is research repositories like Hugging Face.
Priority 3 is general business and tech news.
Right. But I'm looking at this limitation and
wondering why handcuff the AI? Why not just say,
search the whole internet for AI news today?
Because a highly curated source list produces
dramatically cleaner reports. If you tell an
AI to search the entire web, it will pull from
SEO, spam sites, unverified Reddit threads, and
out -of -date opinion blogs. That's true. By
restricting it to, say, five official URLs, you
are guaranteeing the data quality. You are also
explicitly instructing it, do not treat rumors
as facts. OK, so we've restricted the sources.
But even within five trusted official sites,
there's a ton of garbage. Oh, absolutely. Companies
published market. fluff all the time. How do
we stop the AI from summarizing a minor interface
tweak like it's a major technological breakthrough?
That brings us to the second file in your About
folder, the DashboardRules .md file. Okay. This
is where you define consequence. You instruct
the AI to rank every single news item on a strict
scale of one to five based on practical impact
and urgency. So how does that play out in a real
scenario? Let's say OpenAI drops a massive new
model upgrade, but on the exact same day, they
also release a minor bug fix for their billing
page. In a standard chat prompt, the AI might
just give you two bullet points of equal length,
treating both events as equal news. Right. But
with your dashboard rules file, the AI is forced
to pause and evaluate. It looks at the massive
model upgrade, sees that it alters capabilities,
and scores it a 5. OK. It looks at the billing
bug fix, realizes it has zero impact on output
quality, and scores it a 1. Your rules can then
dictate only include items that score a three
or higher in the final report. You've just built
an automated editorial filter. You're forcing
the AI to have editorial judgment based on your
criteria. I see. But even if it picks the right
stories, there's still the problem of how it
actually talks. Yes, the tone. Which leads to
the third file in the blueprint, the writing
style .md file. Yes, and this might be the most
crucial file for long -term readability. You
use this strict writing style file to enforce
clear, direct, human -sounding English. Right.
You demand short sentences. You demand factual
reporting. And the absolute most fascinating
part of this file is the banned word list. Oh,
it's so good. You explicitly ban those cliché,
exhausting AI buzzwords. You tell it under no
circumstances are you allowed to use the words
revolutionary, game -changing, unlock, unleash,
seamless, or dive into. This is a brilliant structural
hack. AI models naturally default to marketing
speak. They really do. Think about their training
data. They have ingested millions of press releases
and marketing websites. When an AI sees a product
update, its highest probability predictive text
pathway is to describe it as game changing. So
what does this all mean when we ban those words?
Were you just making it sound less annoying?
It goes much deeper than just tone. Banning those
words physically forces the model's neural network
to access different, more analytical pathways
to complete its sentence. If the AI is literally
prohibited from saying, a new tool will unleash
your potential, it hits a roadblock. It is forced
to look closer at the data and explain the actual
mechanism. That makes a lot of sense. It has
to say, this tool reduces rendering time by 40
percent. It completely shifts the output from
passive marketing fluff to active practical analysis.
So you're saying we have to artificially constrain
the AI to make it smarter. Yes. We take away
its favorite adjectives. to rely on strong verbs
and hard facts. That is a massive paradigm shift.
OK. So we have our assembly line blueprint built.
The About folder is locked. But how does the
AI actually know where to find the blueprint
and what to do with it? That is the job of global
instructions. You find these in the Settings
menu of Cowork. Think of global instructions
as the permanent laws of your workspace. They
act as the traffic director. Right, so your global
instructions would read something like read the
source list, the dashboard rules, and the writing
style in the about folder. Use the template in
the templates folder and save the final output
to the ITPU test folder. Exactly. But here's
my question. If we went through all the trouble
to write these complex rules, why do the sources
explicitly warn us to keep the global instructions
relatively short? Why not just put the band words
right there in the main settings? This raises
an important point about system architecture.
Global instructions exist only to define the
workspace logic. If you shove your one to five
ranking system, your band words, and your source
URLs into the global instructions, the cognitive
load is too heavy at the top level. I get it.
The traffic director gets overwhelmed trying
to build the car while directing traffic. Great
analogy. keep the global instructions short so
they act as a stable foundation just pointing
to the folders. The complex, specific rules belong
safely isolated inside those separate markdown
files. The traffic director just points. The
blueprint does the heavy lifting. I like that.
So the traffic director points the AI to the
sources, filters the news through the rules,
and then point it to the template. Right. Inside
your templates folder, you have a file called
inews -template .md. Exactly. And a weak dashboard
template just asks for a list of headlines. A
powerful, useful dashboard demands structured,
actionable intelligence. The sources show this
template dictating a very specific final output.
It must include an executive summary. It must
display the impact scores for each item that
we talked about earlier. But the most valuable
part is the requirement for content angles. It
literally forces the AI to provide a newsletter
idea or a LinkedIn post idea based specifically
on that news item. What's happening here is the
transformation of passive information into active
utility. You aren't just reading that Anthropic
released a new feature. Your system is handing
you immediate, actionable ways to use that news.
Which is huge. Right. For example, if you are
a developer, your template might demand a code
integration angle. If you are a marketer, it
demands a campaign angle. You are tailoring the
news directly to your daily deliverables. It
is brilliant. We have our isolated folder. We
have our assembly line built. We have our traffic
directing global instructions. Now it is time
to flip the switch and watch this digital employee
go to work. Let's talk about execution. This
is where you see the magic of the architecture
you've built. You open a new task in Cowork,
ensure it is pointed at your specific Cloud Cowork
News dashboard workspace, and give it a simple
one -line instruction. Like what? Just create
today's daily AI news dashboard. That's it. One
sense. That's it. Because of all your setup,
Claude knows exactly what to do. It checks the
blogs on your approved source list. It evaluates
the rumors versus the facts. It ranks the updates
one to five. It forces its neural pathways to
avoid the banned words. And it formats everything
into your template. And along the way, it might...
pause and ask you questions, right? Yes, and
this is a vital part of the co -work process.
It might ask clarifying questions before it starts
generating the final report. Give me an example.
Well, it might say, I found three updates that
score a four today. Do you want me to expand
the content angles for all three or just focus
on the top story? OK, that's helpful. You should
always answer these questions. Providing that
context prevents the AI from making weak assumptions
and filling in the gaps itself. But I have to
push back on this step. If the ultimate goal
of this entire deep dive is to have this thing
work while I sleep, why am I sitting here answering
clarifying questions? I know, it feels counterintuitive.
Why is this first manual run so critical? Why
can't I just build my folders, set my rules,
and immediately automate it to run every morning?
It's a very common impulse, but if you automate
a flawed system, you just get flawed outputs
faster. Oh, that's a good point. The manual testing
phase is your safety net. It allows for early
corrections before you commit to a fully automated
schedule. During this first run, you watch the
tool activity window. If the AI grabs a source
that isn't on your list, or if it slips up and
calls something game -changing, you catch it
immediately. And if it does make a mistake, if
it hallucinates or breaks a rule, is that just
the AI being stubborn? Rarely. And this is a
hard truth for a lot of users to accept. If the
results feel off, it is almost always a thaw
in the system design, not the AI itself. Interesting.
It's an error in your instructions. Maybe your
definition of a level five impact was too vague.
Maybe your source list had a broken URL. The
manual run lets you debug your own rules. OK,
so we've debugged it. We tweaked the blueprint.
We ran it manually. And the output is beautiful.
It is short, it is actionable, it is verified,
and there's no marketing fluff. Perfect. Now
we reach the holy grail, scaling and automation.
Once your manual run is perfect, you can utilize
the scheduling feature. You literally just type
slash schedule in the task bar. Wow. You can
tell Claude to run this exact dashboard workflow
every single morning at 8 0 a .m. Just typing
slash schedule. That feels like magic. Yeah.
But. We need to be incredibly clear about the
technical reality here because this is where
people get frustrated. Yeah, there's a catch.
What are the strict requirements for a scheduled
task to actually fire? The requirements are physical.
This workflow is not running on a server somewhere
out in the cloud while you are disconnected.
Claude Cowork operates locally. Explain the mechanics
of that. Why does my computer need to be involved
if the AI lives in the cloud? Because the AI's
brain is in the cloud, but your workspace, your
about folder, your templates, your outputs lives
on your physical hard drive. For the scheduled
task to execute, it physically needs to access
the files on your hard drive, read them into
its memory, process the data, and save a new
Markdown file back to your local drive. That
makes sense. If your machine is asleep or if
the desktop app is closed, the bridge between
Claude's cloud brain and your local files is
completely subbered. The digital employee can't
get into the office, so your claw desktop app
must be open and your computer must be awake.
So if your laptop is closed in your backpack
at 8 a .m., the scheduled task will simply fail.
You have to treat it like a real machine that
needs power and access. Precisely. But if we
pull back and look at what we've accomplished,
the core lesson here is profound. Good outputs
come from good systems. For the past couple of
years, the entire tech world has been obsessed
with writing clever, complex prompts. We are
finally moving away from simple prompts and moving
toward robust processes. You are building an
architecture, not just asking a question. That
is the perfect summary. Let's distill the actionable
advice for you to take away today. You can build
this. Start your first workspace folder today.
Build your assembly line. Put in the upfront
time to create your source list, your ranking
rules, and your strict writing style. Keep it
incredibly focused. Demand that your outputs
are short and verified. And above all, do not
grant the AI access to unnecessary files. Keep
it strictly contained to its workspace so it
doesn't get confused by your personal data. Let
the machine work for you, but only on the exact
terms you define. And I'll leave you with a final
thought to mull over. We just spent this time
walking through how to build an AI agent that
can independently read industry blogs, rank them
by importance based on your custom criteria,
and write a human sounding newsletter draft while
you are asleep. Yeah. So ask yourself what other
repetitive daily digital chores in your life
or your business are just waiting to be turned
into a scheduled co -work folder. What a question
to leave on. The potential is massive. Thank
you so much for joining us for this deep dive
into building your digital employee. We will
see you next time.