Well, the reality of 2026 is actually quite stark.
Beat. The real productivity gap isn't between
those who use AI and those who don't. It is between
those who just chat with it and those who architect
an autonomous digital workforce. That is exactly
the dividing line. I mean, it changes absolutely
everything about how we work. Welcome to the
deep dive. We are really glad you are here. Today,
our mission is to unpack a comprehensive 2026
tutorial on this topic. We are exploring the
multi -layered Claude ecosystem. Okay, let's
unpack this. We will explore the three distinct
rooms of this system. We have chat, co -work,
and code. We will dive into the power add -ons.
Those are skills, connectors, and plugins. And
finally, we will show you how to push this AI
further. It can manipulate your local files.
You can run automated schedules while you sleep.
It really is a massive shift in perspective.
You know, people often treat the system as just
a single website. You log in, type a question,
get an answer. But that is fundamentally incorrect.
It is actually a triple product strategy. Right.
It's not just a single conversational interface
anymore. Exactly. Using only the chat window
is incredibly limiting. It is like buying a massive
professional toolkit. But then, you only use
one small screwdriver. You are missing out on
incredible power. The ecosystem is actually three
distinct rooms, and they all share the exact
same underlying brain. But they serve very different
functional purposes. Yeah, completely different.
Chat is for fast... transient tasks, you know,
things under 10 minutes. Cowork is for long -term
project persistence. It actually remembers things.
And code is for local software development. But
here is the critical step for any of this. You
absolutely must download the desktop application.
I have to pause you there. Why is the web browser
not enough anymore? We have been doing everything
in the cloud for years. What fundamentally changes
for power users? Well, browsers are limited by
design. They are walled gardens. They protect
your computer, which is good for security. But
the desktop application unlocks agentic features.
Those are tools letting AI act without constant
human prompting. The desktop app lets the AI
interact directly with your file system. It bridges
the gap between the cloud and your actual machine.
So real automation needs the desktop app to actually
touch your local files. Right. And without that,
you are just talking to a screen. Establishing
that we need the whole toolkit is step one. Now
we should look at the most familiar room first.
We need to uncover its hidden mechanics. That
brings us to the chat product. This is where
everyone naturally starts. It is built purely
for speed and immediate answers. Think of it
as your everyday workspace. It is highly reactive.
You draft quick emails here. You summarize uploaded
PDFs. You can parse dense financial spreadsheets.
You drop a raw CSV file in, and it reads it instantly.
But chat has a major structural problem. It forgets.
Once you start a new conversation, your custom
instructions vanish. That is exactly why they
built the project's feature. Projects are fascinating.
Think of a project as a highly sterile clean
room. Like a decontamination chamber for your
data. Precisely. You can uplay your specific
brand guidelines into this clean room. You establish
very rigid custom instructions. You might say,
always use a formal tone and never use emojis.
And crucially, those rules stay trapped inside
that specific room. Yes. When you leave that
project to ask a random question, the main system
completely forgets those strict rules. It doesn't
bleed over. It keeps your main chat perfectly
clean. I have a vulnerable admission here. I
still wrestle with prompt drift myself. I keep
forgetting to use projects and losing my custom
rules. Beat. It just gets messy so fast. Oh,
we all do it. You start asking about dinner recipes
in your work thread, suddenly the AI is confused.
But inside a project, the system also quietly
builds a background memory file. It learns user
details over time. It just happens automatically
behind the scenes. And then we have artifacts.
This feels like a huge leap forward in the interface.
Artifacts are essentially visual mini -apps.
They are built right in your browser cab. Like
asking it to build an email generator with clickable
tone buttons. Exactly. Instead of just giving
you the text of an email, it writes the underlying
code. Then it renders the interface right there
on your screen. You can click around and use
it immediately. But wait, if it is building software,
how do artifacts differ from actual programs
installed on my computer? They are entirely temporary.
They only live inside your current web browser
tab. They do not save to your local machine at
all. The moment you close the tab, the interface
is gone. Got it. Artifacts live in the browser,
not on your actual hard drive. Correct. So chat
is perfect for these one -off tasks. But what
happens when you need the machine to learn? What
if you do the exact same data entry task every
single Monday? That is when chat becomes highly
inefficient. You don't want to type the same
prompt every week. We need to transition from
a casual user to a system architect. We need
repeatable automation. This brings us to the
power add -ons. Skills, connectors, and plugins.
These separate the casuals from the professionals.
Let's start with skills. I like to use the analogy
of a physical filing cabinet. That is a perfect
way to visualize it. The AI only reads the detailed
step -by -step instructions when the specific
label is called upon. It keeps the whole system
moving fast. Right. It does not read the entire
cabinet every single time you say hello. It just
grabs the one specific file you request. And
the mechanism to make a skill is remarkably intuitive.
You do it naturally right in the chat. You just
run a perfect prompt until it works. Then you
literally tell the AI, please package these exact
steps into a reusable skill. It is that simple.
It writes its own documentation. Next, we have
connectors. Connectors give the AI actual read
and write access to other applications. Let's
break that down so it can read my unread messages.
Yes. That is a basic read action. Or it can write
new tasks directly to your digital to -do list.
That is a write action. It essentially builds
a bridge from the AI to your existing tools.
Wait, so if I have a connector reading my inbox
and a skill drafting replies, do I have to manually
trigger them both every time? Yeah. Because that
sounds exhausting. That is exactly why plugins
exist. Plugins are bundles. They bundle many
skills and connectors together for entire job
roles. You use simple slash commands to activate
them. By typing forward slash skill creator.
Exactly. You type that command and bypass typing
long explanations, the AI just interviews you,
it asks what you want to achieve, and it builds
the skill automatically. But where are these
skills permanently stored so they don't vanish?
You just said chat forgets everything when you
close the tab. If you make them in normal chat,
they do disappear. You must save them into your
dedicated workspace. They live permanently inside
the Cowork or Code areas. Skills vanish in regular
chat. They need to be saved in Cowork or Code.
Exactly. Since these custom skills need a permanent,
stable home, it is time to transition. We have
to enter the second room of the ecosystem. This
room is designed entirely for long -term memory.
This is Cowork. If chat is a whiteboard you erase
every day, Cowork is a dedicated office. It is
about setting up a workspace that remembers your
decisions from yesterday. The first requirement
here is purely structural. You absolutely must
create dedicated, specific, local folders on
your computer. Like a folder named Q3 Marketing
Presentation. Yes. No giant catch -all folders.
Do not just give it access to your entire documents
folder. That is a massive mistake. The AI needs
a clear, isolated boundary to read and write
files safely. And inside that specific folder,
there is a very critical file. Let's talk about
how it actually works. You were talking about
the Claude .md file. This is essentially the
memory core for your project. When you sleep
and shut down your computer, the active session
ends. But the next morning, the system boots
up and reads that Claude .md file first. It parses
the text to rebuild its context window. It remembers
everything from the day before. Two -sec silence.
Think about how powerful that is. The context
is perfectly preserved. It knows exactly where
you left off. And then we add the schedule feature
into the mix. This turns the A into a true autonomous
agent. You can set it to run entirely without
you. Whoa, Pete. Imagine waking up and the AI
has already checked your calendar, scanned your
inbox, and drafted a clean 8 a .m. briefing.
What's fascinating here is the fundamental shift
in our daily behavior. You are no longer sitting
down and doing work to create an output. You
are simply reviewing the AI's multi -step autonomous
work. It is a profound change in our human relation
to software. But I have to play devil's advocate
here. What about the danger of the AI putting
files in the wrong place? If it is running while
I sleep, couldn't it mess up my system? That
comes right back to the folder structure we just
discussed. Cowork is strictly restricted to the
specific folder you assign to it. It physically
cannot wander around your hard drive. That's
why isolated folders are crucial. It keeps the
AI from messing up your desktop. Precisely. Control
the environment and you control the agent. It
is entirely safe if you set the boundaries. Mid
-roll sponsor read. Okay, we have covered chat
and co -work, but there is a ceiling to dragging
and dropping files. If you actually need to build
entirely new tools from scratch, you need more
access. Which leads us to the third room. We
are entering the most advanced, highly technical
space. This is building software with code. Claude
code is a completely different beast. Here the
AI is no longer contained in a neat browser window.
It types directly into your computer's terminal.
Let's clarify how that works. When you give it
terminal access, you are bypassing the visual
interface entirely. It is typing raw commands
directly into your operating system's brain.
Yes, it is pure text -based execution. Cloud
Code can install software dependencies locally.
It can spin up local servers. It can build complex
programs from scratch. I mean, it is essentially
a senior engineer sitting inside your machine.
But you have to choose the brain for this engineer.
You have to actively select. the model it uses.
Model selection is absolutely critical here.
Models are just the specific neural networks
processing your request. You have three main
choices depending on the test. Sonnet 4 .6 is
the first one. Sonnet 4 .6 is your best overall
choice. It beautifully balances speed and extremely
high intelligence. Most of the time, for everyday
coding, you use this. Then there's Opus 4 .6.
Why wouldn't I just use the smartest one all
the time? Because Opus 4 .6 is heavy, it is computationally
expensive, it is noticeably slow, you only deploy
Opus for incredibly complex, deeply difficult
logic problems that nothing else can solve. And
finally, we have Haiku 4 .5. Haiku 4 .5 is incredibly
fast and light. You use it for simple, repetitive
tasks like formatting code. It saves energy and
runs almost instantly. And beautifully, the system
allows you to switch between these models mid
-conversation. Now, because this tool writes
files directly to your hard drive, we have to
talk about permissions. Giving an AI access to
my terminal sounds like a cybersecurity nightmare.
How is this actually safe? Well, it is a valid
concern. Beginners absolutely must leave permissions
toggled on. The system will explicitly ask you
before it runs any command. It pauses and waits
for your human approval. It is safe because you
are the gatekeeper. But experts can turn those
permissions off for pure unhindered speed. That
seems like a huge risk of accidental file deletion.
It is a massive risk. If permissions are off,
it will just execute its own logic. If it makes
a mistake or misunderstands a prompt, the file
is simply gone. So should a non -coder ever turn
those terminal permissions off? Never. The risk
is simply too high for the average user. Do not
touch that toggle. Keep permissions on. The speed
isn't worth accidentally deleting your whole
project. Right. And there is one more advanced
feature here we should touch on. Remote control.
Checking your computer's progress via your phone
browser. It sounds amazing in theory. It sounds
great, but practically it has severe limits.
Your physical computer terminal must stay open.
Your laptop cannot go to sleep or lose Wi -Fi.
There is a strict 10 -minute network timeout.
And frankly, it requires the expensive $100 a
month max plan. That feels very restrictive for
an autonomous future. It is restrictive. For
real, heavy lifting while you're away, a server
-based agent is the superior alternative. It
runs entirely in the cloud 24 -7 without relying
on your physical laptop. If building local software
via terminal feels too intense, that is completely
okay. Not everyone needs to be a developer. There
are much more familiar places to deploy this
intelligence. Exactly. We are moving beyond the
desktop application now. We need to weave this
AI into the mundane tools you already use every
single day. Let's start with the mobile application.
The mobile app is excellent for native file access.
You can upload photos or documents natively stored
on your phone while you are commuting on the
train. It seamlessly syncs all that data back
to your main co -work workspace. And then there
is the Chrome extension. This has a feature that
legitimately blew my mind. You're talking about
the Teach Claude button. Yes. How does a text
-based AI even do this? You click it. and the
machine literally watches your mouse movements.
It watches exactly where you click on a screen.
It is mapping the DOM elements. Those are the
structural building blocks of a web page. It
takes rapid screenshots, maps those elements,
and translates your X and Y coordinates into
an executable script. So it learns how to navigate
complex internal company websites just by observing
you do it once. It converts your visual human
actions into a repeatable automated script. It
is incredible technology. And finally, we have
Excel integration. We all use spreadsheets. It
is the universal language of business. Co -opting
directly into Microsoft Excel gives the AI very
special localized powers. Imagine a Tuesday morning.
You drop in raw messy CSV data. What happens?
It can physically highlight cells. It can write
complex VO up formulas for you. It can conditionally
change row colors based on subtle data trends.
It can generate simple pie charts of your spending
habits instantly. But how does this Excel integration
differ from just asking for a formula in chat?
Well, in chat you get the text. You have to copy
the formula, open Excel, find the right cell,
and paste it yourself. The integration does the
physical work for you. It manipulates the spreadsheet
directly via an API. It actually executes the
math and builds the charts right inside your
spreadsheet. Exactly. It completely removes the
friction between getting the answer and applying
the answer. So what does this all mean? If we
connect this to the bigger picture, We are witnessing
a profound evolution. We are building complex
automated systems now. We are using customized
skills. We are establishing specific local folders.
We are deploying scheduled agents to do the heavy
lifting. It is ultimately about buying back your
own time. By architecting this digital workforce,
you are saving hours. every single day. You automate
the routine algorithmic tasks so you can actually
focus on deeply human important work. But we
want to encourage you to just start small. Do
not try to build a massive complex software project
today. That will just lead to immediate frustration
and burnout. Start with just one thing. Create
one isolated project room for a specific client.
Or package just one repetitive weekly email task
into a single skill. Take it one highly intentional
step at a time. It stacks up quickly. You build
the foundation and the automation goes from there.
And I want to leave you with one final thought
to mull over today as you look at your own workflows.
Two secs silence. If an AI system can now learn
a complex multi -step task just by watching where
your mouse clicks on a screen, how long until
our job description aren't defined by the specific
software we know how to use, but by the entirely
new autonomous systems we can imagine building.
Out T -Row music.