Claude Code has gone viral, one of the fastest growing apps of
all time, reaching over a billion dollars in run rate
revenue within just six months of its launch.
It's so popular even non developers are now using it for
things that Anthropic never ever anticipated.
I'm definitely one of those. I've started using Claude Code
in Visual Studio, but just a programming interface for tasks
I never expected, including even creating documents.
But for most people, Claude Code still feels very technical.
Going into a terminal, running commands, staring at a dark
screen, it's a it's a big barrier.
And so onthropics team who built Claude Code has now built Claude
Co work. So it's the same agent wrapped
in an interface that everybody can use, your head of marketing,
your operations team, probably even your dad could use it.
Today I want to breakdown exactly what Claude Co work
actually is, explain to you how it works and why I think this
might be one of the biggest product launches in AI for many
years and actually what it signals about where the entire
industry is heading. This is in the loop with Jack
on. I hope you enjoy the show.
So let's start with what Quad Co work actually is.
Quad Code has been named one of the worst name products in AI
because the Co part just actually just distracts from
what the tool actually does and represents.
It was never just about coding. It's a, it's a general purpose
agent that just happens to be really good at coding, but it
can do a lot, lot more Co work isn't just this dumbed down
version of clawed code. It's literally the same agent,
just with a completely new interface.
Mindset itself has taken a huge amount of inspiration from
clawed code, the way it does its reasoning, the way it structures
its tools and systems that it can access.
So being able to provide this type of technology just with a
new interface for everyone is going to be incredibly powerful
because ultimately you download the desktop app, there's going
to be a few different tabs when you access clod, there's chat,
there's Co work, and then there's code.
Obviously the code is clock code and Co work is Clod Co work.
Now on the face of it, it's like, OK, cool, what does it
actually do? Well, it can do a lot of things
that regular clod cannot do. And Clod code can do so it can
interact with files on your computer, it can control your
browser. It can connect to external
systems, as you know clod does, but doing all of that in one
place is where it becomes very powerful.
In particular, I'll talk about why interacting with files is so
damn important. So for example, instead of just
chatting back and forth and then creating a artifact.
So if anyone's used clod, it's an artifact.
The equivalent on ChatGPT is a canvas where you know, text
appears or whatever you're creating appears.
So it's where the chat just moves to the left and creates
what looks like a kind of document on the right hand side
that you can interact with. And a lot of companies have
spent a lot of time trying to define what this is going to be.
And I think it's really interesting here, and I'll come
on to this later, that actually perhaps just the winner of the
new artifact or asset that AI creates and then you share with
others is not a artifact or a canvas or a whatever thing that
people are naming it. It could just be a local file
that's downloaded straight onto your desktop.
So yeah, called Co work and then open those files on your
desktop, rename them, create spreadsheets, navigate websites.
It could send an e-mail and you might go, OK, cool.
That's what a lot of these tools seem to do.
But there are big differences. Now I'll come onto exactly what
those are and why it matters later.
So to help understand why this is also important, I'm going to
first start with how it actually works, like how you could use
this, what you'd use it for. Because really one of the things
I'd love for people to do is go and test this and really use it.
It's not, you know, like a new browser that's kind of
interesting, but probably unlikely to make you change.
This is actually something really, really powerful if you
can systematize it and get it across all your company.
So when you open Co work, you're starting I guess with a blank
slate. You know, it can't see any
files, it can't access the browser, it can't do anything.
You have to grant access explicitly.
So for files, you can give it access to specific folders, just
the ones that you want to work with.
For the browser, you can obviously you can install a
Chrome extension that lets you see and interrupt with any web
app for external services like Slack or you know, whatever
system that you're using, you can just connect it through
MCPS. So it's just like the cloud
interface today. And anytime it wants to do
something, so change a file, interact with something, it will
come and ask you first and you can always say no.
So you can stay in a lot of control.
So I'll give you a, the most simple example, which doesn't
really make it seem like super exciting, but then I'm going to
build on that a little bit. So you might have a shed load of
receipts in your folder because you know, you've been travelling
with work, you've been doing stuff and you've taken
screenshots, you've got it on your PC or your phone or
whatever it is. And essentially it's just a a
bunch of files with very unhelpful names like receipt,
one dot, PDF, March, GPG, you know, whatever it might be.
You can then give Claude access to that folder, giving Co work
access just to that specific location where everything's
stored. And now you can ask, I have a
receipts folder. Can you just rename all those
files to say match the dates on the receipts?
And it's super simple, but actually quite helpful for
anyone that's up to, to do invoicing and, and get paid by
their company for the things that they've already bought at
work. But anyway, so, so the agent can
just open that folder, look at each receipt, read all the
content and information inside it to find the correct date, and
it just then renames the files on your local device on your
laptop. If one of the dates might be
missing a receipt, instead of guessing or doing something kind
of weird, it stops and asks, you know, this receipt doesn't have
a date. What should I do?
And it's because Anthropic, they've spent a lot of time on
trying to get the model to ask for clarification or this agent
to ask for clarification when it's uncertain rather than
making assumptions that are just often crap.
And it sounds like a small thing, but it makes a massive
difference in practice. Now, you could then go further
with this receipt example. You could then say, can you put
this into a spreadsheet? And it will be able to actually
do that. It creates an Excel file with
all the receipt data, dates, amounts, whatever was on there
organize into columns just like that.
And then maybe you don't want this local file just sitting on
your computer. Maybe you want it in Google
Sheets so you can share it with your team and then everyone can
access it from anywhere. So you say actually go make this
a Google Sheets instead, and the agent opens the browser and can
navigate to Google Sheets, create a new spreadsheet and
start populating it with the information.
You can watch it type around, control the interface, and now
it's not quick, it's not instant, but you can watch it
working through every single step.
And at one point it might notice it didn't do something correct,
so it has to go back and it fixes it.
So it's checking its own work as it goes.
There's been a lot of progress made on computer use in the last
kind of 6 to 12 months. And then you say now, OK, go and
take this and send it to this person, let's say Amy, and the
agent will be able to open Gmail, Click to compose or
create new e-mail, type in Amy in the receipts field and
actually pull from contacts. Now an objection many people say
is that this is just very slow. It's cool, but very slow.
I could do all of it myself. And this is where cloud cowork
gets a massive amount of insiration from cloud code.
Because yes, you could do something like that for a single
task faster. And you're definitely faster
than most agents right now for doing tasks on the Internet,
let's say. But the real shift is doing
things in parallel because while cowork is doing one thing, you
could be just doing something else.
So this is something that called code got really, really right.
You can create many agents that are doing many different things
all at the same time. For example, you could have one
creating a plan, 1 executing part of the plan that you're
happy with, one writing tests to assess the quality of the either
plan or the things that have been written, the code all on
one project, whilst another agent is then planning your next
task. So that's what you can do now.
You can do that on Claude today with many, many different tabs,
but cloud Co work is trying to bring the ability the cloud code
got so right into the main application.
And what's really important here, and this is what we'll
come onto and spend a bit of time on, is everything it
creates, say every file it creates, it persists because it
can be saved as a local file on your device and you can just
point it at that local file. You don't need to make AGPT or a
cloud project, or you might create loads of, let's say,
artifacts or canvases that you might put in a Google Doc and is
then lost in many ways for contexts that another future use
of AI could have benefited from. Instead, you can literally every
file, save it, put it in the folder, organize it in a
specific way, and suddenly the agent will then access it
anytime. And this overcomes many, many,
many big problems, which I'll come onto in a minute.
Now developers have been doing this for years, creating and
storing files on their local device, committing it to the
entire project. This is a very normal thing.
And This is why I think non developers are about to go
through what developers have been doing for many years and in
particular with AI over the last 12 months.
And they're going to do a break net speed.
So we're going to see a huge amount of change in how people
generally work because I really think the difference between
say, chatting with normal cloud and working with coworkers, the
difference between having conversations and continually
building a system. And it's a real mental shift
that everybody must really take on board because the ability to
create files and save them straight on your device
overcomes, oh, many big problems.
So for anyone that's been using normal cloud, you might end up
hitting context window problems. And you do the same on GP2.
So you've put too many files in, you've had too many
conversations, you've had too many iterations.
It says I've hit the file context in it, which is a pain
because you might be on a really good track and suddenly you have
to go make a new chart. All of that can go away because
you can suddenly just create a chart and all the files you've
saved and assets. Not if I've put it into a
folder, point cowork at that folder quickly and it's done so
super, super quick. Another huge benefit outside of
context windows here is the way you can create a giant system.
So a good example here with how we work.
So essentially if you create the right folder structure and give
Claude access to certain folders to run instructions, and you
tell it to always follow this folder full of the instructions,
it will always do that. So it's like creating a giant
project, but the agent won't just be stuck on one set of
instructions. So we've got a folder which
essentially is an agent because when people go, what is an agent
or why the what's the value of an agent?
It's literally just a series of prompts that fire at the perfect
moment to provide an LLM with the perfect amount of context to
complete that task. So what you're doing by creating
a folder structure is essentially pointing an LLM at
folders full of information. You might have a folder called
planner, you might have a folder called researcher, you might
have a planner called Presentation Maker, whatever it
might be. And inside that there is a set
of instructions, which is essentially just a document
saved as an MD file. An MD file is just the best
format for an LLM to read. But you know, imagine it's
equivalent as a Doc X. You can download a file as an MD
file. So you create a folder on your
local device with a set of instructions, which are
essentially your agents. Because when you say to an LLM
essentially go read that folder, it's going to use that context.
You might then create another one called skills.
Skills are processes that this agent can follow.
So you might have one called a product requirement document
creator. You might have another one
called Python Writer, you might have another one called e-mail
writer. So these are skills, which is
essentially a document which lays out in a specific format
how an LLM should follow a set of instructions.
So as soon as it then understands that this person is
asking for a certain task, it will be able to essentially
access that folder with that skill and read it and then go,
OK, I know I need to use this skill in this particular way.
And it will fold the process that you set out.
And all you can start to do here is continually build this
system. And every time you create a new
file, you can commit it to the right folder and an agent can
then read it further and further over and over again.
So you're creating a giant system here.
And this is what we've done it from a engineering perspective.
It's what we're doing across the company.
It is literally going to transform how people work.
Another major mental shift with this here is just this idea of
having many, many, many different claws or AIS all the
time. See, it is like tending to your
claws or tending to your AI because instead of going super
deep in one thing, you have many things going on at once.
You're going to check in on each one of them because it might be
going for three minutes, 5 minutes, 12 minutes, thirty
minutes on a specific task and you check in to make sure it
doesn't need to be redirected for some reason.
And you're just doing this across all your different
clauses. And that's how programmers have
been coding for the last 12 months with all these new AI
tools. It's how I've been using
different systems and now Co workers bringing that to
everybody. One thing again, I mentioned it
earlier about skills. I think this is going to make
things increasingly better for people over time.
Essentially, skills is when the agent creates, let's say a
spreadsheet, it can load up something called an Excel skill
behind the scenes, which is essentially a set of
instructions that teach claw the best way to create a Excel
spreadsheet. Now Anthropic provides
prepackaged skills. We as mindset, we build skills
all the time. But what you can also do is
build your own set of skills with MD files so you can start
to build these skills that are very particular to your team and
process. Now that said, if you're just
getting started, of course, don't over customize, don't go
too deep. Start simple.
Go online and search for Co work file structure, for example, and
look online for the best way to create and set up the system.
You know, Anthropic have actually run competitions for
programmers on the best system design and that's where we've
got a lot of inspiration from. So yeah, start very simple.
Install the Chrome extensions, install the app so you can
actually interact, you know, with web as and do things and
justice. Start using it and force
yourself into this new way of working because I promise you
it's going to be infinitely better.
There's a reason Cloud Code has been so successful.
So yeah, I think let's move on to a few interesting use cases
and what I think is practical tips here outside of how this
all works and why it's a bit of a mental shift.
I think some of the really interesting use cases here is
large processing of content. So there was someone called
Lenin Rychisky. So he's a big podcaster in the
space, in the product management space.
He basically gave access to 320 podcast scripts on his laptop,
which is about 450 to 600 hours of content, so a huge amount
that would take forever in let's say if you were uploading that
to ChatGPT or clot because you can't drag 320 documents into a
chat window badly. With Co work you can just
literally point it at a folder done as another use case.
One of the fantastic things about accessing your actual
local files is the ability to produce lots of local files and
then get AI to organise them better.
So you might constantly be creating folders and systems.
And This is why looking online for a really good initial system
to structure this around is good.
But as you create more files, you download it, you save it,
add it to new folders, you can then just ask Claude to go and
reorganize it, tidy up and make it efficient for whatever system
you're trying to design it. So again, using AI to prune the
AI system that it's going to be following.
Another big use case here, I think is actually starting to
like unlock things like APIs without being someone that's
technical because most vendors, most technology platforms offer
APIs that allow you to do really interesting stuff, pull
interesting data straight into your conversation.
It allows you to just do a lot of different things.
And for most people, they cannot use those unless they're a
developer. So suddenly a big use case, it's
emerging and it started because of non-technical people using
cloud codes or any coding tool for that matter.
But suddenly with Co work, because it's the same underlying
agent, it's going to be very good at doing that as well.
Another big use case area is browser based tasks.
So it's really the team has really tried to be good at using
the browser. I think there's going to be an
emerging space to try and create general purpose agents.
So an agent that will sometimes use skills, sometimes use
browsers to essentially be able to go and complete specific
tasks, e-mail things to people and do things for people.
And again, it can be slow and cumbersome.
But if you don't see it as a function of speed all the time,
but having many different tasks all happening at once and you're
just checking in on your clouds or your AIS, then I think this
becomes and is more powerful over time.
And finally, when it comes to research, I think it's really
interesting because you know, if you do research on different,
you know, AI systems, you download the file, you might use
that somewhere else. You have to upload it now.
You could just get it to research many different things
but smaller tasks and just download it all into a project
called Research on xtopic and then start a new chart with 0
context. So the context window is big,
which means you can do lots of work on that research and point
it straight at the folder. So again, I hope you're starting
to see the compounding impact of doing task saving files,
organizing the files with AI, and then starting new chats and
doing it again. But pointing at that folder that
you've created just then. It's really powerful.
So finally, I guess you can see that I'm relatively excited by
this, but it's going to why I think this overall is going to
be a really big deal. So I think #1 it's a platform
moment, and it really showcases the power of, I guess, nobody
really knowing where things are going to go.
You know, when the App Store launched, the initial apps were
beer drinking simulators. You know, where you got the iPod
and you pretend you're drinking a beer.
Nobody predicted Uber or Tiktok, you know, nobody had an idea
what that killer use case was. And I think we're at the exact
moment with agents Cloud code was created for programmers, a
very specific thing and suddenly everybody lived it.
And so they suddenly had to diverse and move out of just
coding. See, I think it's going to spawn
a load of new potential ways of doing things.
And I think the other big area of the reason I think it's
really exciting is the exponential impact of this is
real. You know, a year ago people at
Anthropic were saying by the end of 2025, most people won't be
writing code. And I think it sounded very
aggressive. I actually commented that it
sound quite aggressive, but many engineers are having most of
their code written by Claude. It's certainly the case that
most of the code that is produced at Mindset is now with
AI, and I think that's the trajectory for coding.
But the same thing is going to happen across all forms of work.
If you listen to last week's episode on what trends are going
to be there for 2026? A couple of them were everybody
at knowledge workers becoming an AI manager and knowledge work
having a transformation akin to what programmers has.
And this is a big reason why. See, I think often we think in a
they think progress is going to happen in a linear way, but
sometimes it can happen exponentially.
I think this is going to be one of those instances.
I think another big reason this is going to be so impactful is
that everybody is going to follow.
Open Eye is obviously going to copy this.
Google is going to do something, Microsoft is going to do
something, probably built into Windows and distributed to
billions of people. Apple are going to incorporate
it in some way over the next few months.
It's going to be in added into things like Adobe and other
forms of systems like that. The entire industry is just
going to converge on this pattern.
And what I think is really interesting it actually that
there's a lot of investment into things like the idea of a canvas
or an artefact or a new, I guess, file type that happens
within the AI system that you can create things with
collaboratively with AI. And what's funny is that I think
maybe what we've just seen is that simply downloading a local
file onto your laptop, the oldest thing in the book, I
think that's been around for the longest, might just be that
winner. I think this is going to be
massive. I think that's going to reshape
much of the industry. I think this is going to lead to
a big transformation of how products are going to be built
and designed. In short, I think the genie is
out of the bottle. So yeah, in conclusion, I think
Co work is clawed code without the terminal, the same agent.
It's accessible to everyone, it works on your files, it controls
your browser, run tasks in parallel.
So you can have many different Claws or AIS going at once.
And I think this shift is going to be massive.
And in particular, I'm really excited to see people start to
think about files that you save in folders on your laptop being
something that you spend a lot of time thinking about the
system and structure of that, and whether that is going to
just persist for those that live.
I guess it might feel for some people somewhat technical, even
though it's really not, because it's slightly different to the
way they work today. Or whether this becomes the de
facto standard across the industry.
Or whether some people try to replicate the idea of files
being saved in that format but on an application that feels
more like AG drive. But anyway, I think the industry
is going to converge on this pattern and the people who build
intuition and new processes around this tool now will just
have a huge advantage to how they work.
So yeah, download the app, connect your folders, start
simple, but please do start. This is in the Libra Jackal and
I hope you enjoyed this week's episode.
I had a lot of fun going through it, getting really stuck into
Cloud Co work and I look forward to speaking to you next week.