This content introduces four key upgrades to Claude's functionality, transforming it from a time-wasting tool into a profitable business partner by addressing its inherent limitations and optimizing its output for revenue generation.
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So, I figured out how to turn Claude
Code into the best business partner I
could ask for. And I made three times
more money in the past 30 days. You see,
Claude has these problems that a lot of
people don't ever notice. And every one
of those is costing you time and money
on stuff that's never going to work. So,
what I did is I built a set of four
upgrades to fix every one of those
issues. So, these four upgrades turn
Claude into something that actually
makes you money instead of just wasting
your time. And it doesn't matter if
you're trying to build an app or you're
running an agency or you're doing AI
consulting. This works for anything that
you want to do inside of Claude code.
So, in this video, I'm going to show you
guys the four upgrades and exactly how
you can use them to make more money. So,
let's get into it. Claude has a few
habits that quietly work against what
you're trying to do. Little things that
you might not think twice about. So,
think about how most people use Claude.
You open it up, you type what you want,
you get an answer, and you just kind of
assume that that is the best possible
answer that you could have gotten
because, you know, Claude code is one of
the best AI tools out there, and the
models underneath it, like Opus, are
super super smart. So, it's very easy to
just trust what it says. But there are
these errors that are baked into
Claude's design that make your results
worse than they should be. So by
default, Claude is tuned to make you
feel productive. It is not tuned to make
you money. And these are two completely
different things. And every one of those
design errors is costing you money
because your income is basically capped
by two things. The first one is the
quality of your output. And the second
one is how fast you can produce it. So
the better the output you get and the
faster you get it, the more money you
can make. I'm sure we can all think of
many specific moments where it felt like
Claude was just trying to get us to
spend more tokens or was lying to us
about features that it had built or you
know you feel like you're just repeating
yourself a ton. But the good news is you
don't need to go rewrite Claude's
codebase to fix any of these things. You
literally just need these four upgrades.
And before I started using these, I
remember launching promotions that did
not do very well at all or shipping
automations that were silently failing
or pushing out websites or apps with a
ton of bugs. So that's basically the
whole arc. But before we get into the
first upgrade, if you want to get these
prompts and skills and see how your
results get better, then you can get
them for completely free inside of my
free school community. The link for that
is in the description. Okay, so the
first upgrade fixes the biggest one,
which is just Claude agreeing with
everything you say. I mean, haven't you
guys ever noticed that you tell Claude
you want to do something and it pretty
much will always say like, "Hey, that's
a great idea. You're really smart."
Because it wants you to like it. But
then what actually happens if you say
like, "You know what? I changed my
mind." It will once again come back and
say, "You know what? You're really
smart. I'm glad you changed your mind.
That's a great idea and it's getting
better over time as the models are just
getting smarter and smarter. But this is
actually documented. Researchers call it
sycopant which is just a fancy word for
AI being a yes man. There's a study also
called elephant which measures exactly
this. And they found that AI models fail
to push back on the way you frame
something about 88% of the time and for
humans it's around 60%. And it actually
gets worse the more the model knows
about you. Researchers at MIT and Penn
State found that the personalization and
memory features tend to make the model
more agreeable over a long conversation.
And so that's tough because basically
the longer you work with it and the more
you use it, which is what we all really
should be trying to do, the better it
gets at telling you what you want to
hear. So this is a pretty simple fix.
You ask Claude to start challenging you
and pushing back and playing devil's
advocate before it builds anything or
before it approves any plan. And that's
the whole idea behind a skill that I
built called roast. It basically pulls
Claude out of agreement mode and it
forces it to stress test your idea and
its own work instead of just approving
everything. So basically what roast does
is it spins up a whole council of
personas and they attack it from
different angles. You've got a
contrarian whose only job is to find
fatal flaws. We've got an expansionist
who's looking for the biggest upside.
We've got a first principles thinker
who's working with no outside context,
just pure logic. We've got a deep
researcher that actually goes in and
pulls out a bunch of real market data
and competitor pricing off the web. And
then we have the buyer who actually role
plays being your customer and tells you
straight up if they buy the thing or
not. And then finally, the judge takes
all of those findings and gives you one
verdict. you basically get green light,
reshape, or kill. And it also gives you
the single cheapest test that you can
run in the next 48 hours to find out if
the idea is even worth pursuing, even if
it was reshaped. And so what I'm going
to do is throughout this whole video,
I'm basically just going to build a
little business from start to finish. So
you can see each upgrade working on
something real. So the idea that I want
to build out is a $9 a month tool that
turns a YouTube transcript into a week
of LinkedIn posts. So let me actually
just go open up Cloud Code and roast it
live. All right, so here we are right
now in a fresh Cloud Code project. You
can see right here, all we have is a
cloud.mmd, which basically has like
nothing in it. I just told it that your
job here is to help us make some money.
And then we have our claude with a skill
in here. And this is the roast skill
that I was just telling you guys about.
So, all I'm going to do is do a / roast
and say, I have this idea to make a $9 a
month tool where people drop in a
YouTube video link and that transcript
gets turned into a week's worth of
LinkedIn posts. So, I'm going to go
shoot off that message. So, as you can
see here, before it runs the council, it
has three quick questions to ask us. So,
the first thing is, who's the actual
target buyer for this $9 a month tool?
And let's just keep this as broad as
possible for now and really see what the
council can do. I'm going to say anyone
with a YouTube link. What is your edge
here? What do you already have? Let's
just say that we have, you know, no real
edge. We have no distribution, but we
can build something fast with cloud
code. And we'll shoot that off. And
then, what are our constraints and
budget? How fast do you need to get the
first dollar? Let's just say we have um
a little bit of runway, but not too
much. So, we'll shoot off those answers.
And now we should see the actual council
get spun up. So, here is the brief that
the council is going to judge, and we're
going to see each of these agents get
spun up. The contrarian, the
expansionist, and then the other ones.
And while this is running real quick,
what I want to do is take this, open up
another session, and just say this is my
idea, and just say, do you think this is
good? Do you think this will work? Do
you think I can make money? And it'll
just be cool to come back to that after
we see what the council says and see
what it would have said if we didn't do
that. So anyways, you can see we have
now these five sub aents running and I
will check in with you guys when that is
finished up. Okay, so the verdict here
is to reshape and the confidence in that
is very high. So in one line it says
kill the $9 YouTube to LinkedIn posts
product exactly as described. It's a
free no login commodity wrapped in a
subscription that's structurally built
to churn. But keep the engine and aim it
at a narrow paying niche with the two
features that are the actual moat which
is provable voice matching and direct
scheduled posting. So here you can see
it goes into the why. It goes into our
biggest risk which is no moat and a free
substitute and no distribution with no
audience and a few hundred budget. CAC,
which is customer acquisition cost, will
exceed a $9 LTV, lifetime value, on day
one, and you'd ship a polished MVP,
minimal viable product to singledigit
signups. It goes over the biggest upside
if we do want to, you know, look glass
half full, the money read, the cheapest
48 hour test. So, what it recommends we
do before we go write any code, which
would be pick one niche, DM or email 20
to 30 of them, and see if there's
actually a market there. See if people
would pay for that. So, here's the
overall score. The Contrarian gave us a
2 out of 10. Expansionist gave us an 8
out of 10. We got a three out of 10, a 2
out of 10, and a 2 out of 10. So,
obviously, we would want to reshape this
idea. Now, let's just go over real quick
to the basic claude and see what we got.
Looks like there's a few questions I
have to answer. So, let me do that real
quick. Actually, I have to run this
again because it actually used the roast
skill without me asking it, which proves
that it's, you know, that that's good,
right? But, let me just run this again
and explicitly say don't use the roast
skill. And now, this one has come back.
It did give us a good analysis and said
like, you know, this probably is
something that you want to rework a
little bit before you actually go ship
it. But this advice is so much more
generic and we didn't get the right
perspectives and it doesn't even really
tell us what we should do in order to
actually push this out the door. And
because we just got Opus 4.8 and the
models are going to get better and
better. The whole sick of fancy thing is
something that all of these model
providers are aware of and you know
taking steps to make sure that it's not
just a yes man. But clearly if you
compare these two outputs, getting sort
of a council that has different areas of
expertise and different personas is
going to be much better to actually help
you analyze business decisions and look
at what you should be doing in order to
make money. So that is how the roast
skill works. Even if you don't want to
use that exact skill, I think the
methodology of having your ideas always
be stress tested, always have a devil
advocate, look at it from different
perspectives is the best way to make a
good decision. even if it's not
explicitly about making money, it's a
really good way and a really great way
to just default when you're talking to
Claude or any AI model for that matter.
All right, so that was roast. Now, once
Claude actually builds something for
you, there's one step that it almost
always skips, and it's the one that can
cost you days to fix. So, Claude will
hand you something that looks finished,
but something being finished and
something actually working are not the
same thing at all. And this is once
again a real measured problem. There was
a study out of NYU where researchers
reviewed around 1,600 programs generated
by GitHub Copilot. Well, we all know
that Copilot isn't the best, but
anyways, roughly 40% of them had
security vulnerabilities in them. And
the scary part about these mistakes is
that they're super easy to miss. So, a
lot of the time you don't even know they
exist until something crashes in front
of a client or in some sort of like
worst case scenario for something to
crash like a live demo. I remember one
specific time where we were shooting off
a bunch of emails to people who wanted
to work with us, but we basically didn't
have capacity. So, we were shooting off
emails to let them know. And we had
hundreds of people to reach out to. And
so, the agent that I was building told
me that it had sent out all those
outreach messages. And I didn't know
until 4 days later that, you know, I
checked the email and saw that it only
sent about the first 25% of them. So,
I'm not exactly sure why because it
confidently told me, yeah, I sent off
all those emails. Everything is good to
go. So, not only did it not do what it
was supposed to, but it also lied about
it. And so in that situation, it wasn't
really a huge deal, obviously, because
that wasn't like a super high-risk
situation where it costed us a ton of
money. But imagine what it would have
looked like if it was legitimately
building a bunch of dark code, meaning
you know, code that you didn't write and
it's shipping features or building out
automations, that's a pretty legit like
big deal, which if it lies about it or
does it poorly, that really could result
in your business losing a ton of money.
The fix here is to make Cloud check its
own work before it ever hands it to you
and then also having it check the work
that it already handed to you. So, think
about like how cars get built at the
factory. They test out every single
piece of the car on its own. And then
when the whole thing comes together,
they test it a bunch again. And that's
basically the methodology that we want
to work with when we're using Claude.
This one's a little different from the
others because it's not really like a
pre-built skill that I can give you.
Like I said, it's more of a methodology.
It's more of a mindset shift. And
there's two parts to it. Like I said,
the first part is verification. Before
Claude ever hands something to you, you
want it to check the work as it goes.
And then, of course, by the time it
tells you it's done, you stress test it
more. and you try to find those edge
cases that you collectively didn't think
about both you and Claude were planning.
Now, how you actually do that like
stress testing or the verification is a
little bit different depending on what
you're actually building because if
you're trying to verify a landing page,
that's totally different than verifying
like an edited video or a data pipeline
or something like that. So, so this
isn't just like one magic button you can
press. Like I said, it's more of a habit
that you bake into Claude and more of
the way that you prompt and the way that
you think about working with Claude
code. So, let me show you guys what this
actually looks like. I'm going to have
Claude build out a landing page with a
weightless form for our app or our
product. And then it's going to verify
it with screenshots and it's going to
look at this page as if a real person
was actually looking at it. And then
we're going to have it stress test it by
clicking through the buttons, submitting
a bunch of forms, and trying to break it
and see if there's anything that we need
to fix. Okay. So now its recommendation
for us to verify if this is going to
work was to DM some people and get the
proof of concept, right? And so what we
want to do is have a landing page to
actually send them to somewhere that
shows the features and the brand and
gives it a feel and then also has a
little bit of a wait list to see if
people actually opt in. So I have this
prompt here. I'm not going to read the
entire thing and I will kind of slowly
scroll through it if you want to pause
and look at what I've written up here.
But the idea is that we have a
verification loop. So right here, right
after you build it, do not trust that it
looks right. Verify yourself with
Playright and I need to add CLI here
before reporting back. So start the
local server, use Playright CLI, which
is just basically computer use. So it
can open up the actual website, look
around, take screenshots, click around,
things like that. And it needs to verify
it. So screenshot each section
individually, look at them, and if you
need to, you'll come back and iterate,
right? So the whole point is you repeat
the loop and you iterate, and you only
stop once every section has been
screenshotted at both viewports, and
there are no visible errors and the
weightless form looks clean. And I gave
it down here a definition of done. So
what I'm going to do is copy this prompt
and just put it right in there and hit
go. Now, obviously, like I said earlier,
depending on your actual whatever you're
building right here, your verification
loop will look a little bit different.
In this case, it's able to look
visually, take screenshots, things like
that. But the whole idea is a lot of
times on the first shot, you might hear
this thing called like one shot prompt.
On the first shot, AI will maybe get
you, let's just say 65% of the way
there, and your job then is to review
and to judge and add your taste and go
back and forth. But what if you could
have AI get you 90% of the way there
first and then you iterate from there?
And the whole idea of verification and
checking its work on the way is where
you can have it be a little bit less
lazy and it doesn't actually stop until
it gives you something that you can
basically quickly review and shoot off
because it's a complete waste of time if
it gives you something and then you have
to make all these changes, right? Like
think about it. If you wanted someone
who reports to you, an actual human, you
would want them to give you a report
that you're able to just review once
over and it all looks good and it's all
real. You wouldn't as much value the
employee who's giving you things to
review and every single time he or she
hands you something, you have to make a
ton of changes. So, as you can see, it
is throwing together this little task
list, and it's going to go through and
run the verification loop and fix until
there are zero errors. So, I will just
check in with you guys when that is
done. Okay, so everything checks out end
to end. Apparently, it's done and
verified, not just asserted. We have a
live URL, which I'll click open in a
sec, but let's see. It said it built a
single page, premium weight list landing
page for Cadence with all eight
sections. The verification loop actually
ran and passed. Playright took
screenshots of all the sections. If I
open up this folder right here that you
can see it made cadence landing, we have
like the actual code that went into the
building out the site. We have the
nodes, but right here we have
screenshots and we can see desktop we
have 11 and on mobile we have also 11
that were taken. So that is really
really nice to see. And just to show you
guys, if I clicked in here, we can see
that it's actually looking at what the
page looks like based on mobile or
desktop view. And that's how it's able I
mean obviously this is pretty AI sloppy.
Like it's very generic. That's not the
point. The point I'm trying to make
right now is the verification loop,
right? Obviously, we could do things
from a design perspective to make this
feel more branded to feel less AI
created. So, anyways, let's take a look
now at the actual site. If I click open
here, we're in the VS Code inapp browser
sort of thing. We can see cadence
features. Click on this button that
zooms us down how it works. Pricing. Let
me just zoom out this a little bit.
There we go. Um, join the weight list
brings us down here to this section. We
have different LinkedIn followers,
annual revenue, stuff like that. And
these buttons down here work as well. So
from a visual perspective, besides the
fact that it is pretty AI generic, it's
good, right? Like everything is in line.
Nothing's out of bounds. All the text is
readable. The sections are clean.
There's not any like bugs or glitches. M
dash. Uh-oh. But anyways, that is
showing us how we can get outputs using
sort of a verification loop. Now, we can
even take this one step further. Part of
having it check its own work is not just
in the build process, but it's also in
stress testing process, right? So
because we have the ability with our
website to test out and making sure that
things are functional, we haven't yet
tested filling out the form. So what I
can say is awesome. So what I want you
to do now is use Playright CLI and open
up a headed browser and show me that you
are submitting forms and do multiple
passes of submitting forms with
different dropown options and you know
different types of emails, different
types of phone numbers. Basically just
to stress test this thing to make sure
that there's no bugs in the form
submission aspect of this site. And so
when I say headed browser, that just
means that I can like watch it rather
than a headless browser would be running
in the background and we wouldn't see it
even though it is actually going on and
working in the background. So here you
can see it just opened up a tab. It just
submitted a form and it's filling out a
bunch of different versions right here.
It's doing it really quick, right? We
saw different dropown options, different
types of emails, different types of
names. And obviously we don't have any
backend configured yet, but that would
be the next step, right? We could
configure a backend and then have it
test it out more. It even I don't know
if you guys saw that it was trying out
putting spaces in weird spots. It was
putting some spaces before the email.
There we go. We just got a bug there
where it wasn't a valid email right
there again. So, we're we're seeing all
these edge cases that humans might
actually get. There's another one.
Right? And so, the idea here is that
it's finding things that you might not
be able to think of or you don't want to
sit here and manually do that, right? So
that is what's really cool about this
because we get the creativity of a model
like Opus and then we get the ability
for Claude code to actually do stuff
like this and now we understand what all
the edge cases are and what users might
do. Anyways, I'm going to go ahead and
just let this keep running. But two
parts of having it check its work on the
build side to save you some time and
then of course on the stress testing
side to also save you some time. Looks
like it found all the edge cases and it
decided that that was good enough for
that first run. Right here you can see
all 22 of its 22 tests passed. So, it's
going to pull the evidence. It's going
to look at those passes and the
rejections and then basically just let
us know what we need to change, if
anything. So, there you go. We can see
we had eight valid submissions and then
we had 14 malformed submissions. But
then it said two honest non-blocking
notes. No duplicate guard. So, the same
email could join twice. And email
validation is intentionally lenient. So,
structure only, not deliverability.
Meaning people could submit a fake
email, but if it fits the structure of
like named doommain.com, it will go
through. So there's not a deliverability
check. So those are two things that if
we wanted to action, we could action
that honestly I wouldn't I didn't think
about right away, you know, in our
initial build. So very very helpful. All
right. So those were the first two
upgrades. Now those work for every
single output clause gives you. But to
make them work, you actually have to get
the output in the first place. And most
of the time, the reason people move slow
has nothing to do with what they're
doing when they work with Claude. It's
that they literally hit a wall. The
conversation starts to fill up. Cloud
gets slower. It gets worse. It starts
to, you know, burn through your usage
limit. and it feels like it just has no
memory. And once again, there's a study
on this. It's basically called context
rot. Researchers tested 18 of the top AI
models out there, including Claude. And
every single one of them starts to
perform worse as the conversation gets
longer. Even if it's really, really
simple tasks, that's where you start to
get just so much degrading in the
performance and, you know,
hallucinations. And the problem is that
drop off starts way before anywhere near
the context window being completely
full. So more is not better. And a
longer conversation literally makes
Claude get dumb. So, think about
Claude's context like a desk. If you
piled up a bunch of paper onto it and
then you needed to find one specific
document, it's going to be way harder to
find. It's going to take you way longer
because there's so much information in
there. And on top of that, if you're not
running the best version of Claude,
meaning like the best, most capable
model, whether that's Opus 4.8 or
whatever it might be, it's going to
design things worse. It's going to build
sloppier code. And it might even get
worse at the reviewing and the
verification and the stress testing. So
those two things that secretly decide
whether you make money with Claude are
managing your context and making sure
you're working with the right model for
the right use case. So the fix here is
handling your context properly. There's
a lot of things that go into that, but
basically just making sure that you're
taking care of that and it's on top of
your mind before it quietly wrecks your
outputs. And there's a couple commands
worth knowing here. So first one is
using /context, which lets you see
exactly what's eating up your context
window. /clear lets you wipe the whole
thing and start fresh. Instead of using
/compact, which like compacts your
conversation and then you can, you know,
keep going, I built my own custom skill
called / session handoff. So before I
ever clear anything, I run session
handoff. It writes me a summary of
everything that matters, what we're
working on, the key files we've produced
or key files that hold information, any
open decisions that I've made, and then
basically exactly where to pick back up.
So, all I have to do is run the session
handoff, copy that message, clear the
context, paste it back in, and now I'm
sitting in a completely clean window,
but I'm basically just picking up
exactly where I was, and it doesn't feel
like I lost anything. Now, let me show
you what types of things you want to
think about when it comes to making sure
you're not hitting that context rot
territory. So, the first thing, and the
reason why I'm using this uh CLI version
right now, what I typically use anyways,
you can see my status line down here.
What I'm looking at is throughout my
sessions, I can see the model I'm using,
what the context window is. I can see
the effort that's being used. I can see
basically a visual indicator of how much
of my context window has been filled up.
So 12% which is about 125,000 tokens out
of our a million token window. I don't
really like to let this really pass like
a quarter million. Whenever this passes
a quarter million, I typically tend to
start a new session. So a couple things
that you want to leverage, right? We
talked about SLcontext. So if I do this,
this is going to actually show me and
visualize what is going on in our
session. So we can see, wow, all of
these MCP servers might be well, these
aren't actually taking tokens. These are
load on demand, but if they were loaded
in, that would be a lot of tokens. We
can see we have free space, we have
skills, memory files, system tools,
system prompts, all of that kind of
stuff. And this also will show us, you
know, how many tokens roughly for each
of those items. And this is good to be
able to clean up your products a little
bit if you want to make sure you're not,
you know, starting off with just a ton
of context already eaten up. It also
right here gives a suggestion. So read
results using 490,000 tokens, 49%. So
you could save about 140,000 tokens
here. But anyways, that is one thing.
You could also do a /compact or cloud
code has its autoco compacts. But
honestly, I don't leverage this very
much. It takes a long time. I I
basically built my own skill, which is
called session handoff, which I will
give you guys for free of course in the
free school community. But when I run my
session handoff skill, I've basically
prompted this thing to give me a summary
of what we've done. Um, you know what?
I'll just wait till this runs and I'll
show you exactly what it gives us. All
right. So, this is the session handoff.
We get where it started, decisions that
are locked, and what shipped, key files,
running state, verification, deferred
and open questions, and then pick up
here. So, now I can just do a /copy,
which grabs everything that Claw just
outputed to us. I do my SL clear. You
can see the context window completely
resets. I paste that in, and now our
project has the exact context that we
were basically working in. It has all
the files. It knows where to look. It
knows what we were doing, and it knows
where to pick up. And it's just super
super helpful to be able to just
constantly do a session handoff and
clear. or even if I wanted to do a
session handoff and then move it over to
like I don't know a different model or
maybe even codeex or something like
that, I'm able to do so super super
easily. And sometimes it'll even do
something like this where it says I've
got the handoff. Let me quickly confirm
the current running state before I
recommend our next move and we keep
working. So that skill is super super
helpful and easy to use. Okay, so this
is now the last upgrade and once you
start using it, you'll produce more
progress in a single day than most
people can produce in a week. So, no
matter how good your prompts are,
there's still one hard limit, and that's
the fact that you can only point Claude
basically one direction at a time
because you are the bottleneck. You are
the decision maker and the reviewer. And
Enthropic's own engineering team
actually tested this directly. They set
up a lead agent coordinating a team of
little sub agents all working in
parallel. And they compared it to a
single agent doing the whole job alone.
The team setup obviously outperformed
the single agent by over 90% on their
internal research evaluation. So real
quick, in case you don't know what a sub
agent is, a sub agent is basically a
separate claude that gets its own task
and its own clean context window. It
works all alone by itself and then it
reports back to that main terminal
session. So instead of one worker doing
everything, you know, one step at a
time, you have a whole team of them
running and they're each working on one
of the pieces at once. So personally, if
I'm doing something like planning out a
YouTube video, I'll maybe have one doing
research on a certain topic and one
doing research on another and one maybe
looking through comments on past videos.
The key here is anything that can happen
in parallel independent of each other, I
will spin up sub aents to do that. And
then when everything gets synthesized
together, I can take that output and
just do whatever I need to do with it.
And then I'm going to add one more thing
on top of that which makes it feel
completely like the future. And that is
a command called /goal. So using goal
that lets you set a finish line, an
actual completion condition, and then
Claude will basically just work turn
after turn for as long as it takes until
you hit that condition. And the cool
part about that is that there's a
separate evaluator. there's a second
model that checks every single turn to
see if, you know, done equals true or
not. So, Claude doesn't get to declare
itself done. A different model has to
look at it with a different persona and
actually grade it and see if it's done.
And that's what's so cool about it
because the whole problem in upgrade one
was that Claude would just agree with
itself or agree with you too often. So,
now you have a different one and it
literally separates the worker from the
judge. So, let me go ahead and give it
one job, set the goal, and run this
live. And this last move is cool because
it basically stacks every single upgrade
from the whole video into this one test
because the idea got validated with the
roast. It verified its own work before
declaring done. And that's the
verification methodology from upgrade 2.
It spins up a whole team of sub aents.
Each one runs in its own clean context
so nobody hits the context rot wall. And
then we use goal to drive the entire
thing home. Okay, so this one is really
really cool because it combines
basically everything that we've talked
about so far. We talked about making
sure that we have the right idea by
having some sort of counsel and playing
devil's advocate. We then talked about
how you can have claude verify and check
its own work. Then we talked about
context and making sure that things are
clean. As you can see, we just set our
session handoff. And now we can loop all
of that back together by using things
like sub aents and/goal to help us work
faster. So if I do / goal right here,
you can see it says set a goal. Keep
working until the condition is met. And
then I'm going to basically just paste
in my prompt. So I'm going to shoot this
off and we'll see what it says. And
you'll notice that there's elements that
we've talked about like I just
mentioned. So we have our product. So
the goal is to build a complete ready to
execute go to market kit for our product
and save it in this project. The product
is obviously our web app. We have our
ICP here. And what's really cool is
inside of the goal, we're able to
leverage sub aents. So use parallel sub
aents, one per deliverable. So there
should be six and they each have their
own context. And they're each going to
produce different files that don't
overwrite each other. So this is what
we're having it create. And yet down
here you can see that I defined when
this thing is done, which is that all
six files exist and none of them are
empty. The market research has six plus
competitors. The personalized drafts has
25 number drafts. Things like this. The
more objective you can be with your
goal, the better that it's actually
going to work because obviously it's
going to keep working until it thinks
that it's done. You also will notice
that in here I said after the sub agents
finished, run a verification pass
yourself. So open each file, confirm
that it meets the bar, fix anything thin
or generic before you declare yourself
done. And so this is just going to run.
And now because I frontloaded all of my
thinking into that prompt and set the
goal, I can just kind of walk away and
do whatever I want until this is done.
So this will be running in the bottom
right. It'll say goal active. It'll tell
us how long the goal has been running
and then when it's done it'll say goal
done. So I'll check in with you guys
when we actually have that finished goal
back. All right, so that just finished
up as you can see and it only took about
8 minutes. So one thing about the goal
is just because it's a goal and just
because it has a loop ability doesn't
mean you have to set goals that are
going to run for hours and hours. I use
goal a lot and most of the time I use
goal. It's runs that take less than, you
know, 20 to 30 minutes because I'm able
to just be super clear about my prompt
and just have more confidence that it's
going to achieve the goal. So 8 minutes
we have our six different files and keep
in mind this spun up six different sub
aents and all of the sub aents were
working on their files independent in
parallel. So that's another reason why
this was able to go pretty fast. But all
of these have been verified. All of
these have been checked and now it would
be on us to be able to look at the
positioning, the market research, the
launch plan, the outreach templates, the
outreach drafts and the content
calendar. And because we've looped
together all of these upgrades and all
of these skills, we're in a really good
spot now to be able to start executing
on this vision. And think about this, in
total, all of these demos probably took
me under an hour. And so if you really
wanted to go, you know, start like spin
up a business like this, you're going to
put more than just an hour in. But think
about if you put in like a week of
focused work with all of these
strategies, ideation, building things
out, and then having this full launch
plan and all of this stuff ready to go.
Where could that take you? And how could
you have just leveraged cloud code to be
able to have done something that
probably would have taken a team of 10
and probably would have taken more time.
So just to show you what's in here, if I
click on the go to market, we can see,
let's just first look at the
positioning. We have our ICP. We have
our segment A, our segment B, our core
offer, our tier ladder. Looks like
pricing got locked at 1939 and 999 per
month. We have upgrade logic. We have
our oneline value prop. And we have our
three sharpest objections with
rebuttals. So I could use chatbt for
that. We have I don't post on LinkedIn
enough to need this. AI posts sound fake
and will hurt my brand. So we have good
rebuttals for all of those. And we could
obviously come through, read all of
this, and put our own personal touch on
it. We've also got our market research.
So, we've got our product, our wedge,
our ICP, competitors, which found looks
like seven of them, and we said it
needed at least seven, I believe. We
have some adjacent ones as well. We've
got a full comparison table of these.
We've got where cadence fits, why $19 is
the right entry price. So, as you can
see, all of our sources are here. This
is very in-depth. We've also got our
launch plan. So, this is a 14-day launch
plan, which we would basically just be
able to follow. We've got our outreach,
and then we would start making our
content based on this calendar. So
anyways, that is how we're able to
leverage sub agents, goals, automations,
other things like that to make sure that
you stop being the bottleneck. You are
very much changing from the builder and
producer to the problem solver, the
decision maker, the reviewer, the judge.
That's how you need to leverage this
type of technology to help you grow your
business, to help you make more money.
So that was the four upgrades. Stop
letting it agree with you so you build
the right thing. Make it check its own
work so you ship stuff that actually
works. Manage your context so Claude
stays sharp. And stop being the
bottleneck. Use sub agents. use /goal so
that stuff can run without you. So now
you can use these upgrades to make more
money using Claude. You can get
everything that I talked about today
inside of my free school community.
There you'll also find hundreds of free
resources and courses and over 400,000
people building with Claude. And if
you're ready to go deeper and build an
AI business, then you can join my plus
community where we hop on weekly calls
to answer your questions. The link for
both of those communities is in the
description. But anyways, that is going
to do it for this one. So if you guys
enjoyed the video or you learned
something new, please give it a like. It
helps me out a ton. And as always, I
appreciate you guys made it to the end
of the video and I'll see you all in the
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