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We're going to look at the Open AI
Codeex releases and see if they're a
worthy replacement for cursor and claw
code. And not just that, we're going to
build a microsass idea, a outfit tryon
widget using Google's just released nano
banana, which is super powerful at image
editing.
So, OpenAI are tackling all fronts here
and giving us a lot of options. We now
have an IDE plugin. We have the Codex
CLI and we have Codex cloud. So this is
a really useful ecosystem and something
similar to what cursor have as well. So
what OpenAI are promoting here is a kind
of unified environment. You might work
in the IDE when you're working on human
in the loop code. You might involve the
CLI if you're wanting to work aically
and have it run on a server and you can
have all of them working together or
offload one to the other. This is very
similar to what cursor offers with its
ID, its CLI and its background agents.
So, not unlike cursor or GitHub copilot,
you can now um add codeex to your pull
requests in GitHub. And we have some
updates to the codeex CLI. So, we're
going to take a look at that and see
what's changed as well and how it
compares in relation to claw code. Just
a quick one guys, if you're looking to
become an AI native developer with the
ability to build apps without writing
any code, you should check out the build
with AI switch dimension course. There
is a ton of modules in there and I just
introduced a whole new section on claw
code to help you get up to speed really
quickly. So first up let's look at the
codeex extension. I got cursor open here
but this will work in VS code and any
fork of VS code like windsurf etc. So
just go up to your extensions here and
then we're going to type in codeex and
then you just click on this to install
it and then it's going to appear here on
your left hand side. So once it's open
there you can just drag it over to the
right. This usually where I keep my
agent. So, click on this button to start
a new chat. If we click on this, we get
a task history. So, this extension has a
task list just like you'd have in cloud
code or cursor. And that's also been
added to the Codeex CLI as well. Down
here, we can add context of any files we
want to add in. And we can now add
images as well. You can also add images
to the Codex CLI. Now, and we have this
button here for auto context management.
So, it will decide what recent files it
should pull in. So we have three
different modes. We have chat mode. So
this is kind of like a planning mode
where you just want to talk to the agent
about your code, but it doesn't
necessarily implement anything and it
requires uh approval to run any kind of
command or an edit. Then we have agent
mode. It's able to jump ahead, think
agentively and read your files, make
edits, run GPS, all those different
things like that and run commands in the
workspace. And then we have full access
mode. So it can actually go and search
the internet and edit files outside the
workspace. It seems to be like the
dangerously skip permissions mode in
claw code or the yolo mode in cursor. So
this is where it gets a little bit
confusing. You can set different levels
of reasoning effort. You have minimal,
low, medium, and high. Originally, I
thought these reasoning efforts map to
particular models like the min, mini,
nano, or high models. But it doesn't
seem to be as simple as that. It's
stated in the documentation that it's
the amount of reasoning effort that is
applied. So, it's hard to get an idea of
how these are priced when you're working
with them. And there is a little bit of
work to be done on OpenAI's site to
understand the pricing and the level of
allowance that you get for your $20 or
greater uh per month. It seems to be
they're going to run this very similar
to how Claw Code is running it. You get
a certain allowance over you get a
certain allowance over a certain amount
of time and then it resets. So, also
like cursor, you have a local mode and
then a cloud mode. So in cursor it would
be basically just the local IDE agent or
the cursor CLI and then if you're
sending to cloud it would be the
background agents. So in this case
codeex web is your background agent to
run things in the cloud. So let's see
how it goes. So I'm going to just say
set up an XJS project with Chadcen
styling and lucid icons in this folder.
So straight away I think the UI is nice
here. Nice and clean. We've got this
working going on here explaining exactly
what's going on in the background and we
can expand each one of the thinking
steps. I actually really like this about
GPT5. It explains how it's thinking
through different problems. So if you
actually read through this stuff as it's
being generated, you can actually catch
the model making a mistake before it
does. And it also helps you understand
how it arrives at different decisions.
So we can set the permissions now by
saying run this time or run every time.
So I'm going to let it run every time.
We can see it's running through all the
different tasks here. And if I look at
my task history, I can see that we're
currently on this task at the moment.
So, it's finished working and it
successfully completed those steps in a
reasonable amount of time.
Pragmatically, normally what I do is I
just run those commands myself. I can do
it in a tenth of the time, but I just
wanted to get a feel for how this
interface looks. And it is quite nice
here. And you can go and click on
individual pages to take you through
those. And then we can see the files
that are changed here. And this is
really clean and really nice. I really
like the UI. So, I really like the UI
here. And we have options between
looking at what's changed. Okay, cool.
Okay, so I'm just going to spin up a
server here just to see if all that
works. So, I'm going to say mpm rundev
in the terminal here. Perfect. So,
simple task, but worked fine. So, now we
have a basic project set up locally. We
can also set that up in the cloud via
codeex web. So, let's just connect it
here. So, I've selected my GitHub
organization here and I've put in the
repository. I'm just going to click
that. I'm going to switch on code
reviews. I'm going to allow internet
access just while I'm setting up the
environment. Now, bearing in mind that
you are open to prompt injection when
you have internet access on. So, just be
wary of this. And I'm just going to hit
create environment. So now essentially I
have two different places where I can
run my commands and run my agents. This
is very similar to background agents in
cursor. So for example, I can just click
on the dictate icon here. Let's add a
hero section to our main page on the
next.js app. If I was working on a more
established app, I would pick a
different branch, but we're just going
to work on main here. And then you can
actually click the amount of versions
that you'd like to have developed. So,
we're going to go with two here and
check it out. So, you have two different
options here. Again, you can ask if this
is a request, and then you can actually
just go ahead and hit code. And when I
click on a task, I can see the two
different versions and the different
environments that are spinning up. So,
we can go back and check on that in a
second. Now, if I wanted to interact
with either one of these versions, I can
actually click on this button here and
it will open that for me in cursor. This
is again very similar to how cursor
works with background agents and the
IDE. I feel like OpenAI are taking a lot
of cues from the cursor workflow here.
So once the work is complete, we get
this kind of view. Essentially, we can
see what has been added. We've got the
two different versions and a summary of
the work and I can see what was applied.
So, I can imagine if you're wanting to
check out multiple different design
approaches for a web page, you could
have five different versions and choose
the best one. Or if you're looking to
solve a particularly complex problem,
you could have multiple different
versions applied. You're using a lot of
tokens here when you're doing this. But
I think this is a pretty cool
implementation. So, let's say I'm happy
with version one. I can go up and either
create a git apply, copy a git patch, or
just go and create a PR. And if I go and
hit view PR,
you can see that a pull request has been
created and I could go and accept that
then if I was happy. Okay. So now let's
check out the updates to the codeex CLI.
So the codec cli again being very
similar to cursor CLI and of course
clawed code. Not quite as advanced but
um it seems to be getting there. So just
copy this command npm install global
openAI codeex and then just go back over
to cursor again. Um, we can open up a
new terminal here and then I can just
paste in that command and hit return.
And then to run it, we just type in
codeex. So before we jump into using the
CLI, you might be asking what's the deal
with all these CLI tools? We have a tool
like windsurf or cursor or now this
extension of codeex. It's very visual.
It's in the IDE. Why do we need all
these different CLIs? And essentially
here is my take. So CLIs what they do is
they offer more agentic take on
development. So you can basically take
them you can run them in any IDE in any
terminal. You can run them in docker you
can run them on servers. You can
actually set them up via SDK so that you
can run different commands and have them
work in multiple different instances and
agents. So they're just a whole lot more
flexible. Also some people like working
in a CLI. They tend to be a lot faster
and have lots of advantages in terms of
extensibility. Now, in terms of UI uh
and user interface, they're not quite as
friendly. So, if you're a uh starting
out in development or you're looking to
prototype or build without writing code
and are a little bit overwhelmed by the
terminal, I would recommend using
something like cursor or using codecs or
rue or client built into an IDE as a
starting point. If you've got a
different take on CLIs versus IDEs, love
to see that in the comments. Okay, so in
the codec cli, the first thing you want
to do is hit /init. So what that's going
to do is create an agents.mmd file. So
that's very similar to the claude or
gemini.md. So agents.mmd is a new
standardization for uh agents file
similar to how we would use a readme. It
acts as context for your codebase and
any particular rules and approaches that
you take. And this standardization of
the file name of agents.mmd means now
that we can potentially move between
multiple different types of tools. So
you can see there's multiple different
companies that have signed on, but I
love to see this level of
standardization. What I like about GPT5
is that you do get a train of thought
being shared. And I've actually found
this really useful in development. And
the UI here is actually pretty nice with
some nice icons added in. If we type
slash here, we can see all the different
commands that we have available. So if I
hit model here, I can choose just like I
can in the extension between minimal,
low, medium, and high. Can set our
approval, start a new conversation. We
can compact our context. Now, adding
MCPS is a little bit different from how
you do it in other CLIs with a MCP.json
file. So, I'm just going to show you
that quickly because I found it hard to
find documentation on this myself. Maybe
it'll help you. So, you'll want to find
your settings directory. So, let's
change to your home directory if you're
in a Mac. So, just cd and tilda and
it'll bring you straight back there. So,
we want to change into the
directory.codeex. And then if we just do
ls to see what's in there, you'll see
that we have a config.toml.
So, then you can just open this config.l
pommel by typing nano config.l PL or
just go and find that file in cursor and
open it yourself. And you see you've got
all your config details details here.
And adding an MCP server is as simple as
what I have here. You type in MCP bright
data and then you've got your command,
your arguments, your environment, etc.
Just add them in like that, one on top
of another, and then they should be
listed. Now, you will have to restart
your CLI in order for them to be listed,
but then you should be good to go. So, I
used Codex CLI to build out a project
yesterday, and I have to say it was a
good experience. But that being said, it
is still early and if I was to compare
it side by side by with claw code, claw
code is still miles ahead. It has sub
aents, it has hooks and many more subtle
commands that are really just quite
useful as you work in a CLI. But that
being said, don't forget how fast and
how big of a company OpenAI is and how
fast they move. It'll be very
interesting to see what they bring to
their CLI over the next couple of weeks.
when you consider pricing of Claude to
GPT5 and the fact that their models are
achieving similar parity, I think OpenAI
are really going to give Antropic a run
for their money. So, the first thing I
want to do is create a basic e-commerce
page as the foundation for this
prototype we're developing. So, I have
my prompt here if you want to take a
look, pause it, and then I'm going to
hit run here. But, I'm going to switch
up to high reasoning. So, this is the
output we got from GBT5, and it was
clever enough to go and pull in some
Unsplash images just to populate them.
Now, some of them didn't work out, but
actually this works out pretty well.
This is just a linking issue, I think.
So, good start. So, out of interest, I
set up the same project with the same
configuration and ran exactly the same
prompt in clawed code just to see what
the output would be as a comparison. So,
in terms of output and design, very
similar, very comparable. So, if we take
a look at what we got from Chach GPT
women's collection filtering, quick view
appears over each image with this little
heart as well. And then if you look at
what we got from Claude, we have the
same type of filtering and then we have
a quick view appeared. But I suppose GPT
went that one extra step to bring in the
Unsplash images to populate the design.
So kind of think it's a bit of a draw
here in terms of design. Okay. So next
up, I'm going to add in a slide drawer
because this is where we want to put in
our try out tool. The ability to be able
to see whatever outfit is in there. I'm
going to drop that down to medium, I
think, and just going to run that. So
this is what we got from chatbt. It gave
us the same kind of to-do list and the
output here. And it was really actually
quite quick. So let's take a look at the
changes. So if I jump over here and
click try on now, we get this nice
overlaid slide on from the left. And
looking at the claw version, we have an
overlay here for tryon. And but then it
completely blackens out the background
here. I think I prefer the GPT5 version.
And yeah, this is looks okay. It's not
too bad. And let's close out of that.
Then I'm left stuck here. and I don't
know what to do. So, a little bit of a
fail here from Claude. Okay, so I put in
my prompt here. Let's remove everything
from the slide. You can pause here if
you want to take a look at what's there.
And actually, let's just crank this up
to high reasoning effort. We are using
Claude Opus uh in Claw Code, so it needs
to be a fair comparison. Okay, so both
agents have finished. Let's take a look
at the outputs. So, if we look at the
ChachiPT version, I click try on. And if
we try and click upload here, and then I
click try on outfit.
Perfect. or change photo, I can upload a
different one. Okay, perfect. That makes
sense. So, let's get rid of that one
now. And let's look at Claw's version.
So, if I click try on here. Okay, we can
drag and drop in our image.
Click open and then try on outfit. So,
yeah, pretty comparable. Maybe I like
this one a little bit more. Okay, so the
other really big release this week has
been the release of Nano Banana, aka
Gemini 2.5 flash image. So, this model
was being tested under the name Nano
Banana for a week or so before it was
actually released and announced that it
was coming from Google. So, it's Gemini
2.5 flash image and it's actually
available via API so that we can use it
and it's really great at product
placement, image swapping, all that kind
of stuff. And you can see it's ranking
really high in uh LM arena compared to
other imagebased models. You can get
access via Google AI studio to give it a
whirl or via Gen Gemini or through the
API. So we just need to go and get
ourselves an API key if we want to work
with it in any kind of an app that we
create. So go to get an API key. It
gives you some quick start guides here.
Go create an API key that you're going
to copy. And we need to copy this into
ourv environment variables so that our
app can use it. So over in our project,
we're just going to create a new file
and we're going to call it env.local.
And in there, we're going to paste in
our API key. So the other thing I'm
going to do is copy the API
documentation directly from Gemini. I
could use something like context 7, but
I just find this a whole lot more
reliable. It's up to date and it's
exactly what I want. So a quick Google
search will give me this. And then I'll
add that as part of my prompt. I just
paste that in there. So, if you can see
my prompt now, I'm saying I want to use
the Gemini Flash 2.5 image preview model
to take an output. Use the prompt to
place the output on the person. I've
added an API key as Google Gen AI API
key. So, you see I've added that in here
to env. And the reason I'm telling it
what the key name is here because um the
model can't see into any files for
security. And then I'm just giving it a
link to the uh docs as well. And in this
case, I'm just going to switch it over
to agent full access so it can actually
browse the web in this case. So, okay,
we're in a good spot. And oh, I must
actually save where I am. And I'm going
to do exactly the same thing with uh
Opus.
Okay, so it looks like Claude finished
first. So, let's go and check that out.
So, with Claude, it doesn't do anything
at all. And I don't get any kind of
input from the terminal to say what's
wrong. And the same when we look at uh
codeex, we get a Gemini request failed.
So I'm going to have to add a little bit
of logging to understand what's going
wrong in both cases. When you're getting
these vague uh server errors like a 500
and 502 and there's no further
information, what I would normally do is
just go and ask the model to add some
logging in the terminal so we can see a
little bit more about what's going wrong
and that will help me and help the model
figure out how to correct it. So I'm
adding a little prompt in here and I'm
going to run that. The problem with
claude is it's actually calling the
wrong model. It should be calling the
preview version. And if I look at codeex
version, it's actually calling the
almost right version of the model, but
it's not calling preview. So, I'm just
going to correct that in both cases. So,
two things I had to change in order to
get it working. It seemed like it only
works well with images where the face is
obscured or you're not seeing the face.
Therefore, it doesn't trig any trigger
any kind of warnings with the Gemini
Flash model. And then I also did a
little bit of a cleanup to increase the
sizing of this here. So let's try and
upload our picture here and the model
here. You'll note I had to remove the
face in order for it to work. And boom,
here we have the outfit. Now for the
caveats, this didn't work for me all of
the time. The problem is the Gemini
model is heavily censored and anything
got to do with swapping clothing is
going to fire some kind of censorship. I
had to apply some tricks like making
sure the face was obscured so that it
would work well. And even at that, it
only worked every couple of generations.
It gave me a refusal. But the good news
is that this is now possible via a
model. And I think in the coming weeks,
if not months, we're going to see other
providers launch their own versions.
They might have open weights. They might
be uncensored in a way that will allow
us to do better swaps. So maybe
something to experiment with in terms of
product swapping, uh, thumbnail
generation. Lots of different ideas you
can apply using Nano Banana. This could
be a really great Shopify plugin. You
could resell lots of different potential
here. So, after a couple of builds, how
am I feeling about all these new tools
from OpenAI? Well, I'm actually really
quite impressed. I think OpenAI have
really been cooking here, and they've
played a lot of catch-up. The next thing
is the CLI. Now, the CLI at a starting
point, it's still pretty basic, but it
uh is working quite well for me and has
some nice features. I can see within a
couple of weeks, a couple of months, it
might hit feature parity with the likes
of Claw Code, making it really quite
compelling. Then we have the
introduction of the extension into VS
Code or Cursor or whatever IDE you want
to use, which in my usage, as you've
seen in the video, is actually really
quite good. Um, what I love is it's one
tool across all the spectrum of needs
that I have. An extension, a CLI, and
some web agents. What we're getting from
Enthropic right now is a really great
CLI in the form of claw code and the
great model series in the likes of Opus
and Sonnet 4. However, the pricing is a
big consideration here and OpenAI is
just so much cheaper than Entropic for
developers. And if you're in an
enterprise scenario where you're buying
multiple seats or you're a hobbyist and
you can't afford these big bills like a
200 max plan, OpenAI really is a very
viable option. But in terms of getting
the job done, both seem to have a parity
for just getting there. They do things
in different ways, but in terms of my
speed to reach a conclusion in my tests,
Anthropic and Claw, Antropic and OpenAI
are getting me there pretty much at the
same time. So, I like to be opinionated
on this channel, and if I was only going
to be able to pick one at this point in
time, I would probably go with OpenAI
purely around the pricing and the spread
of offerings that they have. Now, that
being said, I'm sure Anthropic is
thinking about its own extension and its
own cloud agents that it's going to
bring to the market as well. But will
they be able to match pricing? And the
other thing is there are lots of other
different models coming out like I
showed previously that are really quite
powerful like the Quen series, we've got
Groth, we've got so many other models
that are going to show up like DeepC
that are going to be much cheaper to run
than both OpenAI and Anthropic. And
that's where a tool like cursor might
suit you because it offers all of the
things that we just talked about. It has
background agents to run in the cloud.
It's connected to your GitHub so it can
review your pull requests. It has its
full-on IDE that is really
state-of-the-art. And it has its own CLI
which is a little bit buggy but is
improving all the time. But the big
thing here is I can select any different
model that I want. But generally how it
works is you spend your $20 and
depending on what model you're calling,
you get charged at that API's cost. If
you've got $20 of usage and you're
running a lowcost model, it's going to
go a hell of a lot further for you.
That's where it stands at the moment.
It's still really early stage. All these
tools are working in exactly the same
way. Their CLIs work in the same way.
Their agents are working in the same
way. You learn it in one tool and it
seems to apply across all the others in
terms of feature parity. So, I wouldn't
be too worried about picking a wrong
tool at the moment. Just play with them
all or pick one and work with it as much
as you like. And then if something
better comes into the market at a later
stage, the switching cost isn't going to
be that high. We're just having a ton of
fun playing with all of these new tools.
So, I'd love to get your opinions on
what tool you're currently using and
where you're having success in the
comments. It's really helpful to me and
to others who watch the channel. Thanks
and see you next week.
Become an AI Native Developer and Build Apps Without Writing Code - https://switchdimension.com Discover why Codex CLI is the cheaper, maybe better alternative to Claude Code and other AI coding tools. In this video, I show you how to build a cutting-edge e-commerce outfit try-on widget using OpenAI's Codex CLI, GPT-5, and Google's revolutionary Nano Banana image model. Watch as we prototype a micro-SaaS product that you could sell to e-commerce brands or as a Shopify extension - all without writing code! I'll walk you through OpenAI's latest developer tools and demonstrate how easy it is to become an AI-native developer. Perfect for tech professionals who want to leverage AI but feel stuck by technical barriers. Learn how to stay ahead of the curve with these powerful new tools. CHAPTERS 00:00 Intro 01:30 Codex IDE 02:53 GPT-5 Model Power 05:14 Codex Cloud 07:19 Codex CLI 09:03 Agents.md 09:59 MCPs & Codex 11:31 Nano Banana API Build 19:08 Final Thoughts on Codex Suite