FREE n8n AI Ads Generator: Nano Banana + GPT Image 1 = $21k In 1 Week (AI Agency Meta Ads)

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n8n AI Ads Generator - Nano Banana + GPT Image 1 = $21k In 1 Week

Last week I made $21,600 using AI-generated ads. No cold calling, or cold email - just cold prospects who'd never heard of me through paid ads.

In this video, I'll show you the exact n8n workflow I use to generate unlimited ad creatives using Nano Banana and GPT Image 1. You'll see how to create 1,000+ ad variations in minutes with the Airtable base, AI agents for prompt generation, and n8n's new Data Tables feature.

This is the actual system I use in my AI agency to scale—not just a demo. Download the complete workflow and Airtable template free in my Stride AI Academy

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⏳ Timestamps

00:00 - $21.6K with AI Ads (No Cold Outreach)

00:40 - Live Demo

03:08 - Full Tech Stack Overview

04:04 - Get Workflow Free via Stride AI Academy

05:20 - Preview: Airtable + Ad Setup

06:21 - Step-by-Step Ad Generation

08:23 - Add Styles, Memes & Prompts

09:26 - Pick AI Model, Size, Variations

10:26 - Execution Walkthrough

16:34 - Build from Scratch or Use Template

23:27 - Prompt Strategy & AI Agent Breakdown

26:08 - N8N Data Tables Logging

33:17 - Airtable + Google Drive Integration

37:13 - Final Thoughts & What’s Next

39:15 - Join Free or Pro Stride AI Academy

Transcript

Last week, I made $21,600

for my AI agency using AI generated ads.

No cold calling, no cold emails, no

Upwork or any other BS method. This came

from cold prospects who had no clue who

I was or my company. And this didn't

come through my YouTube channel or my

personal brand. And yes, of course,

we're scaling this method aggressively.

Now, as a matter of fact, I or no one

from my team even spoke to any of these

leads to convert them into appointments.

I also had a voice AI agent do so for

me. Now, one of the reasons I was

actually able to do this is because we

are now able to generate unlimited

creatives in seconds. And in this video,

I will show you exactly how you can

generate thousands of creatives in 5 to

10 minutes with a few simple copy and

paste prompts. Now, I'm just going to

walk through a quick demo right here,

and you'll see some other examples as

well in this video later on. Now, I also

want to show you some variations here

for the generations. So, I changed up

the headline here just to join our

stride AAI Academy for free. And you'll

see that pretty much everything else is

the same except I switched out some of

the styles. So, some of these styles are

all unique. And then here, I just wanted

to show you the memes particularly. So,

um, and you know, you could use memes,

you can use different things like you

guys can get creative here, but you can

see like we got the hotline bling meme.

Uh, we got guru's plan. These are just

different creative things that, you

know, the AI will actually take into

account for generating these images,

right? So, mocking Spongebob, uh, Batman

slapping Robin, uh, the two buttons

meme, and then Oprah, you get a car,

right? So, if we go ahead and generate

all these, we'll see what we actually

get. And then for these last two, I'll

change the variations to four and five,

just so you can see that the variation

system we built actually works. We can

see all these executions now are

running. So, looks like everything is

going good so far. All right. And now we

are seeing some of the generations come

through here. So, we can see Gru's Plan

and Mocking Spongebob. This one's in

manga. Uh, this one's in Claymation

style. Here we got Studio Gibby right

here for Oprah Winfrey. You want a car.

We got Dragon Ball Z here for the Batman

slap. So, if I click on some of these,

um, we'll see their different

variations. So, join the Strat AI

Academy for free. Join the Strat Strat

AI Academy for free. We click over here.

We got these ones of Spongebob

with manga.

If I go here to Batman, we can see this

one and the two buttons right here. We

got this one in Rick and Morty style,

right? So, you can see the different

styles here. And it should generate four

like you can see there. We go to this

one, Oprah. We can see that she's doing

the winner car sort of style right here.

Um, so you guys get the point. Uh, this

is pretty damn cool. This allows you to

create so many different variations, so

many different types of creatives for

really any type of ad or really any

image you would like. Now, the stack

that we will be using in today's video

includes Nano Banana, Google's new image

generation model, GPT Image 1 by Open

AAI, and we'll be using this on Kai AI,

which I'll show you in just a second.

We're also, of course, going to be using

N8N for our workflow automation. We're

going to be using GPT and Claude for the

LLMs. And for logging our ad creation,

we're going to be using N8N's new data

table feature. And if you're not

familiar with that, I'll dive into that

briefly in just a second. And this is

what the complete workflow will look

like. I want you guys to know I spent

quite a few days building and refining

this entire system that I actually use

in my business. This isn't just some

template or some copy and paste nadn

template that you may see on other

YouTube videos. I actually currently use

this in my AI agency to make money and

scale my business. And before I dive in,

you can get this entire NAND workflow

for free by joining my new school

community that I'm launching right now,

the Stride AI Academy. Link for this

will be in the description, guys. I am

going to be putting so much value into

this community. We're going to have many

different videos on NADN automations,

Stride AI tools, different CRM setups,

uh, AI coding, a bunch of different

things, but specifically many different

NHN videos because this is one of the

main tools that I use in my AI agency

today. Now, before I dive into the

workflow, once again, this is really how

this system works. AI powered meta

advertising system. So, the AI ads that

we're going to generate here. And then

we utilize instant lead response AI

systems. So AI appointment setters via

SMS, email, chat, WhatsApp. More on that

in future videos. We're really going to

be focusing on the AI ads here. And

these are of course static images just

to show you the proof right here. 21.6K

Canadian right here in my Stripe

account. And this is for a week time

frame right here. And you can see the

cost per leads that we were generating

with some of these different ads, right?

So this can vary. It can go up or down,

but this is just a sense of what we are

getting. So, here's a preview of what

we're going to be setting up in today's

video. So, not only do I give you access

to the N8N workflow, I'm also going to

be giving you access to this Air Table

base. Now, what does this base entail?

Well, this is really where we actually

uh generate these ads and these

creatives. So, you can see here, these

are just some different ad creative

examples that we've been generating.

I've been generating uh really hundreds

of different examples probably like

about five to 600 maybe even a thousand

here because a lot of these are you know

variations of eight or four or whatever

the case may be and I'll click through

some of them to give you a sense and

we're of course going to do some test um

executions in just a moment but the

reason this works so well is because

there is many different dynamic features

which you'll come to see in just a

second and you can really customize this

for really any which way that you want.

Even if you're not going to use this for

running ads, let's say, maybe you just

want AI image generation. Well, you can

use this base as a framework and plug

and play your own different ideas. Um,

which I'll show you in just a second.

All right. So, here I'm actually going

to show you an example of me generating

an ad. So, first things first is we have

our headline, our big claim/hook. So,

this is just very basic. I personally

wouldn't use this, but we have

streamline your business workflows with

AI automation. So, I definitely, like I

said, if you're actually running ads

with this, I would make something much

more of a promise, something more of a

hook that actually gets people

interested, you know, outcome driven.

Next, we have the sub headlines. So,

supporting proof/context.

Very, very basic. I'm just keeping it

generic for this example, guys. So,

definitely get creative here, guys. I

will be making more videos in the future

on actually how to structure your AI

offers. I'll be posting more content

like that as well in the school

community. So, make sure to join. But

we're just saying right now the future

is here. And then for our CTA, we'll

just say learn more. Okay, so this is

the CTA call to action. You can really

customize any of this. Now, you don't

have to fill all this out. You could

actually fill none of it out if you

want. You could just fill out the

prompt. Like all this that I'm showing

you, you don't need to fill out every

single thing. And I'll show you just in

a second. It's dynamic. Um the way we

set it up. Next is a prompt. So if we

want to use this, we can. So I could say

I'm just going to say like make it kind

of comedic. You can really say anything

in the prompt. You could say, you know,

do a comparison, do really whatever.

Right. Next is what I call foundations.

It could be called offers, whatever the

case is. It's linked to this table over

here. And this table is really just, you

know, whatever offer you're running. So,

I have one offer right here. Um, then I

have some offer details. This is like a

short summary of the offer right here.

And then right here, I have a

foundational dock. This is more a little

bit more proprietary. We just have a

long dock right here. Um, and then if I

click generate, it actually just makes a

summarized shortened version of the

offer. But the main thing you would want

to fill out is just the offer details.

So, information about your offer, maybe

who you're targeting, really anything.

It doesn't even have to be too in-depth.

And you, like I said, you don't even

have to use this. It is an option,

though. Next, we have some styles. So,

you can select from different styles

here. So, you'll see here that I have

quite a few. I have like Pixar, um,

Disney, Renaissance, uh, Simpsons, you

know, South Park, you know, Dragon Ball

Z, Future Rama, like a bunch of

different ones. And you can really just

go to your styles tab here and just fill

out a bunch of styles, put some style

prompts. You can get this generated by

Chad GBT. Put that in there and you can

use some of those because what we're

doing here is in our images table. Let's

say I select a style like Pixar. I

select my um foundations offer right

here. And you can see that we have a

lookup record for offer details as well

as style prompt that's being pulled in

right here. And this will be referenced

within our NAD workflow section. Next,

we have a memes tab right here. So, I

could select a specific meme if I want,

like Spider-Man pointing, Drake hotline

bling, distracted boyfriend. You don't

need to do this, like I said, but if you

want to like insert some memes into your

uh AI ads, you can definitely do so. All

right. Next is where we're going to

select our model. So, we can use either

use GPT or nano banana. I'm going to use

GPT for this example here. We can select

our uh sizing right here. So auto 1 3 4

9 to 16 4 to 6 16 to9 you really have

different options. I'm just going to

keep it 1 one. If you don't put anything

here it will default to one one and then

variations. So you could do like one you

could even add like even up to 100. You

would just spend a lot on tokens here

but I have it up to 10. I wouldn't

really suggest any more than that. I

would just if you're going to do more

just do a different generation. U maybe

add some tweaks to it. Trust me on that.

Um, so we could do like in this example,

let's do three. All right. And then all

I have to do is click this check mark

right here and it's going to actually

launch the execution. So we should see

this send any moment now. Okay, perfect.

So we see that the execution is running

right here. Okay, so the execution

succeeded. So basically we just went

down this path right here. So, I'll

break this down more in depth in just a

second, but you can see we have our GPT

um section right here. And then we have

our nano banana section. So, depending

on what it is uh for the model that

we're using, it will go down each

section. Each section is really the

same. You'll see over here, I kind of

noted what these what these different

paths are. So, first path up here is if

there's two reference images included,

and I'll show you that as well. Uh

second path is if there's just one

reference um image included and the

third path which we used is if there's

no reference images included. So you can

see that since we use GPT it went down

the third path for GPT which is no

reference images. Okay. Then it uh went

through the LLM. It used NADN's data

tables right here to log this AI

generation. We had a bit of code here

and then we're looping um which is

contingent on how many variations we

want to create. Since we wanted to

create three, it looped three different

times. Use the Kai API to create the

image. Use the Air Table get record to

get the record, update the record, and

then wait a little bit and then just

loop right through. Now, when each one

of these loops for the AI generations

are done, we're going to get some

executions right here, which depending

on if it's Nano Banana or GPT, it's

going to fall into one of these. These

are our callback URLs right here. And

we're just searching for that Air Table

record and then adding the image to Air

Table via the API, downloading the image

as well, and then uploading it to Google

Drive. All right. So now we can see that

three executions just popped up right

here, which means that they are done. So

we can see this successfully executed.

Now, if we take a look at our Air Table

base, we have three images here that

should be kind of similar to the

distracted boyfriend meme with a Pixar

style. Um, kind of comedic and they are

talking about AI and AI workflows. So,

if we take a look here, we got

streamline your business workflows with

AI automation. The future is here. We

can see the girl, the guy, he's looking

at the AI robot. We take a look at the

next one here. Streamline your business

workflows with AI automation. future is

here once again. Um, following all the

different prompts that we gave it. This

one looked like I messed up here, but

you get the point. You can really

generate many different types of

variations for your images. I'm not

going to go through them all, but you

can see here we got some Dragon Ball Z

ones, some South Park ones, different

ones like Kermit the Frog drinking,

different memes like that. A bunch of

different varieties. Now, you'll also

see here there is a option to add

images. So you can add reference images

into you know your uh generations. So

you could either add one or two. So with

this you could do things like for

example combining two images together. I

could take a picture of me, take a

picture of you know um Hawaii and say

put me in Hawaii for example. So you

could do that or you could just add one

image like you see here. I added my logo

but you can add different things into

your images as well. So, you don't need

to use these, but like I showed you in

the actual workflow right here. If you

use two images, it's going to go through

the first path. If you use one image,

it's going to go through the second

path, and then no images goes through

the last path. So, the second path is

actually this one right here. The first

path downloads both images here, and

then goes on. All right. So, to show you

one more example here, I'm going to

generate a bunch of different ones right

here. They're going to use the same

headline, CTA, sub headline, etc., as

well as the prompt, but we're going to

use different styles. So, here I got

Simpsons, Family Guy, Dragon Ball Z, um,

Studio Gibby here, a bunch of different

ones, Futurama, and we're going to use

some that are GPT as well as some that

are nano banana. We're going to use one

by one for most, and I'll use a 9x6 as

well. And then we're going to do two

variations for each. And I'm going to

generate all these right now. Now,

you'll also see for some of these I'm

using a reference image. So, here I'm

using one reference image for these two.

This one is Executive Stride logo. This

one is just a picture of me. And then

here for this one, I'm using a picture

of me and the Executive Stride logo. So,

I don't know how these will turn out. I

haven't really done too much

experimenting with actual human photos

here, but we'll see how it uh goes right

now. So, we can see all these executions

running right now. Okay. And we can see

that the ads are already starting to be

generated. So, if I click into some of

these, the Nano Banano got done really

quickly. So, these ones go by pretty

quick. And we can see streamline your

business workflows with AI automation.

Future is here. Studio Gibby right here.

And we got another one like so. And if

we go to this one, which is supposed to

be kind of Futurama type style, you can

see we got Bender here. And we got more

right here. So, um, GPT is still

generating. Okay. And now you can see

these GPT ones are being generated. We

got the Simpsons right here. So, if I

click into this, we can see streamline

your business right here. We got the

little Simpsons AI bots right here. Um,

and we even got some we got me me right

here. So, this is my picture. And we can

see that it is this is me when I was

bald. Um, and this is me again. So, not

perfect, but you can do some tweaks

here. We got even more right here with

uh myself as well. Okay. In the house.

And this actually looks I mean not

exactly like me but it could take a lot

of different things from the image and

it even has our executive stride guy

right there. So pretty cool stuff. So I

pretty much just showed you every single

example that you know these different

flows could go through. Now I'm not

going to walk through each and every

single one. I'll mainly walk through you

know some of the main ones. Uh these are

really duplicated so keep that in mind.

But let's start from scratch. Um because

you can either go ahead try to build

this out um but like I said join our

free school community. You'll be able to

get access to this workflow 100% for

free. This is something that I've used

to personally make money thousands of

dollars like I showed you. So yeah. So

what we're actually starting off with is

just a basic web hook trigger. So um

you're going to make a web hook trigger.

And what we're going to do is within our

air table base, you'll see under

automations, what is happening is when

this record is checked. So when we check

that box, it's being triggered. And then

we're running a script, which all you're

going to have to do is just come into

this script and simply just change out

this URL for your production URL of the

web hook trigger in N8N. Okay, so that

is going to send to N8N and begin the

actual workflow. Once that happens,

we're getting the record that we want

simply by just grabbing the record ID

right here. And then we have a switch

node right here, which is basically just

checking to see if we're using a nano

banana model or if we're using a GPT

model or if the uh model doesn't exist.

Because if the model doesn't exist, you

can either put it to nanobanana or GPT.

It's kind of just your default one,

whatever one you want. Right now, I

guess I have it set to nano banana.

Honestly, I would probably switch that

to GPT just because personally I've tend

to get better responses with GPT, but

you can use nano banana or GPT, either

one of course. Right. Next, we have

another switch node. So, this switch

node, first one was just for the model.

This one is checking to see if we have

an image. Right? So it's just looking to

see if this image field exists, right?

That means we are using images or if it

does not exist. So if it exists, that

means that we're either using um you

know, one image or two images, right?

And if it doesn't exist, then we're just

going down here. So this switch node

right here is checking to see if we have

the image two that exists or not. Right?

If image two exists, then we know that

it's a two image generation. All right,

we don't even have to check for image

one because we know that you're going to

be using image one and image two. So

then it goes down this path right here,

which I'll go through in just a second.

If image two doesn't exist, then it's

going to go down to this switch node,

and this switch node is going to check

if image one exists. If image one

exists, then that then we know that we

just want a one image reference

generation, which is this path right

here. And then, of course, if no images

exist, then we're just going to go down

through this path right here. So, I'll

walk you through what image two uh with

two images looks like. So, what we're

doing is if it, you know, image two does

exist and we're going to download image

one, right, with just a simple HTTP get.

And then same thing with image 2. We're

just going to download that. And once we

download that, we're actually using

imageb.com.

So you can just go here, sign up for

free. This is actually just a place

where you can upload a binary right here

to be able to host your image um online

for free. All right. So we're just

calling that API. So simply just go

there, sign up for it. It's very basic

to just get it set up. And then what

we're doing is we're using a merge node

right here to combine these two. And

then what we're doing is we're using a

GPT node right here to analyze image

one. So all I'm saying for the text

input is what is this image? Okay, it's

going to give us, you know, really what

this image is. And then we're using

another GPT um node to analyze the

second image. So what is this image?

This is just going to describe what the

image is for us. Okay. Then what we're

doing is we have a AI agent node right

here. So you'll be able to see that this

is our prompt. Now I'm not going to read

through this whole thing. You can read

through it if you'd like, but it's

basically just saying, you know, you're

an AI image generation prompt expert.

This is what you specialize in. It's

giving some context like always make

sure the text fits on the image and does

not get cut off. This is very important.

It's telling it what its task is. Um

some basic um you know points around

prompt handling. If a prompt exists,

then expand it uh while preserving its

core intent. If no prompt exists, create

an intelligent prompt that combines the

analyzed image in the most effective way

for advertising. And then image analysis

integration. So use the image analysis

data to understand visual elements,

composition, style, and content.

Incorporate relevant details about

lighting, mood, objects, people, text,

and overall aesthetics. Okay, you can

just go through read this. Now, also do

color integration. So, if you do want

specific color schemes, you can actually

add the hex codes right here. So,

there's options for three different

color schemes. So, I didn't add any of

but I've tested this. It works. You can

like add hex codes here if you'd like.

So, if it does mention one, two, or

three colors, then you can use the those

colors as primary, secondary, and accent

colors. But if it doesn't mention them,

then you don't have to use and you can

just, you know, use whatever colors.

Business inter intelligence integration.

So this is referencing that foundational

offer information and how to actually

use this pricing guidelines. So here I

just state like never mention any dollar

amounts or pricings or anything like

that. Output requirements. So some stuff

here quality standards and then response

format. And then we're referencing our

business intelligence info right here.

So the offer details, the prompt, the

image analysis that we got from here,

the image analysis two and one, and then

the different colors if we use any, and

then the style. So this uh image

generation should be within this style.

And then we're mentioning, you know, the

headline, secondary headline, and CTA,

what they are and how to actually use

them. And then we're mentioning if we

reference any memes. Okay, so I know

that's a lot to kind of take in. You

really don't need to fully like re

understand the whole thing. You could

just use this out of box. But if you do

want to go in here, tweak some of the

prompts. You can definitely do so and

optimize it for your use case. The one

thing I will say is depending on each

path, the prompt is a little bit

different. This prompt right here has

everything because it has um the two

image generation analysis information

here. Uh the second prompt that only has

one image analysis, I think it's a

little bit has a little bit less. And

this one here even has less because you

know it doesn't need any images. So

yeah, just if you want to like look at

all three of them, you can kind of see a

comparison, but they're all other than

that it's pretty much the same after

that. Um and as a side note, we're using

for the chat model, you can use

anything. We're using GPT5 mini right

here. So we get good quality at a cheap

price. And then as a backup, we just

have Quad Sonic 3.5. You could really

use anything. I have a calculator here

for no particular reason. Then we have a

code node right here where we're

basically um just returning some dummy

data. So depending on how many

variations we actually set in our air

table base, like if we set 1 2 3 4

whatever um if we set 5 10 you know 15,

it's going to generate 15 dummy datas or

five dummy datas. Right? The reason we

do this is because if we go into Kai

here, like I mentioned at the start,

this is the model that we're uh the the

uh tool that we're using for these AI

image generations. So, it's actually a

very cheap platform. You can also, you

know, switch this out and use other

platforms, too. There's many different

ones. You could use foul.ai. Of course,

you would have to change this workflow,

but for this workflow, we're using Kai

um AI. Um there's many different ones.

You could use the direct API of Google

or OpenAI. But yeah, so access the best

video, image, and music models in one

API. So it's pretty um convenient. You

can see we got like many different ones.

V3, runway, seed dance, and you could

actually improve upon this workflow. And

if you guys do, make sure to let me know

in the school community because I'd love

to see it. Um but we also have all the

different image generation models. Nano,

Banano, Seeddream,

um 40 image, uh Flux, IMI, uh idoggram,

Midjourney, Imageen, Quen, a bunch of

different ones. So, you could add uh

additional ones as well if you want. So,

sign up for here. Um buy some credits.

It's pretty cheap, very cheap, actually.

And then just grab your API key, and

I'll show you where that node is in just

a second. Um but yeah, back to this. So

depending on how many variations it's

going to generate that dummy data

because like I was mentioning in Kai

over here um a lot of these like I think

40 image has a it does give you the

option um for to send specific

variations but it it's only like one two

or four. You can't do three or any other

number above that. So that's why I just

thought it made more sense to just have

a loop right here. So we can really give

it any amount of variations. And then uh

nano banano I believe in Kai doesn't

even allow you to put any variations

through their requests. So that's why

we're using the loop. So we're sending

multiple variations and we're just doing

so by creating the dummy data here and

then looping through however many

variations we put in that air table

base. Okay. So I hope that makes sense.

After that, we're using NAND's new data

tables feature. This is a really cool

feature that NAN just came out with, and

I won't have time to go through a deep

dive of data tables in this video. I'm

definitely going to do more videos on

data tables. And this is a beta feature

that actually was released last Monday

and now it actually just got fully

released onto the um self-hosted

version, right? So, it got released here

literally just 7 hours ago. So, so if

you're not familiar what data tables is,

it's essentially like spreadsheets but a

lot quicker. You don't need to fumble

around to connect your Google account

through authentication or anything like

that. It's directly into NAD. Now, here

you can see this is really just a basic

data table. We're really just logging

every single time we create an ad and

we're just really um putting that same

information that we are, you know, in

entered into Air Table into our data

table. Um, you know, we don't have every

single field. We really just have the

main ones that we inputed. So, you know,

our headline, our sub headline, our uh

prompt, CTA, foundational information,

the model we use, the sty uh size, offer

details, style prompt, record ID for our

air table base, how many variations, the

style, and then the created at and

updated at. You can add different

columns here, strings, numbers,

booleans, date, time, and this is still

in beta, so they definitely are coming

out with a lot more upgrades to this to

make it a little bit more production

ready. So, for the time being in this

workflow, we're really just using it for

logging capabilities. Um, but if you

didn't want to use this for whatever

reason, you could just remove this and

the workflow would work the exact same.

All right, so just so you guys can see

here for the image analysis, I just

resent an execution here. So you can see

the image shows a man standing in a

modern well-lit kitchen. This is the

picture of me with a beard. Gives the uh

analysis right there. Same thing for the

logo right here, executivestride.com.

Just so you can get a sense of what that

would look like. Next after the data

table is we have our loop. So it's just

looping right here with a batch size of

one. All right. So this right here is

where we're calling the Kai API. So, as

you can see, this is going to generate

the image. Um, what we're doing right

here for the nanobano is we're calling

this to create a task. We have our Kai

AI um in the bearer off right here. And

then here in the JSON, what we're doing

is for the model, it is nano banana. And

then for our callback URL, this is where

you would actually want to put that URL

that I'm going to show you in just a

second. The one down below. You're going

to put that here for your call back URL.

The prompt is just right here. You can

see this is the prompt that GPT

generated us and then the image URLs are

right here. So this is where we're

actually referencing the image URLs in

um 2Kai because not only are we just an

analyzing the image and giving that to

GPT for the prompt because we want the

prompt to kind of take in what these

images are about. We're actually able to

just upload the images too of course to

uh Kai. So we're doing one and two since

this is the two image path. And then our

output format that we're doing PNG and

then the size right here is one one. So

of course there are some different um

parameters you may be able to pass. You

can change this to JPEG if you want.

Just check the Kai docs if you want to

do anything different here. But this is

really all you need to do. If everything

goes well you should get a 200 code for

success. And you'll see here that we're

getting a task ID and a record ID. Okay,

the task ID is important. This is

actually what we're going to be using.

So with that, we're actually getting

that same record ID that we got from the

very start. All right, we want to get

that initial record. And what we're

going to do is then in this node, we're

updating that record from our same base

with the task ID. So we're adding the

task ID here. Um, but you'll notice

since we got the record already in this

node, we're we got the record and then

we're adding the old task ID that we may

have had. If it's the first generation,

it won't be anything. But let's say it's

the third generation, then you're going

to have two task IDs from the previous

one. So, we're referencing those and

then we're adding in the new task ID

that we just generated from this

request. All right? So, this is why

we're looping. uh each loop we create

the image and then we're adding the old

task ids. So let's say we generate 10

images, we'll have like nine task ids at

that point and then the new task ID from

that other uh recent image generation.

Okay, then I just have a weight right

here of 5 seconds and then it just loops

through. Okay, the reason why we add all

the task IDs is because since we kind of

like you know made our own variation um

system, right? uh to be able to do like

one 10 gener variations depending on how

many we want. Um when we come to our uh

fallback URL here and we're we're

searching a record, we're going to

search the record based on any record

that contains the specific task ID um

when the generation hits. Okay. So what

I mean by that is each time it loops

through this um trigger down here is

going to be hit every single image,

right? because that's the fallback URL

that we defined in our call request. So

if I grab this data right here, you'll

see so here we're just getting that

information from the web hook. So this

is the image when it's done generating

it's going to trigger the callback URL.

You'll see the completion time, the cost

time, uh created time, the model, the

parameters, so the input output and the

result in JSON format. And then what

this right here, this code snippet is

doing is it's just pulling that URL out

from the uh JSON results. So we got that

URL right here. So if I went ahead and

searched this URL, you can see we got

our image here. This is using the nano

banana one. So like I said, sometimes

it's not as good that I find with Cernet

images. All right. Um but you can see

that definitely does look like me. And

then what we're doing is we're searching

the record right here. So we're using a

filter by formula and we're just finding

that record that contains the task ID.

Okay? So where the task ID contains this

and then we got that record because no

matter if it has 10 task IDs in it, if

it contains this new one that came in,

it's still going to be able to find it.

So that's what we do opposed from just

like updating the task ID and replacing

it with a new one because then it won't

be able to find it won't be able to just

add all the images into that same

record. it would just replace that

image. Okay, what we're doing now in

this code block once we get that record

is we're grabbing all the URLs for that

record um of the images. So if it has

one image, if it has five images, we're

grabbing all those records as you can

see right here. And if I go into JSON,

like it's literally all images and then

URL for the different ones right here.

And then we have an all images string

right here. And this is what we're

actually going to be using. So if I go

over to the update image right here. So

what we're doing in this uh request, the

reason why we have to use the actual API

and we can't just use the air tableable

node is because it doesn't have what we

the specific action that we want here.

Um we actually just want to update an

image and add it onto it. Um so we need

to use a patch method right here. We're

calling the air table API and you'll see

that we got you're going to need to

change these two out the app ID and the

table ID. Way you get this is you go to

air table and you'll see here that we

have an app right here. The app ID is

this right and the table ID is this. Now

make sure when you need to be in the

images table when you get your table ID

because if I switch a different table

you're going to get a different table ID

up here. The app ID is just the whole

base. Um but the table ID changes. Okay.

So, make sure you get the table ID here

and the app ID here. And then you add

those in right here because that's what

we're going to be using to then grab the

record ID from that base. And then what

we're doing is we're passing for the

JSON the AI images right here. And this

is literally um those all images strings

right here that we're passing through.

Okay. After that, we're literally just

downloading that new image and we're

simply just uploading it to our Google

Drive. The reason I upload it to Google

Drive is because Air Table is a nice way

to visualize the new ads, but in terms

of downloading those new ads to be able

to actually use them, it's a lot easier

to bulk download when it's in Google

Drive. So, that's why we do it in Google

Drive. Now, I'm not going to walk

through the fallback URL process for the

GPT. There is a little bit uh it is a

little bit different just because the

data that we get from Kai is actually a

little bit different for the nano banana

versus the GPT. So, some of these code

nodes are a bit different, but it's

basic. It's the exact same process.

Okay, we're doing the exact same thing

here, just a little bit different,

right? But all in all, guys, that is

pretty much it. That is this system.

Now, I know I just walked through in

depth um the process for the two images.

Um just the one image is literally the

exact same except the prompt's a bit

different and we're not we're only doing

one image generation right here. And

then for the no image references, it's

actually the simplest uh path right here

because we do no image uh analysises.

And it's literally just the AI agent

right here, but it's doing the exact

same thing. Okay, so the I know it looks

very complicated, but all you need to

remember is these this path right here

is for two images. Same thing for GPT.

It's the exact same thing. It is a

little bit different just cuz we work

with things a bit differently. Um, in

some of these requests like for example

in GPT, we actually need to upload the

images to Kai's file upload URL right

here. So that's what we're doing here.

Um, so just so you know, we're uploading

those images there. Okay. And we're all

you need to do is just add your Kai API

and it will just autoconnect. And then

we're that when we're actually making

the request for the GPT image, it's a

little bit different as well. So this is

what it looks like. Um, for the prompts

for all them, by the way, we're

basically just replacing any specific

characters that may screw up the JSON

here, just so you know, where you have

the size here, the file URLs, same sort

of thing. We're just referencing it

through the Kai uploaded image area. And

then you're just the same thing. You're

going to need to change out the callback

URL here. The variant is just one

because we're doing the variance through

that loop function that we built um

through N8N. And then we're enabling a

fallback model just in case which for

this is just flux max. So it's pretty

straightforward, pretty much the same

thing as Nano Banana. But yeah guys,

like I said, that's pretty much it for

this system. We use this system to run

ads on Meta for our own AI agency. We

use a variety of other different um you

know, solutions and strategies of course

as well. And I'm going to be making a

lot more videos because like I said,

this is definitely something that helped

us, you know, attain some of these high

weeks and we're scaling very

aggressively. So this I know this for

some this may be a lot, for others this

may be nothing or you know there's every

level there's another devil. So um you

know you always want more and more when

you're scaling your business of course.

So, we're scaling very aggressively and

I'm excited to uh continue scaling this.

And part of the reason why I haven't

been uploading as many YouTube videos

lately is because I've been so focused

on the business side of things. And I

personally want this YouTube channel to

be not just something that's built um

and you know, me making a bunch of money

just from making YouTube content about

how to run your own AI agency when not

actually running a successful AI agency

myself. Um you know, I actually want to

be someone that's actually doing the

same thing that I'm going to be telling

you guys. say if I'm going to make a

video on how to actually make money with

your AI agency that I'm actually running

one myself uh day in and day out. So

yeah, I wanted to make a very practical

video that you guys can take uh get some

value from this, start leveraging it um

immediately to, you know, make some

money. But there are so many other

things that go into actually scaling

your business, scaling a successful AI

agency, being able to deliver really

great results for clients, systematize

your business, hire superstar team

members to be a part of your company,

and a bunch of other things. So, if you

want to see more content surrounding

those sort of things, as well as content

around NAND, new templates, new

practical knowledge, then one, subscribe

to this channel, drop a like to show

your support if you got some value from

this, cuz I did put a lot of time into

this template. And like I said, guys,

make sure to join the Stride AI Academy.

I'll leave a link down below for the

free version of the Stride AI Academy

where you'll be able to get the template

that I showed in this video as well as

the Air Table template right here. Now,

I will be adding a few additional

bonuses in the Stride AI Academy Pro.

So, if you want to join that one, it's

the cheapest it's going to be, but I

will include some additional prompts, my

exact, you know, memes and uh styles

that you can literally just copy and

paste, so you don't have to come up with

your own. And if you do want help

growing your AI agency with a proven

model, you know, you want us to help you

with your offer, how to actually sell,

take it to market, get a bunch of leads

and appointments for your business, book

a call on my calendar. I'll leave a link

down below where you can speak to myself

or someone from my team and and we can

see if it would actually make sense for

us to work together, if we can help you

or not. Other than that guys, that's

pretty much it for this video. I hope

you enjoyed this content. Let me know in

the comments down below what type of

content you want to see in the future.

If you have any questions or thoughts

about this video, let me know as well.

And then if you want to connect with me,

like I said, join our Stride AI Academy,

the free or pro version. And I'm going

to be putting a lot of time and effort

into those communities um right now as

we are just launching them. Once you're

in the community, by the way, you can go

to the Stride AI automation section

right here, and this is where you'll see

the video uploaded. You'll see all the

NAN videos uploaded right here, where

you can get all the different templates

and whatnot. Other than that, guys, I

will see you in the next video. Keep

hustling, keep grinding, and of course,

guys, accelerate your stride. Take care.