I'm sure I created the best LinkedIn posting system you will ever see.
And trust me, I know what I'm saying.
I spent a few days on building this project because actually it does everything for you from making
a research and picking the most popular topic to actually creating all of the LinkedIn and post parts
such as post description, LinkedIn, title image, prompt to going through actually the process of
generating the high quality image.
Picking the best one and actually auto publishing it on LinkedIn.
So either on your personal account or just your business account.
And I will be showing you everything in this specific video.
And trust me, if you watch this lesson to the end, your skills will skyrocket because we'll be building
this entire project from scratch, from setting the trigger to setting all of the nodes here.
And in general, you will see how to use different nodes, how to combine it together, how we solve
errors actually approximately.
And this automation, I had over 100 errors before building it.
It's highly complex.
And again, watch this video to the end because after you do this, your skills will be much better
and you will be much more experienced.
However, before we get started, let me show you how this automation works.
And actually I will run this automation.
So we will see the post generated with this entire project.
But first things first here we've got the instructions.
So what it does step by step.
The first thing what we do we provide the keyword via our chat.
And there is a disclaimer actually you can have different triggers.
For example right here in the alternative I provided for you other actually beginning of this automation.
So you can use a trigger which is schedule trigger.
You can run this automation every day and publish your content on autopilot.
All right.
So then you've got Keyword Agent that creates for you this specific keyword.
However um let's move to the back end.
So in general, for the original version for this project we provide specific keyword for.
For this case, maybe it can be a agent.
Then what we do we do the keyword research.
So we use HTTP request and we connect to Google.
So we search for the most relevant related and popular keywords right now to the keyword we provided
in the chat.
So simply if I provide some keywords to my chat then what it does it takes from the internet, from
Google search the most and the best related keywords to the specific keywords, and it returns it for
me.
So actually what we do, we take the most popular topics from the internet right now and we create the
post about it.
So it simply works by this way.
And after we run this step, we get our search results and then we need to format them.
So we are using these fields and right away we go ahead to our loop.
So content creation and part.
So in general what we do here um the list of approximately ten keywords, is stored in a variable and
passed into a loop for content generation.
So inside the content generation loop for each keyword.
So we will have approximately ten runs.
We generate LinkedIn post description title for the post tailored image prompt um which is actually
given for us.
All right.
So for this case we've got our free agents that are creating for us the entire content and we save.
Additionally, um, like this information inside Google Sheets, after our content is generated, we
store all of the data inside Google Sheets.
So approximately ten ideas.
And now even you can ask, all right.
But for what do we need ten content ideas.
And actually the reason is very simple because um, by this way we expand our possibility to choose
the best content, all right.
For this specific purpose.
After we've got this loop, we create ten content ideas.
We've got our a agent that chooses for us one keyword, and then we retrieve the data for the specific
keyword.
So this agent is pretty I can say complex.
We've got a think node.
We've got the search web.
So we search for which one actually keyword and which one content which will be the best.
We've got structured output parser.
And then after we choose one keyword from ten of them and we search for the data, because actually
we saved the keyword along with the content inside Google Sheets before.
So we search for this content and we retrieve it and then we generate our image.
And this part is, I can say really interesting because what we do, we generate our image, we convert
it to file, we get image URL.
However what we do inside this specific I can say loop.
And this is actually not the loop however condition if we analyze our image quality.
So we ask all right if this image is high quality, if don't we have truncated text or any, you know,
cut words or so.
So actually this node does everything for us.
And then it returns the response which is yes or no.
If actually the response is yes we generate another image until the image is perfect for our LinkedIn
post.
So we've got this step which is kind of our safety net.
So again until our image is being generated it's totally perfect.
We run actually no.
Again the image creation process.
So all right, after we generate the image it passes through the if node.
Then what we do we just publish the content on LinkedIn.
And at the end we just check the status to publish and we save image URL.
So this is the entire automation.
And now look on these three parts.
This is the content creation.
We've got ten ideas.
Then what we do.
We choose one keyword.
So from ten ideas we choose only the best one.
Um actually I can say idea for the content and then even we create the best image possible.
Because, you know, sometimes we may have some automations about LinkedIn, how to generate posts,
how to do all of this stuff.
However, it's not that complex.
Uh, but with this automation, I need to I need to say I spend a lot of time.
I had over 100 errors and I came with this entire project.
That is perfect.
I'm using it for my very own LinkedIn.
And also I provided for you alternatives for this specific case.
So for this automation I will show everything in this video obviously.
However what you have here you use LinkedIn note.
So you publish the content to your business profile.
And this note actually publishes your content to your personal profile.
Also in this video it's very important I will be showing you how to publish your content to your LinkedIn
profile, which is personal and your business profile.
I know many people struggle with this specific case.
So yeah, again, watch this video to the end.
But obviously the most important fact is that how this automation manages to create our content on LinkedIn.
So this is the post that our automation created.
What we have inside we've got perfect description.
Artificial intelligence is reshaping the way we handle complex tasks.
And agents are at the forefront of this revolution.
We've got here some statistics.
According to Gartner, by 2025, 50% of businesses will have agents incorporated into their operations
to some extent.
So this is the end of 2025.
Remember, these agents are not just performers of routine tasks, but are becoming decision makers,
providing insights that drive business growth.
Even like look on this quality of this image.
So for this case, we are using this specific loop to indicate if image is perfect.
And yet we came with the best image possible for the specific case we've got perfect text not truncated
and quality is great.
And another case actually also we've got the hashtags have another case.
And this is my business profile.
All right.
This is my business profile.
But this is my personal profile.
And also I've got some posts.
So this is another format of the post.
However still we've got our image.
We've got specific facts.
So here efficiency boosts over 30%.
Even yesterday I were testing this automation and then some person actually I don't have a lot of reach
on LinkedIn.
However, some person commented like this post.
So we've got to know this post here.
And the image and there we've got a discussion.
So the numbers you shared are wild, but honestly think they are still lowballing IT.
Companies dragging their feet on a integration are gonna get left behind fast.
So even you know, we've got a lot of impressions.
Um, and this content is highly professional to post on LinkedIn right away, even to show you that
the posts our automation can create are highly valuable.
I've got two examples.
So firstly we've got Gary Vaynerchuk.
And for instance like this image can be generated by ChatGPT image generator.
We've got over 2000 likes here approximately 300 comments.
And this is another another example.
So we've got Ruben Hassett and like look on the structure.
Like our automation created similar structure of the content.
And here also we've got an image generated by AI and over 3000 likes 900 comments 300 reposts.
But all right really long introduction.
Nevertheless our video for this automation will be very long either.
So let's don't waste more time.
And let's start with actually first steps.
So I can go ahead to my Nan and create the workflow.
And here we'll be starting.
And so the original version will have the trigger which is action chat message.
So we pick that trigger and that's perfect.
Insight will provide some keywords.
Also remember in the resources of this material you've got all of the instructions.
Um yeah I just created for you.
So for instance you can copy all of the system messages prompts from our from our agents.
You see how to set up your HTTP requests.
So this is pretty long file.
However, I put a lot of attention, um, to include everything here.
So again you've got all of the instructions and we'll be using it for the case of this material for
this video.
And because we've got a lot of steps.
All right.
As you can see here, we've got a lot of agents a lot of HTTP requests.
So for sure it's really valuable.
Also we can download the JSON file and the resources to remember about it.
But for now let's move on.
Um, after we've got our chat we've got our HTTP request.
So simply, um, if I go ahead to our file we can scroll up.
And it's really unusual HTTP requests, I need to say, because right there we've got only two specific
fields set up.
So method which is get and the URL.
So right now let's just copy this URL.
Um pretty simply let's move on here.
Just paste this URL method which is get and we can execute previous nodes.
So for now I can open the chat and type A agents.
All right.
That's great.
And then what we have after we do this we've got our data however in XML format.
So these are the searches from the internet.
So simply we provided the keyword which is a agent.
And then it searched for the most relevant actually related and popular keywords right now on the internet.
So it works by this way, it's great because we are using main Google search tool.
That's great.
Um, and also we provide here chat input at the end.
So this is us our search result.
And then we need to kind of And put it so this HTML format into JSON.
So I can pick actually HTML node.
This is not HTML however XML.
I'm sorry.
So in general what we do, um, we've got the property name which is data.
We execute the step.
So after we do this there's a simple note.
We don't need to provide anything here.
Just set it up.
And what we get we've got our one item with our all of the keywords.
So let's see how many we've got of them.
It counts from zero.
So we have ten.
That's perfect.
And in general we need to do some specific stuff.
So I want to have ten different items.
It's pretty important.
We need to have ten different items to then put them into our loop loop part.
Okay.
And because we'll be creating for each of them content.
So I can pick the node which is split out so we can turn a list inside items into separate items.
That's great.
And for this specific case, we just drag and drop our object variable.
So top level.
And then what it should do.
Actually no no top level.
However we just pick complete suggestion.
So when we execute this step we should get our suggestion and data.
So we've got a agents and then we've got ten items.
Let me let me show you.
We've got first keyword which is agent.
Next.
We've got agents explained a agents versus agent.
I, I can say it's highly popular.
Popular right now.
So agent I.
And we've got even agents course directory crypto Reddit and action.
That's very very cool.
And remember afterwards we'll choose from these ten items one item to create the content for it.
So if it gives you some specific keyword that you don't think will be great.
Let's say maybe it's agent stocks.
All right.
Don't worry.
Our agent will handle the entire problem later.
That's cool.
Let's move on.
And now what we need to do.
I want to provide the edit field.
So instead of the data.
Because let me show you.
We've got here, um, the data and the variable.
So the name of it I want to actually, you know, name it as keywords.
So provide the name keywords and then just pick our variable which is data and the value.
And now execute this step.
So we've got the items and keywords you know for each of them.
Perfect.
It was pretty fast.
I need to say so we've got our inputs right here.
And the next step will be providing our loop.
So let's move on and provide our loop.
Loop over items.
And batch size is one.
All right.
So it indicates how many items you would like to return with every loop iteration.
Even you can read it here.
Probably the number of items to return with each call.
Um, obviously we have one, and mostly it's one, so it works by this way.
And inside the loop, what we'll have, we'll have our free agents.
Um, the first one will generate for us the post content, the second the title and the third image
prompt image prompt.
And actually the fourth step is saving the data inside Google Sheets.
So let me show you what we do.
We provide our node which is a agent.
After we do this we pick define below.
So source for prompt.
Uh so we provide a prompt um inside our file.
Let's see what we have.
So let me guide you through uh maybe firstly let's provide the prompt.
So we've got the post keyword uh and then um even actually we can set it up.
Um, let's execute the previous nodes so we can execute this node.
All right.
To, to provide the data.
So we've got in the prompt post keyword, we choose here the keyword from loop.
So simply I dragged and dropped this specific um the specific variable.
So with every loop iteration we take other keyword and we feed it to our agent.
And this agent is to create LinkedIn uh post description.
That's perfect.
Um, so the prompt is pretty simple.
I like to every time like structure it by by by this way.
So provide in the prompt all of my variables and in the system message like background, you know,
instructions for the model to behave in a particular way.
So we can add system message.
And here um, I will pick the system message and we'll analyze it in a while.
Okay.
Um, let me go.
Here.
Pick expression.
Um, now open it in full tab and see what we have.
Create high impact LinkedIn posts that are professional, insightful, and designed to grow our LinkedIn
presence.
Each post is built around a specific keyword provided by the user.
When you receive a keyword, follow this process.
Read the keyword as the core focus of the post built around it using relevant facts, statistics, case
studies, or market trends.
Make sure the post delivers clear value to a professional audience.
Write the post.
So also I like to highlight the sections of the system message.
By this way.
So providing these two signs and again just providing these two signs.
So inside how it works we've got different points.
So the first one is research contextualize.
We read it already.
And the second one is write the post.
So we've got bagging with a strong hook to grab attention immediately.
Maintain a confident professional tone throughout.
Use short paragraphs and logical flow to ensure readability.
Deliver education or actionable insights.
Teach.
Inspire or provoke.
Provoke thoughts.
So also you need to remember.
Actually, I customized these instructions based on the best LinkedIn posts out there from other people.
Actually, um, you know, I based on the best content on the proven strategies, how the content should
be structured.
So also this instructor instructions contain that.
That's highly great because, you know, not even you've got the automation.
However the entire strategy, how the posts, um should look like.
So inside I included everything inside this specific part.
So write the post for for example, you've got also use emojis only if they enhance clarity or tone.
Add a few smart keyword specific hashtags to improve reach and visibility, even with hashtags.
As you could have noticed in our LinkedIn post, I showed you previously output roles.
So for the output roles only output the final LinkedIn post.
Do not explain the post or describe the steps taken.
Avoid quotation marks unless part of a statement.
Keep formatting natural and LinkedIn appropriate.
No markdowns or styling notes.
Don't provide line breaks or new lines, just give plain text.
Don't use the specific sign and this sentence is really crucial.
I had a lot of errors previously without the sentence, without just providing it.
So what it does and for what reason this is if we go ahead to our automation that we'll be creating
step by step, we've got here HTTP request.
And the catch is inside our JSON code here.
When we have our text we need to have something I need to say without any, you know, like specific
signs or even like let's say line breaks right here.
So it's compatible with JSON format.
So for this purpose, that's why we've got this specific sentence.
However, I need to say that you've got other way of connecting LinkedIn.
So here in general you don't need to like be very restrictive.
You can provide the content format that let's say you want to provide the breaks.
I don't know, maybe some numbers like from new lines and so on.
So you've got two methods how you do this.
However, I wanted to explain you why we've got this specific sentence.
So by this way again we've got the plain text without any specific parts.
However, this text is highly professional.
Again, still, it's very, very good.
Also remember you've got the alternative.
So you can I will show you later how you can switch for example this part with some parts inside and
just use the LinkedIn note inside.
However let's let's move on to the video.
So yeah it works by this way.
This is the instruction for the first agent.
We've got also sample use case.
So let's say we provide the keyword and then, um, like the instructions.
So you create a polished LinkedIn post with a sharp hook and a lot of more.
Perfect.
After we run it, um, obviously, first we need to pick the chat model.
This is OpenAI.
OpenAI, GPT 4.1 mini is enough really enough?
So what do we do next?
Uh, we run this agent and we see what happens, actually.
So we run this agent and we've got a.
Agents are transforming how businesses operate, offering unprecedented scalability and efficiency.
And now we need to check for for the format.
So that's perfect.
We've got plain text with hashtags which are which are totally fine.
So it's pretty cool.
All right.
The next agent we'll have will be our, um.
I'm sorry, will be our agent for generating the title.
And now we can do the following trick.
So let's click on this agent and click control.
Click on this note and now click Ctrl D and again Ctrl D.
What do we do by this?
We just um yeah we just connect our different agents.
Um, actually we duplicate them and we connect them to our loop.
So let's do it by this way.
That's perfect.
And for this agent will be generating the titles.
If we go back to our instructions.
So the file you can download in the resources of this material, you will see we've got the prompt which
is only we provide here the description.
So we can actually copy it and then just paste it here.
Let's see what we have.
So it doesn't work because probably we've got a different name of the previous agent.
So this is LinkedIn create LinkedIn post description.
And let's see what we have even for for that I can just, you know copy it create LinkedIn post description.
Let me see what is happening.
So even actually what we can do.
We can provide the output from this specific agent and see all right we've got JSON output.
However for now what we can do we can click just delete it.
And now for instance we can execute the steps to see if that works.
So now for an example to solve the problem what we can do.
We can take this output and see what's different.
Okay so I know we've got only JSON output.
So we don't have like this specific you know variable right here.
So it's pretty enough.
We've got our here JSON output.
So we fit our LinkedIn title agent with our description we created.
That's perfect.
So also it matches the description it creates for it the title.
But further instructions.
Let me show you what we have.
Instructions.
Instructions are pretty simple.
Um, let me paste it here actually.
Yeah.
Let me paste it here.
So your job is to create a clear, highly engaging title for the LinkedIn post based on on its description.
Don't use word title at the beginning, just provide plain title at one emoji at the end of the title.
That's great.
That's enough.
We use only GPT 4.1 mini model, so the same model and we do the following stuff.
Remember, every time you run some steps inside loop, you need to run the entire loop itself.
So let's say if we'll be creating our image, our agent to create image prompt also will be need needed
to run the two previous agents.
Uh, it's pretty annoying in terms of just, you know, testing the automation and doing all the stuff.
However, later I will have for you specific solution because imagine if you have ten items to create
in the loop.
Um, and then you've got other notes after the loop, you need to wait for ten items to be created.
You spend credits and so on.
Actually, the credits, the credits spent on this specific loop won't be high.
We are using mini model.
We are generating only the text so it won't be high.
However, I will show you, um, actually you know other way how you can, um, make the process faster.
But for now, what we can do, let's go ahead and rename it as create LinkedIn Post title.
Great.
Then, uh, we just create our image prompt, create image prompt a agent a agent.
That's great.
Now let's move on to our file and see what we have here.
Again let's pick our prompt very simply.
Um, and this is the message I will explain everything.
So we've got our free agents for each.
We've got different, um, obviously different instructions.
So again we've got the purpose.
We've got goals, visual criteria, output requirements, design style, style elements.
And here prompt format example.
So let me preview it.
You are an AI agent designed to convert LinkedIn posts into visuals, prompts, descriptions.
These prompts guide the creation of compelling marketing graphics that visually enhance the written
message and align with professional branding standards.
Goals.
Analyze the content of the LinkedIn post and identify the core message, insight, or idea.
Craft a visual prompt that represents the message in a striking and brand consistent way.
Ensure the prompt is ideal for generating a professional marketing style graphic, not a literal or
hyper realistic image.
So this agent actually makes sure our image is the best.
I put a lot of attention into preparing all of these prompts, like system messages for example.
For this one.
Also, I had to put a lot of effort.
So what we have your graphic prompt must visually support or represent the key idea for the post.
Be suitable for a modern LinkedIn post.
Clean.
Professional.
Polished.
Reflect a current design aesthetic and we've got output requirements.
So what we should receive for us or even design style elements.
There is the point.
Remember it's very important you can change these A agents.
So after you generate this entire automation.
Firstly obviously like create it or even like you know I recommend you to watch this video to the end
because again after you watch it you will see like a lot of different steps, you will be much experienced
in Nadine.
However, if you wish you could just take the JSON template, just, you know, import it here and take
your credentials.
Just connect and see if that works.
And later what I mean you can change the agents to, to just create for you something else.
So let's say you want to have your content for healthcare, maybe for football for some, I don't know,
maybe news for Other specific purposes.
Also, we can provide other instructions.
You can improve them.
So the point is for this automation to run properly.
And then um, yeah, I created everything for you.
You've got the JSON template and then you can customize it for your specific needs.
Hope you appreciate it.
But let's move on to design style elements.
Flat illustrations, bold or minimal typography elements.
Infographic components, modern abstract shapes or gradients.
Metaphors.
Layout suggestions.
So we've got everything here.
After we do this, we execute this step.
We get our image prompt.
So again it runs through the entire, um, loop.
So we have it modern flat style illustration on a sleek a robot interaction with digital holograms of
business charts and innovation icons like light bulbs and gears.
Perfect.
Um, then what we can do, uh, we can open our Google Sheets.
So I would like to open the row in sheet.
And now let me show you what I prepared for you.
So actually we've got our template inside Google Sheets that contains the keyword post title, post
description, post image prompt.
Then we've got the status and image URL.
And for this specific purpose.
So for this Google Sheets we save only a few different, uh, you know, information.
Um, the first one, uh, which is very important is our keyword.
So we take our keyword from edit fields.
So we just fit our Google Sheets with this specific data.
So edit fields and again it changes with every loop iteration.
So it provides you know the relevant keyword for the post title.
Uh we just provide the post title here for post description.
We just do this.
We provide it here.
Image prompt.
Let's move on.
We've got the output.
And for the status we um, actually mark it as not published because if we move on to our Google Sheets
for the status.
We've got two fields not published and published.
And now there is the important part actually for what do we save the data here inside Google Sheets?
There is one main reason because actually we want to store it for each loop iteration.
So every time, uh, like our first let's say first iteration runs.
So this is our first iteration.
It saves the data.
Then we've got second iteration.
It saves the data for the next, you know, like the next content.
So in general we store all of the content inside Google Sheets.
And the next part is actually after we choose specific keyword and specific content we would like to
use for LinkedIn post.
Then from Google Sheets what we do we take the data from here okay.
So firstly we store it.
We store all of the approximately ten different content ideas.
And then after we choose specific keywords and content we want to get, we want to create the post for
it.
We just take the data, all of the data from here.
So it's pretty important.
And yeah, let's move on here.
And now what we can do we can execute this step.
However, for now let's connect our Google Sheets with our loop.
So we do it by this way.
Also let me put it here.
So let's run it.
Let's see how it works.
So for now nice.
We can see it works great.
And in general right now um let's see.
We've got here the schema post title, post description, post image prompt and status not published.
And there is important fact.
Now this loop should run ten times.
So if I open the chart I type a automation.
I need to see if that works.
So what we have actually here make sure the last executed node outputs the content to display here.
Let's see what we have.
Um actually let's run it again.
Agents.
All right, so now it runs and we can see it generates first first content.
All right.
So first keyword.
Let's see.
Um, let's move on.
We've got our agents.
Agents.
Um, explained.
Um, let me even preview actually this specific stuff A agents.
Great.
All right.
So as you can notice right here, it produces for me.
All right.
Yeah I know what happens.
Okay, I didn't see I, I was confused, but now it produces for me all of the keywords along with the
content with the status which is not published.
And now it runs until it generates approximately ten different content ideas.
So our automation works.
We've got post title for each of them.
That's truly amazing.
Um, so here this is the entire description.
We've got agents Reddit crypto directory.
Okay, so it runs, it runs, it runs.
And it's pretty professional actually.
You know, you've got ten content ideas from where from which you can choose.
Um, so that's great.
And then it's cool.
So inside our loop we've got our loop and path and done path.
So after our content is generated.
So we've got our IDs inside, um, Google Sheets, we want to process with the path which is done.
So then what we want to do firstly we want to use aggregate node to actually, you know, create one
item like gather all of the information and one item in one field.
For this case I will be using split out node.
I will drag the suggestion inside here.
Um.
However, firstly I need to change um the aggregate um action type so all item data into a single list.
Now let's change the expression.
And then just provide a suggestion.
So here we've got a suggestion.
And then we should get our result so we can execute the step.
So we've got as you can notice inside one item we have all of the data.
So let's say here we've got the first keyword content for it.
The second keyword content for it, the third keyword content for it, and so on and so on up to ten
different keywords.
So in general, again we've got one item.
So then we can use it for our next step which is edit fields.
We will not take it.
So we just drag and drop.
Let me let me click here.
We just drag and drop this specific field.
So this variable we put it here and then we just specify actually no this is the name.
So we can specify as the content and for the value we put it here.
So what we do, we want to make it a string.
So put all of that data, put all of that data into one string.
Later on we can use our A agent and we can feed this a agent with this specific string.
So the point is actually it will look terrible obviously because we've got one string.
We've got one, you know, like one chunk of the text.
It's looking really messy.
Terrible.
Ever remember for our agent sometimes it doesn't matter.
Like it matters less than for us as humans.
We are very, you know, visual persons like we are visual creatures, not persons, visual creatures.
But again, for our agent, it's totally fine.
Like it will retrieve the most important information from here.
So, uh, when I said that, like, you can, um.
Yeah, you can guess.
The next step will be our agent with this agent will be taking one keyword.
And this is.
This will be the most sophisticated agent from this automation because the task, his task will be pretty
important.
Let's move on here and see what we have.
So this is choose one keyword a agent.
We've got multiple different um, multiple different here extensions.
I will talk about them in a while.
However, for now let's provide a prompt.
So for the prompt what we have we've got the content and we've got the JSON keywords.
I don't know why it's keywords but it should be JSON content.
Okay.
So we just give this entire variable for system message.
Uh we are using this system message.
Let's move on here.
It's quite long I need to say it's quite long, but still.
Um, as I said, this agent will be the most sophisticated from, um, the entire automation.
Let's open the expression and let's see.
We've got a purpose.
So from a list of generated LinkedIn post data, each including a keyword, pose, description, title,
and image prompt.
Select the single most promising topic using real time web insights.
Look how professional like it will be.
Like how powerful?
In general, we'll be using Think Node along with our web search option.
So what it will do it will connect our open AI model.
It will use Think node to decide actually to manage the entire process to be as a manager of this assistant.
And we'll be using our web search extension to search the web for the most relevant topics.
So in short, what this agent will do, it will take all of the keywords, all of the content ideas.
It will decide which one is the best based on the actually best search from the internet and its best
knowledge.
So we've got our purpose.
How it works.
When you receive a long block of input containing multiple LinkedIn post entries, each with keywords
for description, title and image prompt.
Follow this process.
Identify all keywords, so extract all keywords from the provided content.
Each keyword represents a separate post entry.
Evaluate keywords using web search so we have all of the data you know, current relevance, trend potential,
virality and engagement potential, and a business professional context and give priority to topics
gaining traction in the news or social media posts.
Use cases with real stats, success stories, or market relevance keywords that align with LinkedIn's
professional audience.
Select the best topic from all entries.
Select only one keyword that is the most likely to perform well on LinkedIn right now.
Do not generate new content.
Use the existing title, post description and image prompt linked to that keyword and we've got output
format.
So what it returns.
Yeah.
So return a single final output in the following structure.
Let's see.
I think something is wrong here.
Okay.
Return a single final output in the following structure.
All right.
Here we've got the JSON output format.
So for this case I need to delete it.
Okay.
And I also will right now adjust it here.
So you know like errors happen especially if you have that sophisticated automation I had over 100 errors
in this specific project.
Can you even imagine 100 errors like meanwhile building it?
It's huge.
I spend a lot of time.
It was really hard.
I need maybe not that hard, but a lot of time was spent.
But let's go back now to the automation and do not return multiple results.
Explain your choice at formatting or markdown.
Include extra commentary.
Example use case.
So yeah, you can read it and JSON output format.
So only return the following JSON structure.
No extra text.
And we want to get the keyword perfect.
And now there's the part we need to enable the specific output format.
So right away we can add some structured output format.
And here inside the file I provided the output format here.
So this is very simple you know output parser even you can just write it manually.
It's pretty straightforward.
Remember and be careful with the JSON format.
So you need to just specify this JSON format.
Otherwise you may have some problems.
So the safer way will be to just copy it from the document.
I think up next for this agent we add the chat model.
We had the chat model and then we add memory.
So we are using not memory however tool I'm sorry.
We are using the think tool.
And the think tool is really crucial really essential for this specific agent.
Because you know, if we have the agent that operates on different extensions Ventures like web search,
you know, OpenAI, maybe structured output format.
We want to make sure all of that, like we've got the best result and all of that is structured.
It works in the proper way and in the proper format.
So Think Tool allows our agent to think to structure its work.
Um, so that's why I'm using it.
And let me show you what I prepared in the think note.
It's quite interesting.
So I've got a think node and I've got my very own description.
So you're filtering and reasoning assistant.
Review the information gathered below.
Your job is to make sure a agent used web search to the site.
Which keyword and content is the most proper to pick?
Make sure the output format is only one chosen keyword, so it's not that hard.
It's not that complicated.
However, it's it's highly professional.
It's enough.
It's enough for our.
Let me see.
Um, next we've got our search web.
So we've got all of the steps we need to we need to provide.
So overall we'll be using.
Let me show you.
Now let's organize it.
Maybe let's put it by this way.
And even what I can do, let me pick all of my inputs here like sticky notes.
I'm sorry.
Let's let's pick all of the sticky notes.
So you know what is happening inside.
So now I can just paste it here.
And now let me very quickly organize it.
So now it's properly done.
And let me guide you through what we've managed to actually accomplish.
So we've got our inputs part.
So here we provide our keyword and then we format it.
We create a content with our loop and our free agents.
We save it to our Google Sheets.
Then we aggregate the content.
So we put everything together.
We added the fields.
So we change change this specific type of the content into string.
And we put it inside our agent, which is to create our keyword.
So I'm actually not create.
However take.
Um, so what we can do, let's rename it very quickly as choose one keyword and what's now left keyword.
And now what's left is our another tool which is for which is for web search.
For this case I will be using the Tavli, which is actually the application that allows us to do this
stuff.
So I will be using HTTP request to connect to it.
And then um I can click on method which is post, and now provide the URL for the URL we can pick.
We can go ahead to our instructions.
So we are using API.
Com slash search.
Um okay we've got a post search.
And then we need to connect our credentials.
So we pick generic credential type.
We can pick header authentication and now create new credential.
So in general we can name it as.
A web search.
Perfect.
And now we can go ahead to lovely, lovely.com.
And in general you've got your free, you know, free credits inside so you can use it totally for free.
I've never, um, you know like paid for this specific tool.
And it's great because it allows you to search the internet.
So what you need to do, how to connect it, and the name you need to type authorization and exactly
just type it.
This is the required format.
And in the value just go ahead and type BR.
Perfect.
And then just copy your API key.
You can create it here.
Um so copy your API key and now just paste it there.
Perfect.
We can click on save.
And now there is the point.
We need to send the body And click.
Using JSON.
And there is the point.
We need to take the data from here.
So JSON.
Let me show you.
We've got JSON, um, the specific code.
And inside what we have.
Let me show you.
Um, we've got the query topic search depth and chunks per source, max results days and so on.
So just all of the metrics, you can read them if you wish or not.
But this node is now is now almost set up.
However the last case is to type here.
Use this tool to search the web.
Search the web.
Perfect.
Um, great.
And now, with all that being said, what we can do, we need to run this step.
However, there is the point.
Um, to run this step, we need to go through the entire loop.
Um, part, um, looping part.
So creating ten items.
Um, but I've got for you one simple trick to avoid it.
So you go ahead here You pick settings and you click execute once.
Obviously we'll have only one keyword inside our keyword agent, but still.
Um, yeah, we we need to set up the entire automation and then we can turn it off.
So now look, if I want to run this specific step, it needs, it needs to run through the looping part.
But it will return only one item instead of ten.
So it's faster and yeah, it works.
We've got our IT search our internet.
It uses OpenAI think tool.
Even we can preview what is happening inside.
So firstly it used OpenAI.
It gave the input, then it used to search for uh, you know, for the specific content.
And we've got our OpenAI think tool OpenAI again and at the end structured output parser.
And what we get in return, our keyword.
That's perfect.
So obviously right now we've got one keyword.
Remember we don't have like we didn't choose from, let's say ten keywords, but only one.
So that's great.
We want to have only this specific keyword because based on this keyword we'll be searching Google Sheets
for content.
So we have get rows and sheet.
We've got get rows.
And now we pick this specific document this sheet which is this one.
And then what we do um we add the specific filter.
So the keyword and the value should be this, this value from our um agent to just one to choose one
keyword.
When we execute this step, we should get the entire content.
That's great.
So row number two keyword agents post title.
We've got this.
We've got the specific post title.
That's correct.
And I can say our content selection part is already created already set up.
However, the problem is I can see we've got four items and it.
Occurs because inside Google Sheets we have four um keywords for a agents.
Usually it doesn't happen like it.
It's not happening because remember we'll have different keywords like you know a lot of keywords and
so on.
And they will be different.
So there will be just one item.
And but for now again we are just creating this automation.
Um, so for the simplicity for now we can um, actually and we can just maintain it also and we can
return only, only first matching row.
So that will um, let our Google Sheets to pick first specific row with the content.
And again, for the purpose of this automation, it's totally fine after we just set it up, you know,
um, let's see what we have.
And this service is receiving too many requests per minute.
Um, let's wait a while.
Um, and again, let's run it.
So maybe now let's try.
Let's see.
Um.
Let's wait.
Hopefully it works.
Um.
All right, I need to wait a few minutes after running it once again.
It's cool.
We've got one item.
The next part is image generation.
And I really enjoy.
And I really like this part, actually, I like to generate images.
Um, and there I just created for you a really nice workflow that creates the best image for your LinkedIn
post.
If we go ahead to our documentation and many times, um, actually we set we actually set the generate
image node.
So here what we have, we've got our URL, we've got the method which is post.
However it will be a little different.
Usually we use JSON format.
But for this case we are specifying um specific parameters.
So let me show you.
Um let me go through um okay.
Authentication Predefined XML type open API.
Let's type it.
We've got our account and here we send the body hover for this case.
Um we've got parameters.
Okay.
The first one parameter as I can see will be the model.
The second one will be the prompt.
Third, the size and fourth quality.
Let's fulfill it very fast.
Um, so first we've got the model and then we've got, uh, what we have uh, prompt size and quality.
Prompt size and quality.
Perfect for the quality we want to pick high, obviously.
Um, I provided the quality note here, um, because for, um, all right, for further, um, for further
testing, we can change the quality every time so it doesn't take you, um, a lot of, you know, credits
for the quality and high image you pay $0.17 for medium you pay for and for low you pay one.
So let's say if you are testing and because obviously we will, we would need to like run this loop
once again like many times probably.
So if you want to test it, you can just set it as low and then just pay $0.01 and make it faster,
whatever for the size.
Um, here, um, let's pick the size.
Um, perfect for the prompt.
What we have, we've got our post image prompt.
So this one and for the value.
So we've got um, GPT image one probably this is the proper, um GPT image one.
This is the proper value.
Perfect.
Um, let's generate uh, actually here we want to pick the post and let's execute the step.
And even before generate image, let's execute the step.
Um, let's click all right executing the step.
And for sure it works.
It's it takes approximately 40s for high quality image.
So we need to wait just a while until it finishes.
Process is done.
We can click on plus and usually we provide here convert convert to file of course from base64 to string
to file we provide the specific variable.
We get binary file.
Let's view it.
Wow.
That's perfect.
Just look like we don't have any truncated text.
Uh, we don't have any, you know, high quality.
So of course everything is perfect.
Even this specific part of the automation, let me show you, will prevent from any, you know, mistakes.
So let's say the text is truncated or it's bad quality.
It will prevent like every error will have because we've got if note and there our OpenAI checks if
the image is correct.
So uh we've got our file and now somehow we need to provide it.
Like get the URL for this case I'm using external platform, which is cloudinary.
Um, and now let's move on.
Get image URL inside our file.
You've got it.
So URL.
I will show you how to set it up and go ahead to cloudinary cloudinary.com.
Um click Get Started, sign up with Google.
Whatever.
Let's sign up.
Um, let's do it very fast.
And after you do this, um, inside home, you should have.
Oops.
What's that?
Inside home you should have your cloud name.
Just copy it.
Um, and inside this URL provide here your your cloud name.
So you've got your cloud name.
That's great method for now is post.
Obviously we post we connect to the application not we get something from the application.
And for the authentication actually what we do.
Let me see.
Um, all right.
Now we need to specify and we need to send the body.
So we send body um, form data.
Um, probably it was form data.
Let me see.
All right.
For the form data here and then binary file file data.
Form data.
All right.
Preset perfect.
So the first case is our binary file.
What we have here um, we've got the file data.
So this is the file data simply the data field name.
Because the field name of this file is data.
So this is the data parameter.
Um this is approximate actually.
Nah.
Then I forgot about it.
This is upload preset okay.
This is upload preset.
Let's see okay.
Upload preset.
Just let's copy it here and in this value you need to provide your preset name.
If you go ahead to Cloudinary, you click on settings, you click on upload right here.
You just you just create your preset, um, name.
Okay.
You click in the right top corner at upload preset, you name it as Na.
Then let's say LinkedIn, I don't know, um, on signing mode you click on unsigned and you click on
save.
Simply copy the name, go here, paste the name and click on Execute Step.
Click on execute step.
Once again what's happening here.
Convert to file.
Execute step.
Nice.
We should get our URL.
Uh we we have our URL.
If you take this URL you open your page, you paste here.
You should get your image.
Great.
So by this way, we store the image in the specific place because we want to have the URL to use the
open AI node.
And we are using this node to analyze image.
That's truly great.
We can use AI to analyze our image and give us a feedback.
All right.
Is this image high quality?
Like if we don't have translated, do we have the translated truncated text or not.
So that's highly professional.
That's great.
We are using GPT for all image URL.
We are using this image URL that, you know, um, we wanted to get from here.
So we have the URL perfect.
And for the text input uh right here we've got the text input.
So uh, I simply provided the following sentence.
Is the image provided high quality without any truncated text respond yes or no.
Don't provide dot at the end of yes or no response.
Let's execute the step.
Waiting waiting waiting.
Um.
Okay.
Is the image provided high quality without any truncated text?
And the response is yes.
Great.
This is our content.
We would need to have only this specific variable.
Nice.
And all of this like all of all of these, um, these two nodes are for the if node will be providing.
So we check if the value here is equal to yes.
And now based on the big letter or small letter at the beginning we just provide, you know another
condition.
We click on or.
So we just prevent future errors you know because it is equal to like refers to the specific sentence.
And also it matters like if we have big letter or small.
So we can just specify another condition for this case.
Content equal to yes with big letter.
We can execute this step and wait.
Yeah we've got the output.
It works.
So this is the true we go through.
If it's false we go back to generate image.
So now let me pick all of these steps and put it like by this way.
So how it works simply if the image is bad quality we go back to this step.
We generate image.
Then we go through.
We go through, we analyze image.
If still it's bad quality again we go through, we generate image until we get something we want.
Then if it's finally good, we um, yeah, it's the true.
So then we go ahead with publish the content on LinkedIn.
For this case, uh, we pick edit fields.
And now for the original version we are using HTTP request.
But actually you've got the alternative.
So for an example, let me pick up like this entire block here.
Um, I won't be setting it up in this video.
I think it will be too long.
It doesn't make any sense.
I provided it here for you already.
This.
These are the alternatives.
So instead of the HTTP request, you can use LinkedIn node for posting on your business account and
HTTP request for your personal account.
But let's move on to setting up the edit node.
And for what I want to use it, I want to combine title and description together, because we'll be
using JSON format for HTTP request, and it requires to have specific format of the content.
So we are using Edit node to just combine everything together again for the value we pick.
Where do we have it?
Our agent for the title.
Perfect.
Then we can provide dot um space and remember to don't provide here any enter like new line breaks and
so on.
Other like in that case if you provide it like it won't work inside HTTP request.
Uh, we can provide a dot and space and with other node which is LinkedIn node.
Other way of connecting it.
We would um, we can actually, you know, do this stuff.
So provide the breaks like provide numbered list like like do all of the formatting.
It's fine.
But for this HTTP request we are not able to do this.
For now we can pick the description.
So put it here okay.
We need to make sure we don't have the.
All right the break execute step.
Let's wait.
All right.
It runs the entire loop.
So now we can preview if other steps work.
Um however for now we've got here the high quality.
So it will take approximately 40s.
But still here it works like we've got web search.
It takes 40s.
All right, so I need to wait.
As you can notice, it worked.
So we have our content in the JSON format.
That's great.
It's great JSON format actually it's proper JSON format we can use for further step.
And the further step like the next step is HTTP request will be connecting um, our na den to LinkedIn.
I'm using Bluetooth, which is a platform that allows me to do this stuff, and I will show you how
to do this step by step.
Uh, we've used it in other videos.
It's great application.
Um, I can log into potato.
Um, you've got seven day free trial.
The link will be in the resources of this material so you can open it.
And however, if you click on Start Free trial then you go ahead to settings.
In the left bottom corner.
You can connect at once to LinkedIn, Facebook, Instagram, TikTok threads, Pinterest, YouTube,
and so on.
I'm using it for my social media, so it's cool.
Um, this is a great platform.
Form.
Now let me show you how it works.
For the potato, actually, you need to pick this URL.
So simply click here.
You've got the URL for the authentication.
You've got the generic credential type header authentication.
And you have potato.
Where do we have potato.
Um also let me show you how we can connect uh your blood.
Do you provide blood API key.
And then in the value we've got here expression however in the value what you need to do, um, you
go here, you pick your, um, you pick your API key right here, right here in the settings, and you
provide the following sentence.
I actually, to be honest, I don't I don't remember if you need to provide the key before the API key.
So do it by this way.
Or you just can paste like, you know, API key alone.
For me, I think you need to provide the key before.
So even right now, let me test it out.
So we see actually if this step works or not okay so I saved it.
Now we need to send um body parameters using JSON.
And then we've got this specific, um the specific um content.
So what we have inside uh we need to get image URL.
So we provide media URLs.
So this is our.
All right.
This is our note.
Um, even let me undo and set this note name as get image URL rename get image URL.
Great.
Now it should it should be highlighted on the green.
Great.
Um okay.
For account ID I will be showing you how you can do this.
Um, you go ahead to potato.
You just click.
What's that for?
LinkedIn.
Okay.
You just click.
I can click on this on disconnect, delete and log in with LinkedIn.
I can click sign in.
It's really simple.
And then I've got my profile connected I click copy account ID then I can provide it here.
So you did it by this way.
And right away I can name it as upload to LinkedIn.
Perfect.
Let me execute the step and right away I will see.
All right.
What's happening.
Right away I will see if that works.
Okay, so we've got, um, check your credentials for this specific purpose.
Probably I'm thinking about right now.
Um, let's resolve the problem.
Um, on live.
So I can copy API key again.
Now, maybe we need to start the value with the, uh, with the big letter save.
Hopefully now it works.
Please.
No.
Um, let's see if.
Do we need, uh, to provide just API key without anything else?
So copy API key, API key.
Um, click on save.
Execute step.
Yeah it works.
We had to provide API key without any other elements.
Um, great.
Um, if if it's done, we can go ahead and um, click on Publish Posts.
So as you can see we've got LinkedIn.
So we can check right now our LinkedIn.
Let's move on to LinkedIn.
To my actually no this is my business profile.
So let's search for DFA Course Academy.
Um, no, this is my profile.
What I'm saying, what am I what am I saying?
Um.
Nice.
Perfect.
We've got the content.
So, look, are you ready to harness the power of AI agents for business transformation?
This is a title.
Then we've got our content, our keywords, like our, you know, um, hashtags and our image.
Great.
We created actually, you know, we created our automation to publish, um, posts on LinkedIn.
And again, remember, it's highly professional.
We create the best content.
We use a agent to choose the best content from ten different content ideas.
We generate images and we check.
We double check if the image is correct.
We publish content on LinkedIn, and at the end there is the step to save the data and mark it as done
as processed.
Oof, it's quite long automation, quite long video however.
Yeah like it if you set it up once again I will say it.
If you set it up once, you've got it for a long time and you've got proven way, actually proven automation
to generate LinkedIn posts on autopilot.
If you're watching this video right now, big congratulations.
Actually, you you are here at this point of this video.
Wow.
I'm very surprised.
Maybe not surprised, however.
Impressed.
Perfect.
Um, now we can, um, update row and sheet.
We can pick our specific Google Sheets, um, here, sheet one.
And now we want to actually what we want to do.
We want to insert get image url URL of an image and change the status to published.
Let's execute the step.
So we should get our status to published one of these probably.
Um here.
Let's wait.
What happens?
Um, the column to match parameter is required.
Um, the column to match on here.
Okay.
Yeah.
We've got a keyword.
Um, keywords.
Uh.
Let's wait.
Okay.
Execute this step.
What's happening?
All right.
It's generating the entire content once again.
So let's wait and see if that worked.
I see right now that I forgot to provide the keyword to map.
So in general we need to map the specific keyword to match.
And then it updates you know that specific field.
So status material.
So now let's click execute step.
And again we need to wait.
You know it hurts.
It's it's really harsh that we need to wait every time to test some steps after the loop until the loop
is finished.
But still um this is how it is.
Um, this trick is pretty nice if you can, like, execute only at once.
So then you don't need to wait ten times.
In the past, I need to say I used to do this like I used to.
Wait, um, until the loop is is like creates, let's say five elements.
Yeah, but I realized that this option exists, so it's pretty cool.
Now let's wait until the image is generated and let's see if it works and if it updates our Google Sheets.
So node is executed successfully.
Let's move on.
And yeah we've got it.
So the content is published.
We've got image URL.
So by this way we completed the entire automation.
However this is not the end because we've got our alternatives.
And now let me show you how it works right here.
So in general what you can do.
Firstly what I recommend you to do is to change.
Um, if you want to use this LinkedIn note and remember it's for your organization.
So with this LinkedIn note you can post to your organization.
However, with this HTTP request you post to your personal profile.
And now let me show you how you can switch it, how you can, you know, just provide it right there.
And in general, I created here other um system like customized system message.
So it returns barely Different content.
Um, I can just provide a system message here.
Other other things are pretty the same.
Um, for now, what I can do.
Um, here, I just provided the image prompt because, um, let me show you if I execute, um, this
specific, uh, you know, like this specific, um, workflow.
That workflow, however, automation, um, it produces for me LinkedIn posts.
It publishes it on my, um, business profile, but I only provide it, you know, for an example,
create image prompt a agent here, create LinkedIn post post description agent here for the sake of
this automation working.
But now again I will show you how to incorporate it.
So how to incorporate this note inside this automation.
So it works.
So you don't need to use potato.
So the first thing I've done I changed the description.
So I pick this description here.
Just a few I don't know a few seconds ago.
And I provided it here.
So again the form is very different.
The next case.
Oh like it's generating for us the content.
It publishes it on LinkedIn.
Let's check.
So DFA Course Academy.
Um let's move on here.
Um.
Right now.
Perfect.
And now look on this specific content.
It's different.
So we've got our breaks.
We've got, you know, like the title.
Like it's much different.
And just look on this graphic.
Wow.
Look on this graphic.
You can you've got the system.
You can run it on autopilot.
It's published now.
Great.
So maybe better let me show you how to incorporate it.
So now the point is we've got our step for agent and we've got our step here for generate image.
And for the sake of this specific case we need to take these two notes.
So let's copy them face here.
And we need to remove these nodes.
These two nodes.
Um.
Make it by this way.
And now, um, okay.
Connect it for the convert to file.
Um, what we can do, let's actually execute.
Uh, we can execute previous nodes.
All right.
So let's execute previous nodes and let's wait.
Uh, what's wrong actually HTTP request JSON chat input.
All right.
Let's provide chat input a.
Um, okay.
Uh.
What's happening?
Could not find a name named um.
Okay.
Let's for now what we can do.
Actually, let's save it.
Let's refresh the page.
And now, uh, we can actually type agents.
Okay.
It works.
Now it returns one element.
We need to wait.
I could have changed.
Like here.
The quality to low.
Um, it would just, you know, take less credits.
I forgot about it.
But in sake of testing the automations, if you have loop, remember to make sure you, you actually,
you know, change it to low.
So then you don't pay a lot of money.
Uh, but still now I want to, like, you know, wait for this image so I can set here the specific
fields.
Okay.
So really, shortly I had to recreate this actually ending of this automation, um, to publish the
content with LinkedIn note.
And this is from the fact, um, actually we need to have convert to file node before the LinkedIn note.
And also we need to have convert to file node after um HTTP request.
So it's kind of hard to, you know, combine everything together um, to find the ways to connect it.
That's why I chose to pick the simplest way and to remove some steps inside image generation.
So for now, it doesn't check if the text on image is truncated or not, or even its high quality.
However, still with ChatGPT image generation tool, um, you know, like it provides great results.
But, um, I need to mention I will put this tool, uh, I will put these two templates.
So this one with LinkedIn note.
So posting to your personal profile, um, and actually this is the no.
This is the organization profile.
So business profile and this template uh, which is actually posting, um, yeah.
To your personal profile.
So you will be able to download two specific JSON files.
But now even let me show you how I connected my LinkedIn note.
So it's pretty important actually.
We firstly um connect create new credential.
So um you pick standard and you connect my account.
So let me show you.
You should get your login here.
So sign in.
Perfect.
And then what you do you go to LinkedIn and you open your page.
Actually you know your business page.
And then you just pick this specific code.
So this is your ID go back here.
You provided an organization.
You are.
And for the text I picked um create LinkedIn post description.
So this is this variable.
So create LinkedIn post description.
This is this variable for media category I picked image.
So the post contains an image.
And then I've got a data.
And it's pretty important because actually we need to have um this specific note.
So convert to file because we are providing an image okay.
So we are just specifying okay this is binary field data and so on.
So that's how you connect your, uh, LinkedIn profile.
Actually your LinkedIn note to an and again, um, let's test it out.
So let's execute this step.
So see if that works.
So I can execute the entire automation.
And after it's executed and we see that LinkedIn note is working properly.
Um, then I will show you how this entire automation works that we set up in this video.
So as you can notice note executed successfully, let's move on to our profile.
Um, and let's look.
Yeah.
Now agents are transforming the way businesses and businesses operate.
And the future is here.
Perfect image.
Great.
Uh, it works for now.
Um, let's run this automation that uses to post the content, and it checks for the image.
Remember about, um, disabling the execute once option.
Also to change the quality to high.
Make sure you you do.
You do it right.
You can open chat.
And let's say I provide a topic which is um, maybe let's go ahead and I then automations, uh, make
sure you provide you you type it correctly.
Okay.
Now what it does it transform.
It transformed our text and it runs through our loop.
Um, so here, let me see how many items we have.
Ten.
So we should have ten loops.
Iterations.
Now we are waiting.
It saves the data inside our Google Sheets.
Um, let's open it.
So I've got like this Google sheet.
Um.
All right, let's, uh, look here.
A lot of, you know, like data here, so it should be appearing right there.
Let's wait.
Okay.
Nice.
It appears now.
Row 191.
Um.
So let's wait.
Nice.
Great.
Now we are waiting.
Um, five loop iterations.
We are waiting up to ten.
Now it chooses the keyword for us.
So on keyword, it used web search.
That's great.
Perfect.
It gave.
As you can notice, it gave ten items to check.
And we get one item.
Uh, what?
It chose na then automation certification.
Wow.
Um, where do we have it?
This one?
This one row.
It should be highlighted on as published, uh, in a moment.
So let's wait.
Actually, I'm not sure.
Here.
Probably.
Yeah, I provided append row.
It should be update row, update row as we have here.
Um, as we have here, it should be this form so it won't, you know, update this row.
Um, however, um, you know how it looks like, how it works.
And it worked for this specific case.
So we've got the proof.
Now we generate a match.
I'm very curious if the image, like, will be sufficient enough.
Um, because remember, like I like this loop.
I like this loop.
I like this part because it checks.
All right.
The moment of truth.
Um, if image.
Yeah.
Like image is not proper for us.
Wow.
All right.
Even the quality is high.
So, you know, like, that's why I spend a lot of time on this automation I didn't want to provide,
you know, like the notes for getting one idea for, you know, generating image.
Everyone could have done it.
Everyone is doing it by this way.
But we are different.
We are just doing something better.
Um, so this is very comprehensive automation.
Let's wait.
Um, all right.
Here.
Um, yes.
No content?
No.
So it stopped.
Um, but I'm very curious.
What what we had view.
Wow.
Look look look look, guys, look, we've got text, which is just wrong.
This text is wrong.
So that's why we've got.
No, um.
This is the moment of truth.
There are no cuts.
We are, um, maybe this one image.
Let's see.
Nice.
It's processed, but the service was not able to process your request.
Why account?
No.
Wow.
I you know, in this video, I connected every time when we use potato.
Um, I deleted my credential, I deleted my credential, and I again created it in this video.
Um, And so the account ID, I will show you the proof.
Account ID is different.
So it's so it's 3159 and it changes every time we do this stuff.
Um okay I will run the automation once again.
Um, and also change this part.
So what we have we've got update row um select column.
Um okay.
So finally I can see it worked.
Uh, we've got the only small problem with, um, Google Sheets.
However, you know, we need to provide first instead of item, and it should work.
But for now and what we have, let's see what image we have.
View.
Whatever.
Let's look on the post.
Um, we published.
So here let's take a look.
Um, we need to refresh.
Um, Okay.
What's wrong?
I don't know, actually.
Um.
Let's see.
Um, wait.
Let's move on here.
So it should publish the content to our, um, LinkedIn profile, but I can't see the posts.
Um, let's refresh again.
So now I can see that it works.
Finally, we tested our automation.
So again, we've got an items like it, chose the best item for us, created image view automation.
Wow.
Really high quality.
Uh, we've got a small problem here.
So, um, like, the solution is to provide I had here the item.
So the solution, if you have multiple items you provide first and now it should work.
But for now let's check our post.
Um, so let's go ahead to my profile.
Firstly this is my profile name.
Wow.
Great.
Okay, so for sure, it worked perfect.
Um, yeah.
So that's how you connect, um, you create the perfect LinkedIn automation.
It took a lot of time.
Um, I know that, like, this video was quite long.
However.
Thank you.
If you're watching this video right now, thank you for your engagement.
I really appreciate it that you watch these videos to the end.
Um, it means a lot to me.
I'm putting a lot of effort with Damian to create a lot of great automations.
And again, thank you so, so much.
Um, the last thing um, I want to mention is that you can take this specific part and you can put it
here instead of these notes and do it by this way.
So then you create the keyword and you can run this automation every day, um, you know, every hour
or every minute if you wish.
Um, but still the point is, um, you can use different triggers and so on.
Thank you for watching this video.
I hope you enjoyed it and I will see you in the next advanced automation.