Quartz 4

LinkedIn Content Automation mit n8n: Von Viral-News zum fertigen Post

32 min read

LinkedIn Content Automation mit n8n: Von Viral-News zum fertigen Post

Haupterkenntnis: Ein vollständiges n8n-Workflow-System kann täglich automatisch virale News scrapen, daraus LinkedIn-Posts schreiben, KI-Bilder generieren und nach manueller Freigabe direkt auf LinkedIn publizieren – ohne tägliches Zutun.

Creator: Duncan Rogoff | AI Automation
Branche: Marketing
Bereich: Automation

Tags: linkedin, n8n, content-automation, ai-writing, rss-scraping, airtable, dall-e, social-media-automation

Kurzbeschreibung

Schritt-für-Schritt-Tutorial zum Aufbau eines vollautomatischen LinkedIn-Content-Systems in n8n, das RSS-Feeds scrapt, Zusatzrecherche betreibt, Posts im eigenen Stil schreibt, KI-Bilder generiert und nach Freigabe postet.

Langbeschreibung

Duncan Rogoff zeigt, wie man in n8n einen mehrstufigen LinkedIn-Automation-Workflow baut: Ein erster Workflow scrapt RSS-Feeds nach viralen News-Artikeln und speichert Headline + Zusammenfassung in Airtable. Ein zweiter Workflow holt sich täglich freizugebende Artikel, führt KI-gestützte Zusatzrecherche durch, wählt zufällig eines von vier Schreib-Frameworks (z. B. PAS, Story-Insight-Action), kombiniert es mit gespeicherten Brand Guidelines und schreibt den LinkedIn-Post. Optional generiert DALL-E 3 ein stilkonsistentes Bild basierend auf Content-Typ-Klassifizierung. Nach manueller Freigabe in Airtable postet ein dritter Workflow automatisch Text und Bild auf LinkedIn und markiert den Eintrag als „posted”.

Stichpunkte

  • RSS-Feeds scrapen → Artikel in Airtable speichern (Headline, Summary, Link)
  • Manuell in Airtable: Social Channel auswählen + „Image: Yes/No” setzen
  • KI-Agent generiert 4 Suchanfragen für Zusatzrecherche (Statistiken, Quotes, Case Studies)
  • Zufälliges Schreib-Framework (1 von 4) sorgt für Content-Variety
  • Brand Guidelines zentral in Airtable pflegbar – werden automatisch eingezogen
  • LLM klassifiziert Content-Typ → bestimmt Bildstil (Strategic, Human, Conceptual, Symbolic)
  • DALL-E 3 generiert Bild passend zum Stil; alternativ: kein Bild (text-only Post)
  • Airtable-Freigabe („Approved”) triggert Posting-Workflow
  • Post + Bild werden auf LinkedIn publiziert; Eintrag wird als „Posted” mit Datum markiert
  • Bild-URL muss als Array-Format in Airtable gespeichert werden (kein Plain-URL)

Zitate

“This system gives you complete control over everything that goes out.” “Clients love a system like this – this is a system I’ve sold to clients personally for thousands of dollars.” “My goal is always to teach you concepts more than it is about any specific tool.” “Sometimes less is more with LLMs – you don’t want to feed it too much information.”

Action Items

  1. n8n-Workflow aus der School Community herunterladen und per „Import from File” importieren
  2. Airtable-Datenbank einrichten (Felder: Source Headline, Summary, LinkedIn Copy, Post Image, Status, Social Channels, Needs Image, Brand Guidelines Tab)
  3. RSS-Feed-URLs für die eigene Nische in den Scraping-Workflow eintragen
  4. Brand Guidelines + Tone-of-Voice-Dokument in den Airtable-Tab „Brand Guidelines” einfügen (kompakt halten!)
  5. Vier Schreib-Frameworks im Code-Node an die eigene Nische anpassen
  6. Content-Typen + zugehörige Bildstile für DALL-E definieren und im Klassifikations-Prompt hinterlegen
  7. LinkedIn-Account über n8n-OAuth verbinden
  8. Täglichen Check-in: Neue Artikel in Airtable reviewen, Social Channel + Image-Flag setzen, dann Workflow laufen lassen
  9. Nach Content-Generierung: Copy in Airtable reviewen/editieren, Status auf „Approved” setzen
  10. Posting-Workflow auf gewünschten Schedule (1x täglich etc.) einstellen

Full Transcript

Yo yo yo, what up, what up, what up. I’ve got the ultimate LinkedIn automation for you today. It’s basically designed to take viral news stories, do additional research for you, and then write all of your LinkedIn posts using your tone of voice and brand guidelines, and then publish everything to LinkedIn without you having to do anything. It’ll even generate an AI image for you if you decide that’s what you want. This system gives you complete control over everything that goes out, and it’s actually based off of a similar automation I built inside of Make.com, which is one of my best performing YouTube videos, and also one of my best selling digital products. And I think this system inside of NADN is even better. So let me show you how it works. If you want access to this workflow, I will leave all of the resources for you, including all the AI problems inside of my school community. You’ll just come down here, you’re going to this NADN LinkedIn writer. If you scroll all the way down to the bottom, you can just go ahead and you can download the NADN workflow as well as get access to the air table database. If you come back into NADN, all you have to do is create a new workflow, come up to these dots, go to Import from File, navigate to your file, go ahead and click Open, and in just a couple of seconds, this entire workflow will pop in for you to use. This community is growing every day. It’s pretty active. I’m releasing new content and new courses daily, and there’s lots more fun stuff planned that I’m building for you. So I’m just going to go ahead and I’m going to test this workflow just to show you how it works. So the first thing it’s doing is it’s getting our viral news sources. And then it’s heading over here to do additional research for us. So basically we don’t want to just take the news article as it exists. We want to search the internet to see if there’s any more information that we can find to make our LinkedIn posts like that much more compelling, that much more engaging. And so this is doing all the research for us on our behalf. And right now it’s getting our writing framework that I use to basically generate all types of LinkedIn posts. From there, we’re pulling in our brand guidelines. The LinkedIn writer is actually writing the posts based off of a writing prompt that we can completely customize to the platform’s best practices. And then basically here it’s deciding if we want an image created or not. And since we did want an image created, it’s basically just going ahead, identifying the core topic of the article, generating an image for us and then saving this back into our database. And we’re done. And so if we come over into air table here, this is the hub for the entire system. This gives you total control over everything that gets published. Clients love a system like this. This is a system that I’ve sold to clients personally for thousands of dollars. It’s extremely effective. And you can see here that if we have a source headline like, you can now use GPT 4.0 to generate images and we have a little summary. If we come down here, we can see this LinkedIn post that our automation just wrote for us. And then if we come down here, this even actually generated an AI image for us. And so if we’re happy with this and we think this is good to go, we can just come down here and we can set this to approved. If we’re not happy with it, we can go in here. We can make changes to the copy. We can replace the image with an image of our own. It’s really up to you how you want to customize this system. And now if we come back in here, you can see we have this really simple automation. We can set this to around once a day, twice a day, however often that you want. We can just go ahead and we can go test workflow. You can see we’re getting that article generating an image and it’s going over to LinkedIn. And it’s going to create the post with our text and our image on our LinkedIn account. So if I just come in here and open up LinkedIn. And I come over to my personal profile. And I’ll just scroll down here and go into show all posts. You can see now we have the entire post here. And if we scroll this down, we have all the text and the image associated with it. I’m just going to delete this that I needed on my account right now. But that’s essentially how the system works. So let me show you basically how I thought about the system. And then I’m going to go through piece by piece and show you exactly how it’s built. So from a high level, this is how I think about the system. And I find this step is actually really helpful in your planning. It allows you to kind of like troubleshoot before you actually get into building the system yourself. You can kind of see where they might be errors or where you might need some sort of like logic involved. Like if this happens, then this other thing happens. And this really allows you to kind of like get ahead and just kind of like get your thoughts straight before you actually go into build mode. So from a high level, this is really basic. It’s just getting a news article. It’s doing additional research. It’s writing the article. It’s creating the image and then it’s posting it to LinkedIn. And then once that’s done, it’s just storing it back in the database for our reference. Really straightforward flow here. I have a slightly more detailed version of it just to again, like just get a little bit more granular to see what steps I might be missing. So basically, you know, we’re going to run this on a schedule. We want to make sure that we’re able to get our posts like from an air table database. And I’m going to show you how to get violent news articles inside of your database. So you can create your LinkedIn content. We’re going to use AI to research this. We’re going to fetch the brand guidelines. This is a step that I left out here on the left. You see what we can create our own brand guidelines and tone of voice that the system will pull in every single time at right to post. So your posts sound a lot more like you. From there, it generates the LinkedIn posts. This is just the copy. And then it just kind of looks to see if we have a checkbox checks. Do we say yes, this needs an image or no, this doesn’t need an image. If it needs an image, it’s going to generate an image. If it doesn’t, it’s just going to go ahead and update the database with either the posts and the image or just the post content. Then from there, post to LinkedIn. And then just marks the posts as complete. So this is really essentially how this system works. So like I said, this is basically all based around viral news sources or trending news sources, right? And so we’re getting these sources just by scraping RSS feed. I have an entire other video about exactly how this part of the system is built. I will leave it up top for you to click on. If you want to go through step by step to see how this is built. But basically the idea is this is scraping RSS feeds that we set. I have someone here that are basically just, you know, news sources about artificial intelligence and AI. It’s going to scrape all of these sources. It’s going to get all of the articles. It’s going to summarize the articles for us here. And then it’s going to store them back inside of this air table database. Again, with the source headline, the summary, a link to the article, and then this little button here that even kind of like lets us go and look at the article, you know, in its raw form. So I’m just going to go ahead and run this system once so that we have some new articles to work with. And so basically what this is doing is this is getting all of the URLs from the articles that already exist to make sure that we’re not kind of adding the same content in there. It’s basically scraping these RSS feeds, pulling in the articles. It’s getting the actual HTML, which is basically all of the text from the article. And then it’s going to go ahead and it’s going to summarize every single one of the articles for us. And it’s going to store that information back inside of our air table database. And now you can see here, if we go over, we have all of these new articles popping in. So we can just go ahead and we can click into this. And we now we have a headline saying, “quease and thank you “to chat you BTS costing open AI money.” And then it goes ahead and basically this is the little summary that our little AI bot created. And we basically have all of these new articles for us to pick from. And these are like current topics, current events right from the last few days. And again, this is one of the aspects that clients really love because again, this gives no control over what gets posted and what they want to talk about. So you come in here and you can review every single one of these new stories and decide if this is something you want to post about on your LinkedIn. This one’s all about cloud that can now read your Gmail and should you be worried, right? This one’s chat to BTS and image library for easy access to your AI images, et cetera, et cetera, right? And so from here, if you want to write to LinkedIn, I think this please and thank you, post. I’ve actually been seeing this like blow up a lot. This might be something we want to write about, right? And so all you have to do is you just select the social channels you want to write for. And basically in this case, it’s going to be LinkedIn. And the next thing I have to decide is do you want this automation to create an AI images for you? Yes or no? So maybe in this case, we’ll say yes. I do want an AI image created. And let’s find another one. I actually like that cloud can now read your Gmail. Should you be worried? I think this is maybe interesting. Maybe we’ll also create a LinkedIn post for this one. And maybe we won’t have an image here. And these other ones, we can just leave blank if we don’t want the system to create content around it. So this system is basically designed to work for you like daily. This hub right here in Air Table, like you would check in on this daily, you would see new stories that have come into kind of like your database every single morning. You would decide if you want to write content on them. You’d select your social channels. And then you’d walk away from the day. Likewise, once the social content has been written, you’d basically approve that content to be posted or not and make adjustments depending on what you like. So now that we’ve basically selected LinkedIn and whether or not we want an image, we can come down here. And again, we can run this LinkedIn right here. So I’m just going to go ahead and test workflow. And let me walk you through step-by-step. Basically, what’s happening? So let me break down for you exactly how the system is built. So the first thing that’s happening is we’re actually just getting one of those records from Air Table. And what we’re doing is we’re searching for the status waiting for content, which is basically the status that we have set whenever a new news article comes in. We also want to make sure that the LinkedIn checkbox or LinkedIn field is selected for the social channel. So basically, we’ve decided that this is ready for content and that we do want actually a LinkedIn post created for it. Here, I’m looping over every single item. Basically, what this is going to do, is just going to pass through every record one at a time. To the system, I found that this kind of gives chat to BT or your agents a little bit of time to breathe and so that they don’t get stuck, kind of trapped between two different posts or two different stories. It just makes it really clear kind of what it is they’re supposed to be talking about. So the article then gets passed over to this query generated. Basically, what we’re doing, the user prompt, is we’re just giving it the headline and the summary that’s coming from over here. So this is just basically the headline and the summary of the news article. And now we have this little AI agent prompt. And it just says you are a search query generator for a LinkedIn research system. Given a source headline and summary, I’ll put four targeted queries less than 15 words each to gather diverse high quality data. Again, we’re using this agent to generate a prompt to do additional research for us based around the topics of the article. So we want to find recent statistics and data. We want case studies in real world examples. We want expert opinions and quotes and competing content analysis and common questions. And from there, we just have this little researcher. And I was basically using this kind of like HTTP tool. I can switch it out for chat to BT or perplexity. Or you can basically update this agent if you want. My goal is always to cheat you concepts more than it is about like any specific tool or the right way to do things like everybody has different needs, everybody has different preferences. And so my goal is to give you concepts and kind of like structure for how these systems can be built. And then you can kind of take it from there and I can adapt it for yourself. So here, we just have this little research agent prompt. It says your research agent and a multi-agent blog creation system, your task is to gather concise high quality information for a keyword using the provider research and output it in a condensed format. And that’s all of these rules about bullet points, key insights, the implications, statistics, and then some more rules just kind of like for formatting. And here you can see we got this really long output from the agent after it did all of the research, right? So it just gives us the operational costs about chat to BT and kind of some more information along with citing all the sources. And so what we want to do now is basically we want to combine our main topic, the news article, the additional research that we have. And then we want to feed it through a writing framework and our own personal like brand guidelines or tone of voice. And so what do I mean by writing framework? Because that’s the next step here. So I’ve developed a couple of like writing frameworks that I think are really effective. I’ve hard coded them here into this code blog. You can actually do this like inside of air table, which I’ll show you in a second. So you don’t have to hard code this in. You can go ahead and make changes. But basically there’s a few different prompts. We basically have that problem agitate solve framework that I was talking about. And this is basically just like a prompt for for an AI agent or an LLM, right? It just says here’s the purpose. Create a compelling linked in post on a given topic. That generates high engagement likes comments and shares by hearing to the PAS problem agitate solve framework. Then here are some guidelines and there’s a task. Here’s the structure that they should structure and some formatting, right? Then I did the same thing for just three other writing frameworks. They’re really structured all the same with like just a little bit of changes. So this one is not problem agitate solve. It’s insight impact recommendation, right? So the insight is start with a strong attention grabbing statement. The impact is elaborate, elaborate on the implications or effect of this insight on the industry or professionals and then a recommendation. Offer actionable advice or steps that the reader can take based on the insight. So you can kind of see how this is structured. And basically all this code is doing. First, it’s just generating a random number between one and four. And if it generates one, it’s going to select this first framework. If it generates two, it’s going to select the next one and so on and so on. So basically this is just a way for us to get kind of like variety in our content. So you can see here this generated the random number three. And it used the story insight action. You know, framework instead for writing and you can see here just feeds in this prompt, which is basically the prompt for the LL. The next thing we’re doing is that we’re getting our brand guidelines. This is pretty cool. I’m just searching air table for the record that has our brand guidelines. And so if we come back into our hub and there’s just a tab over here that says brand guidelines. And if I just open this up, I’ve just copied and pasted all of my brand guidelines inside of here. We have the structure, we have tone and mood, we have focus on growth and development, support and encouragement, all these things. There are a million kind of ways that you can create tone and voice guidelines for yourself. You can give it things you’ve written, you can give it videos you’ve created, you can just like tell it about yourself and ask it to create tone and voice guidelines and adapt it from there. But what’s cool about this is that basically any time I want to come in here and make changes or updates to my brand guidelines or tone and voice, I can just do this in here. And this automation will automatically pull in all of the guidelines for us. And if I’m looking at this now, honestly, this is probably like too long for an LLM. You know, sometimes like less is more with these, you don’t want it to get confused and feed it to information, too much information, although like I have found that this has been working for me, but maybe in the future I might go down and slim it down a little bit. So from there, this is kind of where the magic happens, right? This is our LinkedIn writer, this is kind of where everything happens. And so the first thing always is this system prompt here. So all I’m doing is I’m just feeding it the framework from this get framework. No, because this is basically structured like a prompt. Like you can see it here, this is like the result of this little expression right here. And it just says you’re an expert at LinkedIn and skilled at crafting impactful copy using the story insight action framework. We already looked through this, right? Basically, this is just the system prompt for the writer, so knows how to write the content. From here, we just have, please, this is a user prompt. Please craft a LinkedIn post based on the following new story. One on this out of that, new story and additional research. Focus on the article headline and summary. So here’s the article headline. And I just piped that in. You know, from down here, we have get source, this is just from the airtable database, the headline and the original summary, then any of the additional research that’s coming from our little research bot, right? So again, you can see basically what is getting plugged into the agent or the LLN. So here’s the headline saying, please, and thank you to chat to you. It’s costing open AI money. Here’s the summary, politeness, when interacting, etc, etc. And here’s all the additional research that was done. So basically, it’s combining all of this information with the writing framework. And then on the bottom, just says, be casual, Spartan, and use normal language. Please follow these tone of voice guidelines. And now I’m just putting in the tone of voice guidelines that I already showed you, right? So we have basically these three things we’re combining, the new story, the writing framework, and our tone of voice guidelines to create a lot of variety in our LinkedIn content. This ensures that we can post new content every day without it getting like boring or repetitive or done, right? From there, it’s just coming down this if node. And so all this if node is doing is looking to see if we have an image generated. And basically, it’s just looking in this field here. This needs image field. And if we have yes, it’s going to the top route and generating image. If it has no, it’s just going to this bottom route and it’s just updating the text because we don’t actually need an image. And so if we don’t actually need an image, you can see this one we had set to know. We basically have the new story about cloud canal or your Gmail that pulled all this information in. And here’s the LinkedIn post that it wrote. At first, I hesitated to embrace AI my daily work routine. It felt risky, like handing over the keys to an unknown driver, but this technology evolves so must we. And here’s the rest of the post. Here’s what I learned. And now this is where it’s pulling in some of that research, right? Trust in AI is growing. 34% of US consumers will let AI make purchases for them. That’s kind of interesting. And the prizes are increasingly automated with AI agents. Over 93% of IT leaders are on board. Also pretty crazy. And then privacy and data security are paramount. Robust measures are critical. So here you can see it’s folding in some of that research into this article, right? Or into this LinkedIn post. And then for this top route, you can see we have something similar, but if I come up here to saying, please, and thank you, we have this LinkedIn article. Let me tell you about a time when I thought of simple, please and thank you with just polite gestures. Turns out in the world of chat, these words are costing open AI a fortune. And then it goes in to a little bit more detail. And then also if we come down here, now we actually have an image generated for us to go along with this article. So we don’t always have to have image posts. Sometimes we can have text only posts. And again, this is just to create variety like on our LinkedIn feed. I generated this image using dolly 3 inside of chat you can see I think I talked about this. I feel like I said I might have used flux for this, but I’m actually using dolly because I want to try to future proof this knowing that chat to be key was releasing their new model. So you can see here I’m just generating this with open AI dolly like right now because I have a feeling that in these next couple of days, they’re going to update this so that I can actually use the new model and it’s going to be like really as simple as just like swapping in a new model and this little drop down here. All right, so let’s go back to where we were. The LinkedIn writer has basically written our post, right? And so it says yes, like we do need an image. So let’s come up here. And what we want to do is we just basically want to like pull out the type of content. The reason I did this is when I was working with the client, I was thinking about how they can have like different styles of posts. He kind of gave me like a little bit of information about the type of content he wants to create and his brand. And he had like five or six different things that sort of came out of it, right? Basically, he has like conceptual posts about like flexibility, belonging, remote work, comfort design, human posts that are like stories or daily routines. These are kind of like personal to ham, right? Strategic posts with investor insights, property optimization, business growth, this person’s in real estate. And again, these can all be like adapted for you and what types of content that you talk about. And symbolic and reflective. So basically, I’m just saying that like your task is to identify the main theme of a given article. And then it says these are your only choices. And then basically, I’m just feeding it in the article just for reference, right? I’m actually just giving it the summary here. And you can see it kind of analyze the article and says this is a strategic post. So the reason I did this again is that from here, I’m actually getting the style. So in the same way that we sort of structured these writing frameworks, I’m also structuring like visual styles for the image generator as well. And so depending on the type of post, if it’s strategic posts, a human post, whatever the other ones were, right? Like you can scroll down here. It’s a conceptual post. It’s actually generating a different style of image depending on the type of post it is. So this is a great way to kind of like build consistency in the brand. We can have like kind of these different looks to keep variety in the content. But every time you see like a conceptual post, you’re going to get a soft minimalistic illustration. Every time it’s a human post, it’s a story about like me personally, you’re going to get like something that looks like lifestyle photography. So it’s just building this repetition and kind of like brand recognition, the more you kind of create these posts. And this is basically just kind of like little simple image props, like create a simple clean illustration with smooth lines and minimal details, use muted earth tones or soft pastel collars, etc, etc. And I basically just have kind of like a different writing prompt for each one of these. So again, we decided that a style keyword is strategic. And so here is the style guy that it pulled out, you know, which is for the strategic style post. We always do editorial flat lays or still life. And if we come back in here, you can see that’s exactly what this is. So we’re getting that getting that style. And then we’re just coming to an image prompt generator. And we’re just saying you’re an AI generator vivid image prompts for a specific article based on summary. Do not output any special characters. I’ve noticed sometimes it puts like quotes or dashes in the output. And then it just gets all messed up when you actually try to generate the image. And then I just give it some examples and some constraints. And I’ve said, here’s a style guide. And then I piped in that style guide coming from the know before. So it knows exactly what we want this to look like. And then I’m just piping in the article summary here. And I just said again, do not output any double or single quotes or special characters. And here you can see the output editorial flat layer still life, artfully arranged work desk showcasing AI interaction tools laptop with code snippets energy efficient light bulb. And it goes on right. And then from there, we’re just feeding that exact prompt into this image generator. We’re using the chat to be T generate image. No, we’re using dolly. We’re feeding it this prompt here. And if you come into the options, there’ll be an option here respond with image URLs. Otherwise, you’re going to get kind of like a binary data file. You want to make sure you respond with the image URLs because that’s what you actually need in order to store the image like physically as an attachment inside of air table. So just make sure you have that check. And then here, I’m literally just waiting five seconds. I’m using just like a wait timer. The reason I’m doing this is because I found that like all those images was generated. It kind of like took like a couple seconds just for it to kind of like register, you know, on the server. And so I just wanted to give it a little time to make sure it registered here. This is a really basic like HTTP module. Do you want to add that? You can just type in HTTP and make a request, which actually in hindsight, now I’m thinking, okay, I actually probably don’t even need that note. It’s not really costing me anything. So I don’t mind having it in there since the system is working. I don’t really want to break it. But I actually don’t even think I need that because I’m using the direct URL to actually store the image. So basically, we just have this last air table module, which is just an updated record module. And all this is doing is it’s just finding all the way down at the bottom. It’s looking at this get source. And it’s just coming in here. And if we scroll down, we’re basically just taking this record ID and we’re just matching the record ID. And so you can see here it has all this information. And so we’re just saying, all right, match this record. And for this record, we want to make sure we update the LinkedIn copy that’s coming from the LinkedIn writer, which is up here. We’re just going to drag and drop this text in there. The image prompts, just, you know, just to store it, just so we have it, basically creating image prompt here and just storing that in here. And then the post image, and this formatting is actually important, creating this array with these kind of like these brackets here. I found that if you just put in the URL, air table won’t store, you actually need to put it in this array format. So again, if you download this, I’ll leave this little bit of code in the school community inside the classroom. So you can make sure that you have it and just go ahead and copy and paste that in. So it’s just going to download the image. And so from there, again, everything gets stored in this air table database. And the next thing that needs to happen is super, super simple. We just need to post this to LinkedIn. And so we come in here, say we make some edits to this. If we want to make some edits, like I hate when they say things like here’s the kicker. And so, you know, let’s get rid of here’s the kicker. So let’s just go with politeness actually. So you can make edits. This hasn’t posted yet, right? But then say we’re happy with this. And we just want to go here and we just want to go ahead and click approved. And the next thing we have is just this little AI agent, which again is set to run on a schedule. So you can set this to run whenever you want. And all it’s doing is we’re searching the record to find something with the status approved and that the social channels are LinkedIn. From there, we’re basically just looking to see like, is there an image in the attachment field? If there’s a, if there isn’t an image, just post the text. If there is an image, download the image and then send that to LinkedIn. So again, I’ll just go ahead and test this once. You can see, we’re grabbing one item. We’re basically pulling in. You can see here the saying, “Please and thank you, post along with all our LinkedIn copy.” And we have the image down here. And it’s decided that yes, you know, this does have an image. I was going to this top route. We’re just posting the URL from post image. We’re just typing this into this simple, again, get module, which is an HTTP request. From here, we’re just connecting this to our own personal LinkedIn. And then basically, I’m just feeding the text in because you can see here, it already says the input data is the input binary field is data. And that’s just coming from here. Now, if we come back in to get source, we can just scroll down. And you can see here, I’m just grabbing this LinkedIn copy and I’m posting this in here. And then at the end, I’m basically just updating that same air table record just so it says posted and that we have the date that it was posted. So if I come back in to this, please and thank you. Now you can see that all this is, the status is now posted. And we actually have a date when this was posted. And so let’s just come over to my LinkedIn account. And if we scroll down here, you can see, we have this new post already to go and I can go ahead and I can just click more. This opens up. And this just says, let me tell you about a time when I thought some simple, please and thank you. We’re just polite gestures, et cetera. And we have this image here, which again, I’m going to update with the new chat to be team model as soon as that’s for these for the API. If you thought this video is helpful, please make sure to subscribe to the channel and check out this video up here where I create an entire automation to go through your email inbox and respond to new business inquiries and sponsorship opportunities to make you money every single month. I’ll see you over there.

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