So What? What’s New with the LinkedIn Algorithm?

Well, happy Thursday, folks. This is So What, the Marketing Annex and Insights Live Show. I'm Chris. John is here. Katie is speaking at uh Mima at a uh an event today.

So she is on the road being uh the star of the show. Um this week we are talking about what's new with the LinkedIn algorithm. And so for those folks who don't know, for the last oh, I forget how many years now I think we're on the fifth edition of the LinkedIn Guide, which is the unofficial LinkedIn algorithm guide. We've been publishing this, it's gotta be at least four years now. Um what's new in the system, how it works, uh, and and what things you should be doing.

And this year's this this quarter's version is totally different than previous versions because we used to publish a very, very, very dense technical paper. It's like a hundred and two pages long, uh filled with all sorts of information derived from LinkedIn's actual engineering teams. We're not gonna read 102 pages of Techno Babel. Uh, which is the LinkedIn algorithm guide playbook, uh, which you can get at this URL down here, trustincies. Uh that one incorporates uh some skills for your AI tools, uh, and as well as one pagers, supplementary materials, a whole bunch of stuff uh that's in there.

But John, to start off today's show, based on your experiences, what have you seen working and not working with LinkedIn? Oh, this is a great question for me. Yeah, I haven't been able to make anything work on LinkedIn. So this is this is perfect for me. Um, LinkedIn is and it's funny because we were doing this immediately.

I jumped in, I was like, all right, what like what am I even doing on LinkedIn? Like, how is everything going? And so as I dug down, I see I that I have I have over 5,000 followers, so I have a decent follower count, and I have over 3,000 uh connections. And in Analytics for Marketers, Katie had actually thrown out, she was like, Hey, you know, over in there, how many people do you know? Oh, I take that back.

I have 4,000 followers, 4,099. Um, but she just said, Hey, how many people do you know? And as I was thinking about it, I probably know you know 15% or so of my connections. Like I've done business directly with know them. And then it's weird for us because I will accept pretty much any followers from marketing over coffee, because I feel like I know those people to some degree, or more appropriately, they do know me.

Like they know what I am working on and where I'm going and a bunch of things about me. So I'm pretty open with that. And I know other people are much more militant. Um, but yeah, I've just been following the kind of standard advice of you know, trying to post daily, and I end up maybe posting three times a week. And a lot of times I will share content from Trust Insights, that's number one on the list.

And then whenever I just find something interesting from somebody I like that you know I really feel is worth sharing, I'll share that kind of stuff. So yeah, that's what I'm doing. And uh I'm constantly confused by my alerts that I get from LinkedIn, you know, every week. I'm either it's I don't understand how I can go down 30% every week in engagement, yet somehow that is what's happening with my account. And I'm still getting likes and shares and stuff.

So yeah, all that to say I'm I'm the perfect student for this. I'm excited to you know see what you've got in this round. And it's been interesting. I have at least followed all the previous papers. There have been so many times that it's changed.

You know, there have been times where we and we'll go on, we'll see people saying, Oh, this is how you do it. And I'm like, no, Chris's paper last month said you you have six months' info, and that's totally invalid. So, you know, get with the program. So the that's that's a good segue into sort of a high level overview of what the system even is, because one of the things that people get the most wrong about uh LinkedIn is is actually the phrasing itself, the LinkedIn algorithm. There isn't one.

Um, there's two major subsystems. Actually, that's not true. There's three major subsystems, which two of which are large language models. So they this is comes from the engineering post and from some papers that they've the engineering team has published for conferences like NERUPS, ICL, ICLR, etc. And the first um model is based is built on Meta's Llama 3, uh, the three billion parameter version.

This is called the causal uh causal recommender engine. What this does is it looks at words, it looks at the words that you use in your posts, in your profile, in uh your comments and things, and it creates what are called embeddings, essentially sort of topic maps of the of who you are and what you talk about. And it does that for everybody. And what and it when the LinkedIn system is assembling your feed, it's looking across all these members and all these different words and things and these topics and saying, well, which five or six hundred pieces of content would be best aligned from a word perspective, from a semantic topic perspective for John to see in his feed. That's step one.

Step two is what's called the generative recommender model. This is a much larger model. This is sort of the ranking model that says, okay, based on who John has engaged with in the past, you know, who John likes, who comments on John's stuff who shares, um, you know, connections, new connections, especially. Given all that data, out of this 500 candidates that the causal recommenders created, which ones and in which order should I show them to John? So it's two different models.

And there's a third model, the trust and safety model, which says, like, is this spam? Is this you know what is this garbage and things like that? And so we as practitioners, we have three different levers, right? So we have two gates, uh, two models and three levers. Uh, the three levers we can control.

What's in our profile, headline, description, job tasks, and things like that. And all those words are part of that first model to help us understand, help the model understand who we are and what we're about. The second is the stuff that we post and how we engage with it. And then the third is how we engage with other people, especially people within our network. And for the purposes of engagement, it's it's likes, comments, shares.

Um, but comments tend to be the most useful. So when it comes to using LinkedIn, like the bare basics are you've gotta have words that are related to the people that you're you're care about. Um so if your headline is I help people enlighten them, their their marketing, and and you actually just do like social media. Um those words don't have as strong a semantic or topical relevance to say you know, CMOs who are like, hey, I um the I used to be the VP of social media at this company. Second is you should be publishing content as as often as you can as you can create useful content that gets engagement.

Is that there's a big asterisk span. Yeah, right, right. That's uh there's a lot of heavy lifting in some of those phrases. Uh, and then the third is you should be going out and interacting and engaging with other people and engaging with people who engage with you on your stuff. So those are the three levers that that people can control.

That sounds good. I and I you know, thankfully, none of those are surprise as far as I'm at. But the profile one is interesting because I I am in the same boat, I see all these pro you know, statements, people saying I, you know, I don't know, just like crunchy new age weirdness, you know, like I do connections across and find synergies, you know, and you're like, buddy, you work in HR, like what are you talking about? But I'm kind of so will you are we basically you just dive into the three here where you can see what's going on on these three fronts. I mean, so this is this is fundamentally like the system.

Now, where this gets challenging for a lot of folks is these are language models under the hood. So they're probabilistic, which means that you will get inconsistent results out of LinkedIn. In the same way that if you copy and paste the same prompt into chat GPT three different times, and you'll get three different answers. They'll be, you know, directionally similar. Like if you ask, you know, what's a good coffee shop near me, you're gonna get directionally simple answers, but you're not gonna get the same answer every time.

And the same is true for these two systems. You can publish the same post, you know, once a day for five days in a row, and you're gonna get different results because you have two different language models under the hood that are taking in uh constantly changing data, and that data is part of the prompt that these tools are fed. They like they are actually essentially you know gendered AI models that are being prompted by LinkedIn's own system, which contains stuff like your post, your engagement data, your connections, and underlying that is the LinkedIn knowledge graph, which is this ridiculously huge graph of everybody on LinkedIn. I think they said it was like 16 petabytes across six different data centers planet wide with like 10 second, 10 millisecond latency. It's it's insane.

Yeah, well, and this is kind of opened my mind a little bit because I hadn't thought about it, but because of that, it is you are pulling the slot machine lever every time. It's not deterministic at all. It's the opposite of deterministic. You're you're gonna do this, and who really knows what the heck is gonna happen today? Right.

And that and that's what makes it so difficult. So one of the things that uh is in the the paper, and uh in the premium version, there's like uh analytics skills that you can actually install into your claw or your chat GPT or whatever that you take your data out of LinkedIn you can export it from your profile and start doing analysis to figure out what is actually working. So as an example um I took my LinkedIn data and there's it's it's funny because the way we have uh our our social media sharing system we use a company called Agora Pulse and our account manager Kelsey uh also does schedule posts um uh and I schedule posts so for example I schedule uh you know a shared post for the podcast etc and then I also will just get on LinkedIn and just you know show up and just rant uh in posts and it's interesting when you put your data from LinkedIn into an AI tool to say like analyze this for me and you give it some requirements it's interesting to see what works so in the in this version here let me make uh this bigger here so for the posts that I publish this now this is a year's worth of data um I on my posts when I just show up and just like rant on LinkedIn um I it outperforms everything else by 4x easily uh 4x almost 5x so those other things that I'm doing like scheduling uh the the the short videos for the trusted size podcast um that Kelsey is scheduling on my account they they do not generate near the level of engagement. Yeah, that's interesting. So I uh, you know, seeing that my first thought would be that's that's like, okay, well, everything's scheduled or scheduled by the team is like, here's our podcast, here's a white paper, just kind of the standard stuff.

Whereas the native post is, you know, something that makes you angry enough to go to the keyboard, right? Is that is that the truth? Okay. Right. And so for the share of impressions, native posts, 90% of my impressions come from that, well 89%.

Share of comments, 94% and then typical impressions 1,136. Scheduled by me. So these are like the trust inside podcasts and stuff 3% of impressions, 3% of comments, 273 impressions is the normal count. And then by the social team, 113 posts, 8% of impressions, 3% of comments. So if I was going to if I wanted to and this is where we start to get into into really challenging territory.

If I wanted to maximize my LinkedIn performance metrics, I would stop scheduling things. Right, well I thought of that or do you think there's an opportunity to make the schedule stuff sound more like you off the cuff or is that or do you think it's different content entirely? It's different content. Okay. So it's a content issue.

Yeah. Um and interestingly when I look so just over the overall history now for my account you can see that since March um I've there's just been fewer impressions on a on a per month basis and it's been suppressed. I don't know if this is seasonal if or if this is there's something else going on here but it's definitely uh for and now that remember this is my account I would encourage people make sure that you do this on your own account. This one is interesting the typical post with a link gets 38% fewer impressions than a typical post without a link. So uh even though there's nothing in any of the engineering content that says like do or don't add links to your posts, there's no analysis that shows one is better than the other.

For my particular data, posts without a link do better by by uh a substantial margin 38% better. That's interesting. Okay, and we have Jesper threw a question in here too. He's saying that actually I can just throw it up. Your average impressions, he said that's tiny, given you're just because you are a LinkedIn top voice, too.

We can use I can brag about that here for you. That uh, you know, over 45,000 followers, but that's you know, the average number of impressions is not a big number by any amount. What's just what do you think about that? Yeah, I mean, that that does not surprise me. Um with one of the things that's that's true now of almost all social networks is that the account level stuff doesn't matter as much as the individual pieces of content, right?

The whole idea of today's algorithms are that any piece of content that that resonates well will take off, um, regardless of whether your account has one follower, a thousand followers, or a million followers. And it's not uncommon, particularly if you you look around at you know, some of the big accounts out there, they'll post something and they'll get about the same percentage of visibility for for posts that don't land. Um so it is it is it can be challenging to try to figure out like what actually does work, and it is on a post level basis, not an account level basis. So that's an interesting point. So then it is a matter of you know, anybody can break through.

If you've got good content, you're gonna appeal to the model with that one individual post and it will run. So, yeah, so that's actually quite a statement to for you to be able to consistently stay that far ahead of the pack. That's because it, and again, that goes back to what you just said. How frequently can you publish decent content or engaging content? It's not a matter of if you crank the knob up to 20x what you're currently um cranking out of spam, your odds are probably going to get worse, not even get better, but it's gonna you're gonna end up worse off.

And what's really interesting is when you when you look at take a step back and look at the big picture about why social networks behave like this, it overcomes the cold start problem. So a new account can land pretty well, but it dramatically undercuts influencers and personalities because uh the all these big networks like no, no, we want the ad dollars coming to us. We don't want brands paying you the person. We want we want the money. So we're going to diminish the effect of the account and focus on the content.

And you know, obviously, the if people enjoy the content, they stick around and they don't get that person level uh you know uh boost, which is uh again uh no surprise. Big tech's like, nope, we want all the money. Yeah, right. But this still that is you know, with all of these networks, there's usually at some point where you can crack through and uh uh pull more than the average amount of poll to make it decent. But yeah, again, the social networks ruin everything.

That's just a given. Exactly. Um, the other thing is uh you can and you should use your AI tool of choice to do some topical analysis of your content to see what stuff sticks around, you know. Um, what thing um how your content maps to your audience and things, um, what the different data means, and then where compared to our guide, uh, you might have things that you need to go test. So like I have a lot of things that are marked as not enough evidence.

So the guide says don't spend much time uh effort on time of day. That's generally true because it's not uh taken into account. Mine says there might be something there, but there's not enough data. So I would have to build an actual testing plan and start testing at different times of the day in a more structured way so that I could prove or disprove that this this statement from the guide is actually true. Right.

Well, unfortunately, again, talking about tech ruining things, we LinkedIn is large enough. We've seen plenty of cases where the guides that are put out there give all this generic advice. And when you do your testing, just like this kind of stuff, you found, well, that's not actually true that you know the a bunch of these things, you know, you can do this and it does work, but it's just that you know, PR doesn't want that message to get out there, so they say it's not done, but that's you know, again, only trust data. Yeah, exactly. Um so the big things for me, um, I did not give uh they the AI tool enough data.

Um so it what I think would have been helpful uh and I will do for the next iteration for myself is also taking uh stuff like Google Analytics data to see for where posts where I do provide a link, does anyone click on it? Because if if if I post a post with a link and it gets less reach, but I actually get people to leave LinkedIn and go someplace I care about, that's a win. If I post a uh post with a link and nobody clicks on it, then I'm sacrificing visibility for no benefit. Right, yeah. And and having that that's the you know the one grain of truth in all this, you can always count on the clicks that you've got.

That that's you know, proof that you can put everything against. Exactly. Um, so let's take a look at what else is in the in terms of the of the the guide. So the the versions of the guides that we have, we have like I said, we have the two versions. We have the one pagers and we have um uh the full guide itself.

The big thing is this from a language perspective, focus on the language of the people that you want to be in front of. Right. So if you want to be in front of CMOs, like in my case, if I want to be in front of CMOs, I should probably not be as absurdly nerdy technical, right? Because your average CMO does not care about what level uh quantization uh in a model you're using. They that is not their concern.

And so when you think you look through the sort of the best practices from the guide based on what LinkedIn Engineering says, yeah, these these are all things that hopefully should be fairly obvious. And also, um thing that the the engineering papers don't say, but we kind of inferred is if you've got a post that did well, repost it. When you repost your own stuff, it just shows up in people's feeds again as though it was almost almost new. It just gets a little thing that says, you know, John Wall reposted this, and it's it's John Wall's post. If you got stuff that resonates well, rather than making a net new post, bring it back.

And the reason for that is the original post has that network graph data of who commented on it, who liked it, who engaged with it, and stuff like that. And as new people engage with that original post that you're reposting, it influences the graph of the people who've already engaged with it. So you're kind of greatest hits. It's your greatest hits album. Well, or this idea that you know it has momentum.

Like once it has momentum, it can keep rolling, which is crazy though, because I mean that's just as clean an example of duplicate content as you can get, but that's not, you know. I it makes sense, but it's still driving more people to click and look and it's keeping the traffic there. So they're all on board with that. Yep. Um, so that's sort of the what the at a high level is going on with the alg with the these algorithms.

It's two language models, and you're having to deal with them. Oh, I got another question here. Uh Jesper says, Normally using hashtag results in that word being included in the post URL. Is that not worth considering for SEO or post, not really indexed? Um I'm not sure I understand.

Normally using uh the hashtag results in that word being included in the post URL. Is that not worth considering? So for when LinkedIn posts show up in search results, not all posts do, and I've not been able to figure out a rhyme or reason as to how a search engine like DuckDuckGo or Google, how they even surface what posts are and are not available, because I've run into all kinds of issues where um I've not been able to even get data from from one platform to the other. Um I use the hashtags in posts more as a discovery feature, so that because the there's in both in my my own data and in uh LinkedIn's technical papers, it's not something that helps or hinders. It doesn't, you know, using hashtags using a certain number doesn't seem to matter one way or the other.

It's like this is a magic number. When we had uh when I sat in on a LinkedIn Top Voices briefing earlier this year, um they said, you know, three to five in a post is fine. Uh it's not gonna hurt uh anything, it but it also may not help anything. So I would say, uh I guess uh from a URL perspective, I don't know that from an SEO perspective. I I don't well, first of all, I wouldn't necessarily want to be putting uh link juice for LinkedIn to take as opposed to like my own stuff.

I'd rather uh spend my f my SEO efforts on my own on my own properties. Yeah, right. And then there, yeah, I don't like I said, there's we are privy to no data on that. We have no idea how the heck that works as far as what gets in there doesn't. Yeah, I know uh Google and and other companies have license oh the you know, LinkedIn stuff has a licensing deal with Google, so does Reddit.

Um Reddit has a licensing deal specifically with Google. Um so I'm not sure how that what the terms of that deal are and what data does and does not flow through. I do know uh that Google itself will uh restrict you pretty heavily if you start trying to scrape LinkedIn data through Google. Uh just uh from from first hand experience, right? Nothing that uh we would perjure ourselves with, but you know, we've heard people are talking and we've heard from many people that this is true.

Exactly. Um if I if I had to boil down and say what two things should you do. Um number one is you should make sure your profile is is up to date and contains language that you want to be found for. And that can go um even to some degree to like your older experiences to find your profile, but definitely your headline, definitely you're about, um, need to be updated with how you want audiences to uh perceive you and how you want machines to semantically uh perceive you. And then the second thing is if you have the time, um, I would spend five to ten minutes a day intelligently and very humanly commenting on other people's stuff.

Do not use an AI comment bot. Do not do it. Um LinkedIn debuted was in July. Uh they debuted the seems like AI slop button, and they first debuted it for the feed and it got some use, and then it debuted for comments, and people started smashing the you know, slop left and right. And it turns out uh I saw a post um from uh from uh LinkedIn's uh press room saying people who um uh posts that have had been posted in content that has been flagged as slop get 40% less visibility.

Yeah, no doubt that they would immediately lock you down because they know you're trying to game things. Mm-hmm. So do not use AI comment bots. You're not fooling anybody. If you go to somebody's LinkedIn profile and you um and you look at their, you know, the the the three most recent comments and they're all structurally identical, you know, keen insight, you know, uh one sentence restatement post, and then you know the usual curious, what's your take on this?

It's like, oh, it's the exact same thing over now. No, you you're just getting slapped with the slam spam button. Yeah, uh again, it leaks everywhere. There's nothing we can do about that. But yeah, I you know, try and be human.

We we can at least ask. Yeah, but again, with LinkedIn's uh corporate press room saying, Yeah, stuff that gets flagged as as AI slop gets 40% less visibility. That's a that's a pretty good uh bat to the knee. Yeah, that's definitely you know, if it's it if the ethical argument isn't enough, that's definitely encouragement. Exactly.

Um the second thing I would do is well, so yeah, so po uh profile and comments. Those are the two things that you should do. If you have more time or LinkedIn performance is important to you, I would spend some time doing having your AI tools do the analysis of your LinkedIn content, doing stuff like topic modeling, doing statistical tests to see, you know, to giving it hypotheses and testing those hypotheses and saying, Hey, I think hashtag, you know, five hashtags or more is the way to go. Have the tool ingest your spreadsheet, do the analysis and say for your account, yeah. Because one of the things that's really important is that, and this kind of goes back to what Jasper was saying originally, my account is different than than other accounts.

Everyone's account is different, but like the top voices accounts, even those get like different buttons and features and stuff. Um there's there's a there's a whole set of back-end systems that are not exposed to regular accounts, and so I can't say what works for me is gonna work for somebody else. Right, and then you get the secret insider newsletter too that I'm not gonna talk about. But for your accounts, you absolutely can do the analysis and say what's working for you and then use your AI tools to say, great, here's where we are. Here's the performance we're getting.

Our goal is to get to whatever, whatever your number is, a thousand impressions a day or two thousand impressions, or what have you, or or you know, whatever uh downstream measure you care about. Help me build a testing plan to for you know when or what or how to post different content formats to see what works better. For example, one of the things that I that it pulled out in my own plan is it said you really underutilize things like carousels, right? You you you should probably go in and and test that. Like, okay, I have a testing plan to do that.

Um Jess says, disturbing a quick reversal link to support when they roll out, seems like ASLA explicitly there's a matter of taste opinion, they would not penalize post. Well that was assuaging the guilt to for people who you didn't want to click. Yeah. The thing is though, I think, and again, uh neither of us worked at LinkedIn to work at LinkedIn, but I think LinkedIn saw pretty quickly how much people liked that feature, how much people use that feature and said there's a there there in terms of yeah, people are people all in on on slapping the slap button as as fast as they can. Yeah, yeah.

Well, I mean, there is so much, just absolute zero effort garbage. And it's it's kind of rare that people get the ability to do the slap back, you know. It's usually down votes and things like that end up uh getting abused and getting rid of. So at least for a little while, you have a golden arrow where you can uh punch the gopher. Oh god, that's that that's a throwback.

Um so yeah, in terms of uh uh what you should be doing next, it's it's those testing plans to say, okay, let's you know, ask your favorite AI tool, here's my data, you know, do the analysis, do the uh pick the statistical tests that you know best fit this data and come up with a testing plan for me, and then you can start uh doing the testing plan and even having the AI tools take your original ideas that are yours and just reformatting them. So I would not advocate that you have machines um just outright manufacture the content yourself. But if you've got a great LinkedIn post, particularly if you've done a LinkedIn post that's done well in the past, hand that to a tool like Claw Design or Chat GPT work, whatever, and say, without changing any of the wording, turn you know, take the the highlights and turn this into a set of carousels for me so that we can see how it performs, right? Because it's it's still your words to make sure the tool doesn't manipulate words but in change formats pretty quickly. Um the big thing this week that a lot of people have been trying, and we tried uh ourselves is you can give any generative AI tool that has coding skills the ability to generate motion graphics, like pretty nice motion graphics.

So you can say, here's this LinkedIn post, help me generate a motion graphics reel. Yeah, right, spin it up and have it go. That's I guess that's it's funny. That's just another one of the classics. I'm just like, uh, when am I gonna take the time to figure out how to mess around with carousels?

Like one of the things that just never happened. So yeah, um, it'll be very interesting to see if you optimize some stuff and throw it in there. Does it get significantly better performance than just average post? Yeah, even taking a look at like you know, the the basics of um just like video and like video with a voiceover. Just if you were to do slides and things uh and do that.

Uh Tom says, I I I wish the hate of AS local transfer is uniformly dissatisfying chat GPT style video uh visuals. Yeah, we all know them. And you know what causes that? It's it's when people prompt the AI tools, they do not give it any design language. So it always defaults to that exact same slightly weird, slightly off-putting font.

That same general guide. If you give it a specific um design vocabulary, it does a great job with it, but nobody does because nobody knows anything about design. Right. Well, yeah, it is it's we've talked about this. It's that that bottom level has been lifted up 30%, yet it all looks the same.

Yeah, I've never seen so many amazing bake sale and tag sale posters as I have in the past few years now. Like it's unbelievable. You'll have you know, somebody selling their pile of junk on the corner, but they'll have like Harry Potter, you know, flying over their house for their tag sale on Sunday. And it's just yeah, I I I can understand how people get tired of that rather quickly. That that'll be one of those trends that future generations will look back and you know both laugh and feel bit of pity towards.

Exactly. Uh here, this is a a 12-second example of the the motion graphic stuff that we were playing with earlier this week. Yeah, I saw that go live on social. I was like, that's fantastic. It's just because you know, you don't want just the boring logo logo to slap up there for three seconds or whatever, but you you know have it moving a little bit to catch eyes and catch attention.

And yeah, that's that's bang on. Yeah. So if you've got an AI coding tool that can that can generate the vector graphics themselves, um, it can then assemble them into uh you know video. Uh so even though a tool like, for example, Claude Code does not have image generation built in, it can do the coding necessary to create motion graphics uh stuff. So that's one of those things that again, if you've got some ability to take existing great content that you've made, you know, add it to different formats, have it have do the short videos, do the vertical videos.

Even you could even use a tool like uh used to be called notebook alum, it's now called Gemini Notebook. If you've got a set of LinkedIn posts of yours that have done really well, you can turn them into 9×16 vertical video snippets and say just summarize this post extractively, which means using the language that you wrote in the original post as part of the video so that it's still preserving your words, and then that can go into your LinkedIn testing plan to see if that kind of video resonates with people on LinkedIn. Sounds good. That's a good way to roll that. So to wrap up, we have two versions of the LinkedIn guide.

Um, there is one version that is totally free. Um, that is the trust assess. This is the one on the right. This is the playbook guide. It's only 47 pages long.

It's easy to read. There's no technical stuff. And then there is the deluxe version, which is the version on the left, which is the full 102-page paper. Plus it incorporates specific guidance from the paper for four different roles the founder, job seeker, marketer, and sales. For clawed code, clawed co-work, chat GPT work, anti-gravity for helping you think through changes you want to make to your profile, to your engagement strategy, to your content and uh your analytics for LinkedIn.

And then one pages again for your team for how do you do things like you know, operate on LinkedIn, measure on LinkedIn, because we got some feedback from the previous version of the guide that the it was a little dense. So we we we put handles on and made it a lot easier to uh to digest. So the deluxe version is uh US $20, the free version is free. Both are available now, and we'll be pestering people much more about them next week. That sounds good.

Yeah, I'll be going out on all channels. So watch your inbox. Exactly. Any final thoughts about LinkedIn, John? Anything you're gonna go try?

Well, uh, yeah, I mean, obviously, I'm gonna check out the paper because it because I can get the $20 one for free. So I'm totally and then I will you know dig in. But I overall, the big message though, I am at least happy that you know this idea of creating the best content you can and putting it out there, like that's the most important part to get started with. I mean, optimizing is great, but uh you know, just keeping up with the normal flow of getting good content out there will keep you on track. But I am so yeah, the the big takeaways for me, carousels.

I do need to play with that and experiment with that. And then the idea of just resharing stuff that has worked well in the past. I have a ton of stuff over like the past five years, stuff that I could just pull out of mothballs and just reshare it again and see if that runs. So those are two that I could put to work right away. Exactly.

All right, folks, that's gonna do it for this week's show. Next week, I believe we're we're actually all hanging out next week, and the week after that will be in Cleveland for the marketing AI conference. So I don't believe there'll be a live stream that week, or if it is, it'll be impromptu and probably from the lobby of the Hilton. Um, but that's gonna do it for this week's uh live stream. Thanks for tuning in, folks, and we'll see you on the next one.

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