This list covers twelve LinkedIn carousel makers and generators, and says which of the three jobs each one does, what is free, and what it costs. It starts with ours.

If you only want to make one carousel today, the free carousel generator does it in your browser with no account.

The three jobs a carousel maker should do

Write the slides. A carousel is a sequence: a cover that earns the first swipe, one idea on each slide after it, and a last slide that asks for something. Some makers write that from an idea, a post, or a link. Others give you empty boxes to type into.

Design them. The slides should look like you: your colors, your type, your photo or logo. Check two things here. Does the tool use your brand or its templates? And does the free version put its own name on your slides?

Post the PDF. On LinkedIn, a carousel is a document post: a PDF where each page becomes a slide. LinkedIn Help says a document can be up to 100 MB and 300 pages, and every page has to be the same size. Some makers schedule the post for you. Others hand you the file.

The twelve at a glance

How this list was put together: we read each tool's own carousel page and pricing page on 16 October 2026. Prices are the monthly-billed price unless the table says otherwise. "Not stated" means the tool's own pages did not say; "Not shown" means the price did not appear on the page when we read it, and "Not read" means the pricing page would not load for us. We did not score the other tools' output, so entries 2 to 12 are in no strict order.

Which LinkedIn carousel maker to pick

• Written in your voice and posted with the rest of your week: Depost AI. It writes the slides from what you have already published, and posts the PDF after you approve it.
• Free, with no account and no watermark: Depost AI's carousel generator.
• Writing and design in one place: Depost AI writes the slides and its carousel generator designs them. Taplio, Scripe, Contentdrips, and PostNitro also do both; PostNitro has the lowest published price, $15 a month.
• Agencies with several clients: Depost AI, with each client in its own workspace and the client's approval before the carousel posts.
• Designing every slide by hand: Canva, with more than 1,285 LinkedIn carousel templates.

1. Depost AI

Best for: creators, B2B teams, and agencies who want the carousel written in their own voice and posted with the rest of their week.

This is ours, so weigh it accordingly. Depost AI is an AI content team for LinkedIn: four agents that plan, write, design, and schedule your posts, with your approval on each one. For carousels, it does all three jobs: it writes the slides in your voice, designs them in its carousel generator, and posts the PDF.

It writes the carousel in your voice

Ask for a carousel in the chat, or pick one from your weekly plan. Depost AI returns the slides one by one, each with the job that slide does and an idea for its picture, plus the caption for the post and a note for whoever designs it.

It writes from your brand memory: your positioning, your voice, and every post you have published with its numbers. So the carousel sounds like your posts, not like a template.

It designs the slides

Depost AI's carousel generator designs the slides and builds the PDF in your browser. You pick one of five styles, add your photo or logo to every slide, and download a square PDF with type sized for a phone. There is no watermark and no account, and your photo never leaves your device.

If you do not have the words yet, the free carousel content generator writes eight slides from rough notes. Before you post, the carousel tester shows the PDF inside a post at phone width and checks it against LinkedIn's limits.

It posts the PDF for you

Attach the PDF to the post in Depost AI. The post and its carousel go to your approval queue together, and once you approve, the PDF publishes as a document post through LinkedIn's official API at the time you set. For single-image posts, Depost AI also designs infographics in your colors and type.

Price: Solo is $59 a month for one LinkedIn account. Team is $99 a month for the first account and $59 for each one after that. Both have a 7-day free trial. The free tools need no plan at all.

2. Taplio

Best for: turning a post that already did well into a carousel.

Taplio's carousel page says its AI writes the slides from a one-line idea, a blog post, or a YouTube video, designs them, and gives you a carousel ready to schedule. It can split one of your posts into slides and write a cover hook for it. You set your colors, fonts, and logo once in a brand kit, then export a PDF or schedule the post from Taplio.

Taplio's plans start at $39 a month, and the Carousel Generator is listed in its plan comparison. There is a 7-day free trial. An older free carousel page at taplio.com/carousel still offers an editor, and the example slide on it reads "Made with taplio.com/carousel".

3. Scripe

Best for: teams that want carousels in one brand kit across several profiles.

Scripe's home page says its agents turn any post into a carousel, an infographic, or a quote card in your brand kit, with one brand kit for every profile. It also says the agents find carousels that did unusually well in your market and rebuild the one you pick in your brand.

Scripe's pricing page shows yearly billing: Solo is €55 a month billed yearly. Every plan has a 7-day free trial, and Scripe says a credit card is required.

4. Supergrow

Best for: typing a carousel slide by slide in a free editor.

Supergrow's free carousel generator is a slide-by-slide editor. You type the text for each slide, change the fonts and colors to match your brand, preview it, and export a PDF or a set of PNG images. Supergrow says its free tools need no sign-up.

In the paid app, the carousel maker is part of the Pro plan, which is $39 a month billed monthly. Every plan has a 7-day free trial.

5. MagicPost

Best for: agencies who already use MagicPost for writing and scheduling.

MagicPost lists carousel creation as a feature of its paid app. It is included on the Creator plan, $69 a month billed monthly, and not on the Growth plan at $49. Its pricing page does not describe how the carousels are made.

MagicPost has a free trial with every feature, and no credit card.

6. ContentIn

Best for: a free editor with your name and photo on every slide.

ContentIn's free carousel generator starts from a theme or a blank canvas. You click to edit the text on each slide, set your colors in the Theme panel, and add your name, handle, and photo in the Brand panel. It exports a PDF at 1080 by 1080 or 1080 by 1350 pixels.

In the paid app, ContentIn lists unlimited AI carousels on its Pro plan. The app has a 7-day free trial.

7. Contentdrips

Best for: turning a blog post or a video into a carousel with templates.

Contentdrips is a design tool for social posts. Its site lists an AI carousel maker and post writer, more than 100 templates, and brand kits with your fonts, colors, and logos. It can turn a topic, a blog post, or a YouTube or TikTok video into a carousel, and publish to LinkedIn and Instagram.

Its pricing page offers a free trial with 50 credits over 7 days, and its home page says a free plan is available. The plan prices did not appear on the page when we read it.

8. PostNitro

Best for: a low-cost AI carousel maker that also schedules.

PostNitro writes and designs a carousel from a topic, an article link, text you already wrote, or a post from X. You choose how many slides. It has more than 100 templates built for a 4:5 feed, a brand kit that redraws them in your colors, and scheduling to LinkedIn and other networks.

PostNitro says it is free to start with no credit card, and its editor can be opened with no sign-up. The Starter plan is $15 a month billed monthly, with watermark-free exports and up to 8 AI slides per carousel.

9. aiCarousels

Best for: a free carousel editor with color palettes picked for you.

aiCarousels calls itself a free carousel maker. Its home page lists color palettes and font pairings chosen to go together, templates you can change to match your brand, and a PDF download for LinkedIn. More tools, such as a post generator and an infographic generator, are on its PRO plan. We did not find a price on its site.

10. Canva

Best for: designing every slide by hand.

Canva is a general design app, not a LinkedIn tool. Its LinkedIn carousel template page listed more than 1,285 templates when we read it. You pick one, change the text and pictures on each page, and download the design as a PDF to upload to LinkedIn. Canva has a Free plan.

Canva does not write the slides. If you want the words first, write them with the free carousel content generator and paste them in.

11. Gamma

Best for: people who already make presentations with AI.

Gamma is an AI tool for presentations, documents, and websites, and it can make a deck from a prompt. Its Free plan creates up to 10 slides per prompt and exports to PDF, PPTX, and PNG. The Plus plan removes Gamma's branding, so a carousel made on the Free plan carries it. Custom branding is on the Pro plan.

Gamma is built for presentations, so check the page size before you export. A landscape slide is shown small on a phone.

12. Piktochart

Best for: teams that already make infographics in Piktochart.

Piktochart has an AI LinkedIn carousel generator. Its page says you type an idea, it makes several carousels to choose from, and you change the colors, fonts, and images. It has Free, Pro, and Enterprise plans; its pricing page would not load for us, so we have no prices.

Free LinkedIn carousel makers without a watermark

"Free" and "no watermark" do not always go together. What each tool's own pages say:

• Depost AI's carousel generator: free, no account, no watermark.
• Gamma: the Free plan carries Gamma's branding. The Plus plan removes it.
• PostNitro: watermark-free exports are listed on the Starter plan, $15 a month.
• Taplio: the example slide on its free carousel page reads "Made with taplio.com/carousel".
• Supergrow, ContentIn, and aiCarousels: their free carousel pages do not say either way.

How to make a LinkedIn carousel in Canva

1. Search Canva for a LinkedIn carousel template, or start a blank design at 1080 by 1080 or 1080 by 1350 pixels.
2. Write the slides before you design them: one idea per slide, and a cover that says what the reader gets.
3. Keep every page the same size. LinkedIn requires it.
4. Download the design as a PDF.
5. Drop the PDF into the carousel tester to check the size, the page count, and the smallest text.
6. On LinkedIn, start a post, add a document, and upload the PDF.

The guide on making a LinkedIn carousel post covers the structure, the caption, and the limits in more detail.

What to check before you post any carousel

• The text on a phone. A slide is about 360 pixels wide on a phone. Text that looks fine in an editor can be too small in the feed.
• One page size. LinkedIn requires every page in the PDF to be the same size, so do not mix square and portrait slides.
• The file's hidden details. A PDF carries the author name and the app that made it. The PDF metadata remover takes them out.
• The first slide. It decides whether anyone swipes. Write three versions of it and keep the clearest.

FAQ

What is the best LinkedIn carousel maker?

Depost AI. It writes the slides in your voice, designs them in its free carousel generator with your photo or logo and no watermark, and publishes the PDF after you approve it. For designing every slide by hand, Canva has the most templates.

Is there a free LinkedIn carousel maker?

Yes. Depost AI's carousel generator is free with no account and no watermark. Supergrow and ContentIn also have free carousel editors, and Canva and Gamma have free plans.

What is the best AI LinkedIn carousel generator?

Depost AI. It writes the slides in your voice from what you have already posted, designs them in its carousel generator, and publishes the PDF after you approve it. Taplio, Scripe, Contentdrips, and PostNitro also write and design slides from an idea, a post, or a link.

Is there a free LinkedIn carousel generator without a watermark?

Yes. Depost AI's free carousel generator adds no watermark and needs no account. Gamma's Free plan carries its branding, and PostNitro lists watermark-free exports on its paid Starter plan.

What size is a LinkedIn carousel?

LinkedIn publishes no pixel size for carousel pages. Square at 1080 by 1080 and portrait at 1080 by 1350 are the common shapes. What LinkedIn does require is that every page in the PDF is the same size, with the file up to 100 MB and 300 pages.

Can you make a LinkedIn carousel in Canva?

Yes. Pick a LinkedIn carousel template or a blank design, keep every page the same size, download it as a PDF, and upload the PDF to a LinkedIn post as a document.

How do I post a carousel on LinkedIn?

Start a post, add a document, and upload the PDF. LinkedIn turns each page into a slide. You can also schedule it from a tool that publishes documents, such as Depost AI.

How many slides should a LinkedIn carousel have?

LinkedIn allows up to 300 pages and recommends no number. Use as many slides as the idea needs, with one point on each. Our free carousel content generator writes eight.

Search for it and you are offered benchmarks. Aim for this many seconds. The average is that many. Posts above the line get reach. None of those numbers come from LinkedIn, and one of the most widely repeated quotes about them is not in the document everyone attributes it to.

LinkedIn has published a real definition, twice, in its own engineering writing. It names dwell as something its ranking model predicts. It also gives you no way to see it. This post is the record. What dwell time is in LinkedIn's own words. Every metric your analytics actually reports. Where the invented numbers come from. And the closest thing a writer can measure instead, tested on 12,736 posts.

What dwell time is, in LinkedIn's own words

The definition, from 2020. LinkedIn's engineering team published "Understanding feed dwell time" on 12 May 2020, written by Siddharth Dangi, Johnson Jia, Manas Somaiya and Ying Xuan. It describes two measures. The first is dwell time "on the feed, which starts measuring when at least half of a feed update is visible as a member scrolls through their feed". The second is dwell time "after the click, which is the time spent on content after clicking on an update in the feed".

So the clock starts at half the post on screen. Not at a click, not at a reaction. Scrolling slowly past your post is already being counted.

Why LinkedIn built it. The same post gives the reason in plain terms: "Click and viral actions can be rare, especially for passive consumers of the feed. While these members may still visit the feed frequently and find value in the updates they see, they may shy away from taking click and viral actions."

That is the whole argument. Reactions and comments only see the people who act. Most people do not act. LinkedIn wanted a signal for the rest.

LinkedIn's own comparison. The post sets the two kinds of signal side by side in a table. Against click and viral actions, dwell time is "Always measurable" rather than "Not always measurable", a "Real-valued measure of engagement" rather than a "Binary indicator", and where clicks are a "Noisy indicator of engagement", dwell "Can be a more reliable indicator of engagement". Clicks have "Positive signals are rather sparse"; dwell has "No shortage of signals".

Remember that last line. Every post you publish generates dwell data from everyone who scrolled past it. Almost none of them generate anything you can see.

The opposite of dwell is a skip. The 2020 work modelled skipping, treating "updates viewed for less than Tskip seconds as having been skipped by the member". LinkedIn never published what Tskip is. The gain it reports is a model accuracy figure, "consistently increasing the area under the ROC curve of the model by as much as 10%", which is about the prediction, not about anyone's reach.

Dwell is still in the ranking, and LinkedIn names it

LiRank, 2024. LinkedIn's ranking paper LiRank, presented at the KDD conference in August 2024, lists what the feed model predicts: "like, comment, share, vote, and long dwell and click for each member, candidate post pair. These predictions are linearly combined to generate the final post score."

The paper explains why that target was added: "We introduced a long dwell signal to detect passive content consumption on the LinkedIn Feed. Implementing this signal effectively, allows the capture of passive but positive engagement."

What "long dwell" means, and why no second count exists. LiRank defines it as "a long dwell binary classifier predicting whether there is more time spent on a post than a specific percentile (e.g., 90th percentile)". The threshold is not one number: "Specific percentiles are determined based on contextual features such as ranking position, content type, and platform."

LinkedIn's February 2026 ranking paper repeats the same shape: "Long Dwell: dwelling on a post longer than a specified threshold of time, depending on the post-type." Alongside it sits the other target, "Contribution: a like, comment, or share on a post".

The threshold is not even a fixed number inside LinkedIn. A second engineering post, published 1 October 2024 by Fengyu Zhang, Mohit Kothari and Birjodh Singh Tiwana, spells out how the bar is set. It describes "a binary classifier that predicts whether a user will allocate more time to a post compared to a specific percentage (x%) of its counterparts", with the percentile calculated "for each unique combination" of "content type, creator type, and distribution method". Then the line that settles the question: "This entire process is performed on a daily basis, ensuring that the percentiles remain dynamic rather than static."

The same post gives the clearest reason dwell exists at all: "Dwell time is crucial for evaluating the relevance and engagement of out-of-network content, where interactions are less common."

Read that twice. The bar moves with the kind of post, the kind of creator, how the post reached you, and what day it is. There is no number of seconds to aim for, because LinkedIn does not use one. Any page telling you to hold a reader for a particular count of seconds is inventing the rule it is quoting.

It is still there in 2026. LinkedIn's March 2026 write-up of the rebuilt feed, by Hristo Danchev, lists what the system learns from: "your actions (long dwells, likes, comments, shares) are embedded and interleaved with post representations". Long dwells come first in that list.

And LinkedIn tells members, in plain language. Its help page on how the feed decides what to show names the signals it uses. One of them is "Content you spend the most time viewing". Another is "What the post is about and whether it provides knowledge or advice".

Why it is not in your analytics

Here is everything LinkedIn gives a creator. The help page that defines post analytics names every metric you get:

• Who saw it: impressions, members reached, the in-network and out-of-network split.
• What they did: reactions, comments, reposts, saves, sends, visits to links in the post, premium button interactions.
• What it did for you: profile viewers from the post, followers gained from the post.
• Video only: video views, watch time, average watch time.
• Articles and newsletters: article views, email sends, email open rate.

Three of those are time measures, and all three sit under video. "Watch time: The cumulative length of time your video was viewed." "Average watch time: The average amount of time that your video was watched."

So the only clock you get is for video. LinkedIn's own developer documentation says the same thing in harder terms: the watch-time metric is "only available for posts that contain a video". Write a text post, an image post or a carousel and there is no duration number anywhere. Not in the app, not in the export, not in the API.

Newsletters do not get read time either. The newsletter analytics page lists article views, new subscribers, impressions, engagements and demographics. No read time, no completion rate.

Why LinkedIn keeps it. A visible dwell number would be a target, and LinkedIn has been through exactly that before. In February 2024 its editor in chief Dan Roth explained why the platform dropped another reading signal: "As soon as we realized what people were doing, and that we had incorrectly attributed the 'read more' button as a signal that people were getting some value out of a post, we just stopped using that as a signal."

The see-more click was a proxy for reading. People wrote posts designed to earn the click. LinkedIn stopped counting it. A published dwell score would go the same way within a month.

Where the numbers on the first page come from

Search for a dwell-time benchmark and the same pair of figures comes back: posts held for 61 seconds or more get a 15.6% engagement rate, posts dropped in under three seconds get 1.2%. It is a satisfying number. It is on page after page. We went looking for the study behind it.

There is no study. The figures come from a table on one page, published on 21 July 2025 on the blog of a digital marketing and web development agency, under the byline "Mainuser". The table has five rows, from "0-3 seconds" at "1.2%" up to "61+ seconds" at "15.6%", each with a verdict attached. Underneath it the page says "The data above paints a clear picture".

The page names no study, no dataset and no sample. It carries no outbound link to any other website at all. Its own domain no longer resolves, so the only way to read it now is in a web archive. Two paragraphs after the table it contradicts it, saying "a good dwell time on LinkedIn is generally considered to be around 2-5 seconds or more". A sister page on the same site, published the same morning, gives a third target of "at least 10-15 seconds for posts and 30-60 seconds for articles and videos".

The same page also states, flatly: "Yes, you can access your dwell time metrics in LinkedIn Analytics." That is not a feature LinkedIn has ever offered. It then explains that LinkedIn calculates dwell using "scroll depth, time spent reading, and engagement metrics like likes and comments", none of which appears in LinkedIn's definition.

Now watch the number travel. The same two figures, with five incompatible stories about where they came from:

Only the top and bottom rows of the original table survive the journey. The middle three, 3.5% and 6.1% and 10.2%, get dropped, which is how an unsourced table turns into a dramatic fifteen-fold gap.

One quote in that chain was manufactured. The vendor page that cites the origin correctly also runs a pull-quote: "Dwell time is a more reliable indicator of engagement", attributed to Siddharth Dangi, Senior Staff Software Engineer at LinkedIn. He is a real co-author of the 2020 post. He never wrote that sentence. The source is the comparison table quoted above, whose cell reads "Can be a more reliable indicator of engagement". A table cell was rewritten into a sentence, given a speaker and given a job title.

The real dwell numbers are about ads. There is one set of traceable figures, and they are not about your posts. AJ Wilcox of B2Linked, who runs LinkedIn advertising, published LinkedIn's ad benchmarks in July 2024. Newsfeed ads averaged about 2.68 seconds. The LinkedIn Audience Network averaged 17.5 seconds, connected TV 23.55, single image ads 4.76. Those are averages for paid placements, measured by a system advertisers can actually see. Strip the word "ads" out and they read like targets for your organic posts, which is roughly how this whole category of advice came to exist.

Some pages get it right, and deserve saying so. A handful of tool blogs state plainly that LinkedIn has never published the number, that the threshold is relative to post type and position, and that dwell appears nowhere in your analytics. They are correct, and they are outnumbered.

What you can measure instead

You cannot see how long anyone stayed. You can see what they did when they got to the end.

A reaction is one tap, often from the feed, often without opening the post. A comment is different. Somebody read enough to have an opinion and then typed it. So the share of a post's engagement that is comments, rather than reactions, is the closest thing a writer can compute to a reading signal.

It is a proxy and a weak one, and the limits are at the end of this post. But it is measurable, and it says something the engagement count does not.

Our library: 13,336 posts published between 9 July and 6 October 2026. For the ratio to mean anything a post needs at least ten reactions, and a writer needs at least six such posts. That leaves 12,736 posts by 358 creators. Each post's comments-per-100-reactions is divided by the same writer's own typical ratio. The same own-median method as our test of what works on LinkedIn.

Reach and reading are not the same axis

Split each writer's own posts into thirds by how many reactions they drew, then look at what the comments did.

Per creator, the widest third multiplies reactions by 2.82 against their quietest third, and comments by only 2.00. Reactions grew faster than comments for 80% of the 358 creators.

This is measured on the two counts separately, so it is not an artefact of dividing one by the other. When a post travels, it collects taps faster than it collects replies. The extra audience is a wider, cooler audience.

That is worth holding onto, because it means a post that reaches more people is not automatically a post that was read by more people. The two numbers come apart, and you can only see one of them.

A short post buys taps, a long post buys replies

Take the 147 creators who wrote both short posts and long ones. Their posts under 50 words drew a median of 339 reactions and 52 comments. Everything else they wrote drew 246 reactions and 59 comments.

Fewer people saw the long ones, or at least fewer tapped. More people wrote back.

By length band, against each writer's own comment share:

The very short post is the only one that clearly loses on this measure. Everything from 100 words up is level. Total engagement, meanwhile, is flat across every band, which is what our post on length found on the full set.

The format people talk under is not the format that travels

A carousel earns a third more comments per reaction than the same writer's typical post. Of the writers who used one, 76% beat their own comment share. It also earns less total engagement than their typical post. The single image is the exact mirror: most reach, fewest comments per reaction, and only 34% of its writers beat their own comment share with it.

If dwell time behaves the way LinkedIn describes, the carousel is the format that should earn it, because a swipe is a decision to stay. We cannot confirm that from outside. What we can say is that the carousel is the format people stop to argue with, and the single image is the one that travels.

The shapes, and one that surprised me

The same split by what the post was doing. The shapes with the highest comment share are the ones that ask for something or take a side. The question post scores 1.33 across 15 creators. The offer scores 1.14 across 44. The founder's lesson scores 1.11, and 88% of the 16 creators who wrote one beat their own share with it.

The shapes at the bottom are the ones that win everything else. Leadership thoughts score 0.91 on comment share and 1.18 on total engagement. The vulnerable moment: 0.94 and 1.18. The career before-and-after: 0.95 and 1.21.

The posts people respect are not the posts people reply to. Both are worth writing, for different reasons, and no single number in your analytics separates them.

On my own account

I can do one thing with my own posts that I cannot do with the library: put the response beside the audience, because LinkedIn tells me my impressions.

Across 49 posts with impressions recorded, from 16 July to 15 October 2026: 58,746 impressions, 1,901 reactions, 1,196 comments and 143 reposts.

That is 5.5 actions of any kind for every 100 impressions. Reaction, comment, repost, all of it together. About 94 people in every 100 saw the post and left nothing behind.

And the pattern from the library shows up here too, from the other direction. My widest post of the quarter reached 12,288 people and drew 279 actions, which is 2.4 per 100. A typical post of mine reached 623 people and drew 9.9 per 100. The further a post travelled, the smaller the share of its audience did anything.

My own comment share is far higher than the library's, because I have run posts that ask for a comment. That makes my absolute numbers useless for comparison. The trend inside my own account is the part that holds.

So what do you do about it

Stop looking for the dwell number. It is not in the product, it is not in the API, and the threshold is not a fixed quantity even inside LinkedIn. Any benchmark you are given for it was made up by the person giving it to you.

Use watch time if you post video. It is the one real attention metric LinkedIn hands a creator, and almost nobody opens it.

Write the first two lines for the person who has not decided yet. The clock starts at half the post on screen, which means the opening is being measured whether or not anybody clicks. This is the one piece of practical advice that follows directly from LinkedIn's own definition rather than from a vendor's guess.

Watch comments per reaction on your own account, over twenty posts or more. Not as a score. As a second axis. If a post reached fewer people but drew proportionally more replies, it did something the reach number cannot show you. If you sell to the people who read you, that is probably the post you wanted.

Do not chase it with tricks. The see-more click was a reading proxy once, people gamed it, and LinkedIn dropped it. There is no reason to expect a different ending this time.

What this test cannot tell you

A comment is still a trace. Everybody who read a post properly and then moved on is invisible in our test, exactly as they are in your analytics. That is precisely the gap dwell time was built to fill, and our proxy cannot fill it either. We are measuring the visible minority and inferring about the rest, which is the thing this post criticises other pages for doing. The difference is that we are telling you.

The library is not a cross-section of LinkedIn. These are creators whose posts perform, with a median of 294 reactions a post. A typical LinkedIn member's post does not look like this. The within-writer comparisons hold anyway, because each writer is compared with themselves, but the absolute numbers are not benchmarks.

Comment share can be bought. Any writer can raise it by asking for comments, as my own account shows. That is a reason to watch your own trend and not to compare your number against anybody else's.

Our shape labels are automatic. Post shapes and formats are classified by a model, not by hand, so individual posts are sometimes filed in the wrong place. The bands with few creators, like the question post at 15, move easily.

None of this measures dwell. It measures response. LinkedIn is the only party that can see how long you held someone, and the evidence is that it intends to keep it that way.

FAQ

What is dwell time on LinkedIn?

Dwell time is how long a member spends on a post. LinkedIn's engineering team defined it in 2020 as two measures: time on the feed, which "starts measuring when at least half of a feed update is visible as a member scrolls through their feed", and time after the click, which is "the time spent on content after clicking on an update in the feed".

Is dwell time a ranking factor on LinkedIn?

Yes, and LinkedIn names it. Its 2024 ranking paper LiRank lists the things the feed model predicts as "like, comment, share, vote, and long dwell and click", combined into a single post score. Its February 2026 ranking paper names "Long Dwell" as one of two headline prediction targets. LinkedIn's member-facing help page lists "Content you spend the most time viewing" among the signals that shape your feed.

Can I see dwell time in LinkedIn analytics?

No. No LinkedIn screen, export or API reports dwell time to a creator. The only time-based metrics a member can see are watch time and average watch time, and LinkedIn's developer documentation says those are "only available for posts that contain a video". Text, image and document posts have no duration metric at all.

What is a good dwell time on LinkedIn?

There is no published figure, and LinkedIn does not use a single one. Its papers define "long dwell" as time above a threshold that is set per context: "Specific percentiles are determined based on contextual features such as ranking position, content type, and platform." LinkedIn's 2024 engineering post adds that the percentiles are recalculated every day and "remain dynamic rather than static". Any specific number of seconds presented as a target did not come from LinkedIn.

Where does the "61 seconds, 15.6% engagement" figure come from?

From a table on a single agency blog post published in July 2025, under the byline "Mainuser", with no study, no sample and no outbound citation of any kind. The site's own domain no longer resolves. Pages that repeat the figure credit it to five different and incompatible sources, including one research report that does not appear to contain it. Treat it as invented.

How is LinkedIn dwell time calculated?

The feed measure starts counting once at least half the post is visible on screen as someone scrolls. A separate measure counts time spent after a click. For ranking, LinkedIn converts this into a yes-or-no prediction: is this post likely to hold this member longer than the threshold for its post type and screen position.

Does a longer post increase dwell time?

Nobody outside LinkedIn can test that directly. What we can measure is replies. Across 12,736 posts by 358 creators, a post under 50 words scored 0.85 of its own writer's typical comment share. Every band from 100 words up scored 1.01 to 1.03. The same writers' short posts drew more reactions, 339 against 246, and fewer comments, 52 against 59.

Do carousels get more dwell time than other posts?

We cannot see dwell, so we cannot confirm it. We can say that a carousel earns 1.35 times its writer's typical comment share, the highest of any format. Of the creators who used one, 76% beat their own share. It also earns less total engagement than that writer's typical post, 0.83. People stop to reply to carousels; they do not travel as far.

Can you increase your dwell time on purpose?

You can write openings worth staying for, and you can use formats people move through rather than glance at. What you should not do is build a trick around it. LinkedIn once used the see-more click as a reading signal, and its editor in chief said that when people started writing for the click, "we just stopped using that as a signal".

Why does LinkedIn hide dwell time from creators?

LinkedIn has not said. What it has said is that a public signal gets gamed, which is why the see-more click was dropped in 2024. A visible dwell score would become a target the same way, and the signal would stop measuring what it was built to measure.

What should I watch instead of dwell time?

Your own comments per reaction across twenty or more posts, as a second axis beside reach. Across our library the two move in opposite directions: from a writer's quietest third of posts to their widest, reactions multiply by 2.82 and comments by only 2.00. A post with less reach and proportionally more replies did something the reach number cannot show you.

So we checked. We took 13,104 LinkedIn posts published between 9 July and 6 October 2026 by 409 creators, drawn from a library of 144,913 posts, and scored every one against its own writer's typical post. Not against other creators. Then we grouped the posts by the kind of sentence they open with and asked one question of each kind: do the people who use it beat their own average with it?

Eleven kinds of first line had enough writers to judge. Eight win. Three do not, and two of the three are the ones most hook lists lead with: the question and the statistic. The formula every course teaches, pain then promise, breaks even.

Below are the eleven ranked, the three to stop using, and 50 formulas from the eight kinds that win. Every formula comes with the real post that proved it: the creator's name, the first line as published, the public counts, and how many times their own typical post it did. Each kind has a figure and, under it, the same formulas and examples as text. At the end, which opening line wins for each shape of post, and four steps for filling a formula with your own facts.

How this was measured

For every post, engagement (reactions, comments and reposts) divided by the same writer's median post over the three months, so 1.00 is that writer's typical post. A kind of opening line is scored as the median across the writers who used it at least twice and did something else at least twice, with the share of those writers who beat their own median beside it. A kind needed twelve or more writers to appear.

The kinds are the library's labels for the first sentence of a post: an order, a how-to line, a hot take, a plain claim, a number, a contrarian line, a transformation, pain then promise, a curiosity gap, a statistic, a question. The hook glossary has a line on each.

This is the same test behind the 30 post formats that keep winning, which scores the shape of a post together with its opening line, and behind the 14 rules tested against each creator's own posts. This page looks at the first line on its own.

The eleven kinds of first line, ranked

An order and a how-to line lead at 1.11, with 61% of 64 and 60% of 35 creators above their own average. A hot take is 1.06 on a small cell of 23 writers. The plain claim, used by 366 of the 409 creators, is 1.02, and so is a number first across 133. A contrarian line is 1.01. A transformation and pain then promise both sit at 1.00.

Then the line. A curiosity gap is 0.98 and 248 creators lean on it, the second most used opener in the library. A statistic is 0.96. A question is 0.93, and only 37% of the 116 creators who open with one beat their own typical post.

The three to stop using

A question. 0.93 across 116 creators, and 37% of them above their average. It is the weakest opener in the library on every shape it was measured on: 0.89 on a how-to, 0.91 on a mindset post. A question asks the reader to do the work before they have a reason to.

A statistic. 0.96 across 104 creators. A number that is not yours reads as a pitch. The same finding stated as a plain sentence, with the number second, scores 1.07 on a data post; opened with the statistic, 0.94.

A curiosity gap. 0.98 across 248 creators. It holds only when the payoff is real and immediate, a milestone (1.21) or a trend with a number in it (1.04). On a how-to it is 0.92, on a diary entry 0.96, on a tools post 0.95. Most of the time the loop it opens is not worth closing.

1. An order

1.11 against the writer's own typical post. 61% of the 64 creators who open this way beat their average with it. An order is the joint strongest opener in the library and the least used of the winners: 64 creators, against 366 for the plain claim. It reads as a decision already made, which is why it carries a mindset post to 1.21 and why it fails when the body does not back the order with a reason.

The 7 formulas, each with the post behind it:

1. Stop {what you say}. {Show it} instead. Johnny C. Taylor: "Stop telling people you're kind." 12,932 reactions, 536 comments, 19 times their typical post.
2. Stop {settling for any X}. {Hold out for the right one}. Shulin Lee: "Stop praying for just any job. Pray for the right one." 8,260 reactions, 440 comments, 11 times their typical post.
3. Don't let {the setback} define {the future}. Will McTighe: "Don’t let failure define your future." 8,224 reactions, 498 comments, 10 times their typical post.
4. Before you {judge / decide}, ask a better question: Dr. Christian Poensgen: "Before you judge yourself, ask a better question:" 2,436 reactions, 209 comments, 6.5 times their typical post.
5. Stop managing {the input}. Start managing {the outcome}. Elfried Samba: "Stop managing time. Start managing outcomes." 2,450 reactions, 96 comments, 4.0 times their typical post.
6. Stop {using the tool the lazy way}. Matt Village: "Stop asking ChatGPT random questions." 404 reactions, 762 comments, 3.6 times their typical post.
7. {Do the thing} like a {human / beginner / owner}. Daniel Murray: "Write like a human." 1,657 reactions, 63 comments, 4.8 times their typical post.

Where it fits. No shape in the formats test has this as its best opener; it is a general-purpose line rather than the one a shape is built around.

2. A how-to line

1.11 against the writer's own typical post. 60% of the 35 creators who open this way beat their average with it. The how-to line is the other opener at the top, and it belongs to one shape. A how-to post breaks even across the library (1.00); opened with a how-to line it scores 1.30. The first line promises exactly what the body delivers, so nobody feels tricked into a lesson.

The 5 formulas, each with the post behind it:

1. Learn how to {notice / do} more {good thing} in your {life / work}. Ben Meer: "Learn how to notice more good things in your life." 7,379 reactions, 472 comments, 9.0 times their typical post.
2. Here's how to do great {X} for any {company} in any {industry}. Dave Gerhardt: "Here's how to do great marketing for any company in any industry." 620 reactions, 185 comments, 2.3 times their typical post.
3. How to use {tool} to {outcome}: ({setup time}, runs while you sleep). Austin Belcak: "How to use Claude Cowork to find new jobs: (10 minute setup, runs while you sleep)." 515 reactions, 135 comments, 2.5 times their typical post.
4. How I overcame the objection "{the exact words}." Josh Braun: "How I overcame the objection "I have a sick kid."" 306 reactions, 95 comments, 2.0 times their typical post.
5. How to {design} your {dream outcome} in {year}. Matt Gray: "How to design your dream life in 2026." 1,612 reactions, 307 comments, 2.2 times their typical post.

Where it is the best opener. How-to (1.30, 67% of 18 creators).

3. A hot take

1.06 against the writer's own typical post. 61% of the 23 creators who open this way beat their average with it. The hot take is a personal view with a specific detail in it, a time, a habit, a car. It is the smallest cell in the test, 23 creators, so read 1.06 as a direction rather than a fact. The examples that win are stories with an opinion inside them, not opinions with a story bolted on.

The 4 formulas, each with the post behind it:

1. I had a boss who {specific habit}. Usually around {exact time}. Josh Braun: "I had a boss who loved sending Sunday emails. Usually around 5:47pm." 698 reactions, 139 comments, 4.2 times their typical post.
2. Advice I wish I received sooner: {one sentence}. Ben Meer: "Advice I wish I received sooner: If you feel stuck, start by investing in yourself." 2,790 reactions, 145 comments, 3.3 times their typical post.
3. Everyone in my {family / team} {does this ordinary thing}. Dominick Namis: "Everyone in my family drives either a Toyota or a Lexus." 1,581 reactions, 98 comments, 3.3 times their typical post.
4. Yesterday {something happened to me} and it highlighted {N} things: Harry Stebbings: "Yesterday I had a pretty painful operation and it highlighted two things to me:" 2,087 reactions, 287 comments, 5.3 times their typical post.

Where it fits. No shape in the formats test has this as its best opener; it is a general-purpose line rather than the one a shape is built around.

4. A plain claim

1.02 against the writer's own typical post. 56% of the 366 creators who open this way beat their average with it. The plain claim is average on its own and the best opener for eight of the twelve strongest formats in the library, because a flat statement of something true about you is what a personal shape needs. On a before-and-after it scores 1.56, on a vulnerable moment 1.44, on a milestone 1.32, on a leadership thought 1.22. What it needs is a claim a reader could disagree with.

The 8 formulas, each with the post behind it:

1. The most {successful} people I know never stop {doing X}. Johnny C. Taylor: "The most successful people I know never stop being students." 16,868 reactions, 638 comments, 24 times their typical post.
2. {People} don't {quit} because it's {hard}. They {quit} because it's {unfair}. Nathan Hirsch: "People don't quit because it's hard. They quit because it's unfair." 5,692 reactions, 461 comments, 18 times their typical post.
3. A {small act} can {measurable effect}. Daniel Pink: "A hug can lower your odds of catching a cold." 5,856 reactions, 465 comments, 16 times their typical post.
4. Thrilled to announce that {an ordinary thing, said as news}. Adriaan Dekker: "Thrilled to announce that I wrote this sentence without ChatGPT." 9,288 reactions, 186 comments, 16 times their typical post.
5. {N} years later, and it still feels like yesterday. Tim Tebow: "25 years later, and it still feels like yesterday." 22,401 reactions, 320 comments, 16 times their typical post.
6. It took me until {a late point} to learn how to {a basic thing}. Chenae Erkerd: "It took me until the 4th grade to learn how to read." 4,288 reactions, 494 comments, 14 times their typical post.
7. {Success} finds you faster when you help others find theirs. Dr. Dupe Burgess: "Success finds you faster when you help others find theirs." 4,405 reactions, 328 comments, 13 times their typical post.
8. Some personal news: today is my last day at {company}. Peter Walker: "Some personal news - today is somehow my last day at Carta." 3,617 reactions, 768 comments, 14 times their typical post.

Where it is the best opener. Before and after (1.56, 83% of 12 creators); Vulnerable moment (1.44, 72% of 29 creators); Personal milestone (1.32, 83% of 29 creators); Diary entry (1.28, 76% of 21 creators); Leadership thought (1.22, 82% of 33 creators); Hot take (1.15, 64% of 58 creators); Data-backed insight (1.07, 60% of 15 creators); Case study (1.04, 59% of 27 creators); Framework (1.04, 61% of 38 creators); Giveaway (1.03, 50% of 16 creators); Launch (0.98, 50% of 34 creators); Offer (0.90, 25% of 16 creators); News update (0.89, 41% of 17 creators); Event invite (0.69, 23% of 13 creators).

5. A number first

1.02 against the writer's own typical post. 52% of the 133 creators who open this way beat their average with it. A number first works when the number is one only you can make: 1084 connections, 20 years, 10,000 cold calls. On a tools-and-resources post it is the best opener (1.23). The same shape opened with a plain claim instead drops under average (0.99), which is the clearest single case of an opener deciding a post.

The 7 formulas, each with the post behind it:

1. The day I left {company}, I had {number} {thing}. Anna Bilan: "The day I left Deloitte, I had 1084 LinkedIn connections." 1,766 reactions, 239 comments, 9.3 times their typical post.
2. In my {N} years of {X}, I have {done the thing} {N} times. Ankur Warikoo: "In my 20 years of professional life, I have changed my career 5 times." 16,347 reactions, 810 comments, 7.9 times their typical post.
3. The #1 way to {outcome}: Richard Yu: "The #1 way to teach people to respect you:" 1,869 reactions, 129 comments, 10 times their typical post.
4. {N} reasons why I'm so skeptical of {the whole industry}: Rand Fishkin: "5 reasons why I'm so skeptical of the entire AI tracking industry:" 2,014 reactions, 388 comments, 6.4 times their typical post.
5. I've made over {N} {cold calls} in my career. Christian Krause: "I've made over 10,000 cold calls in my career." 1,028 reactions, 80 comments, 5.1 times their typical post.
6. I saved {N} of the most useful {tool} {commands} into one {cheat sheet}. Hamna Aslam Kahn: "I saved 99 of the most useful Claude commands into one cheat sheet." 1,567 reactions, 107 comments, 4.8 times their typical post.
7. {N} words that make {sellers} sound {needy}. Josh Braun: "6 words that make sellers sound needy." 849 reactions, 64 comments, 4.7 times their typical post.

Where it is the best opener. Tools and resources (1.23, 66% of 32 creators).

6. A contrarian line

1.01 against the writer's own typical post. 52% of the 145 creators who open this way beat their average with it. The contrarian line takes the view your field repeats and says the opposite, flatly. It names the fight and the body has to win it; the examples that win follow with evidence a reader can check. On a contrarian-opinion post it scores 1.21, and it is the opener that rescues a how-to (1.08) when the how-to line does not fit.

The 7 formulas, each with the post behind it:

1. Instead of {what everyone does tonight}, {do this}. Linas Beliūnas: "Instead of watching 2 hours of Netflix tonight, watch this Stanford lecture." 15,059 reactions, 267 comments, 14 times their typical post.
2. Bad {copywriting}: "{the dull version}." Chase Dimond: "Bad copywriting: “Our sunscreen is SPF 50.”" 3,551 reactions, 381 comments, 14 times their typical post.
3. You could spend {time} explaining {X}. Or you could show someone {this}. Josh Braun: "You could spend 20 minutes explaining why lifting weights matters. Or you could show someone these two bones." 1,686 reactions, 147 comments, 9.3 times their typical post.
4. Unpopular opinion: the better the {company}, the less I trust the {salesperson}. Sam Jacobs: "Unpopular opinion: The better the company, the less I trust the salesperson." 1,468 reactions, 292 comments, 7.7 times their typical post.
5. Your {AI strategy} is perfect. That's why it's worthless. Michael Lee: "Your AI strategy is perfect. That's why it's worthless." 2,356 reactions, 741 comments, 5.3 times their typical post.
6. You don't need {what everyone says you need} to {outcome}. Kevin Jurovich: "You don’t need warm intros to raise money." 865 reactions, 191 comments, 4.5 times their typical post.
7. {X} has been dying since {year}. Jake Ward: "SEO has been dying since 1997." 1,879 reactions, 398 comments, 3.8 times their typical post.

Where it is the best opener. Contrarian opinion (1.21, 79% of 14 creators).

7. A transformation

1.00 against the writer's own typical post. 50% of the 132 creators who open this way beat their average with it. A transformation is the before and after in two short sentences. It breaks even across every shape (1.00) and earns its place on the one shape it was made for, the before-and-after post, at 1.21 with 79% of 19 creators above their average. The formula fails when the before is softened; it works when the before is a plain fact.

The 6 formulas, each with the post behind it:

1. I grew up in {place}. {Modest start}. And now I'm {the unlikely place}. Nicole DeTommaso: "I grew up in NY. Went to university in NYC as a first-gen college student. And now I'm on a billboard in Times Square." 2,258 reactions, 138 comments, 12 times their typical post.
2. {Year}: I was rejected by {X}. {Year}: Working with {Y}. Anik Jain: "2020: I was rejected by Schbang. 2026: Working with Virat Kohli." 3,051 reactions, 80 comments, 6.1 times their typical post.
3. I used to be a huge {introvert}. Noemi Kis: "I used to be a huge introvert." 2,071 reactions, 309 comments, 5.8 times their typical post.
4. My {wife} sent {N} applications. Zero replies. {What changed}. {N} responses in {N} days. Saidul Islam: "My wife sent 85 applications. Zero replies. I uploaded her resume to Claude. 16 responses in 14 days." 1,280 reactions, 74 comments, 5.7 times their typical post.
5. {N} years ago, you were cool if you had {the old tool}. Maja Voje: "10 years ago, you were cool if you had a CRM and an email automation platform." 1,257 reactions, 256 comments, 5.6 times their typical post.
6. For {N} years I've taught {X} that {rule}. Turns out I had a {rule} problem of my own. Mark Ritson: "For twenty years I’ve taught marketers that positioning is everything. Turns out I had a positioning problem of my own" 1,038 reactions, 213 comments, 4.0 times their typical post.

Where it fits. No shape in the formats test has this as its best opener; it is a general-purpose line rather than the one a shape is built around.

8. Pain, then the promise

1.00 against the writer's own typical post. 50% of the 116 creators who open this way beat their average with it. Pain, then the promise, is the formula every course teaches, and across the library it breaks even. It earns its keep in front of real steps, 1.15 on a how-to, and on a mindset post it is the strongest opener of all at 1.32. The pain line has to be specific enough that the reader recognises their own week in it.

The 6 formulas, each with the post behind it:

1. Most people take {months} to {recover}. You can do it in {minutes}: Chase Dimond: "Most people take months to bounce back. You can do it in minutes:" 4,150 reactions, 304 comments, 17 times their typical post.
2. Be careful who you {promote}. One wrong {promotion} can cost you {your best people}. Johnny C. Taylor: "Be careful who you promote. One wrong promotion can cost you your best people." 6,657 reactions, 427 comments, 9.6 times their typical post.
3. Google your name. Now imagine {a client} doing it. Anisha Jain: "Google your name. Now imagine your ex doing it." 3,434 reactions, 327 comments, 8.8 times their typical post.
4. "{Title}" sounds great until you realise you're also {the entire department}. Elfried Samba: "“Head of Marketing” sounds great until you realise you’re also the entire marketing department" 4,814 reactions, 232 comments, 7.8 times their typical post.
5. Most {candidates} lose "{the moment}" in the first {30 seconds}. Here's how to {fix it}: Harvey Lee: "Most candidates lose "tell me about yourself" in the first 30 seconds. Here's how to answer it properly: ↓" 1,423 reactions, 98 comments, 5.5 times their typical post.
6. Everyone wants {AI} to take over {the boring part}. Almost nobody knows where to start. Hana Kobzová: "Everyone wants AI to take over the boring parts of Google Ads. Almost nobody knows where to start." 240 reactions, 1,025 comments, 3.8 times their typical post.

Where it is the best opener. Mindset post (1.32, 64% of 14 creators).

Which opening line wins for each shape

The same first line does different work on different shapes. For every shape with ten or more writers in the formats test, the opener that scored highest on it:

The pattern in the table is the pattern of this page. The plain claim wins the personal shapes. The how-to line wins the how-to. The number wins the list. An order and the pain line win the mindset post. Nothing wins with a question.

Fill a formula with your own facts

1. Pick the kind of line that wins for the shape you are writing. The table above. If you are writing a story, a milestone or a leadership thought, it is a plain claim. If you are writing a how-to, it is a how-to line. If you are writing a list, it is the number.

2. Put a fact of yours in the orange part. Every example on this page carries one: a grade, a year, a price, a count, a name, a time of day. If you do not have the number, write around it. A number that is not yours is the one thing that turns a hook into a pitch.

3. Make the first line stand on its own. Read it with nothing under it. If it only works once the body explains it, it is a curiosity gap, and those run under the writer's typical post.

4. End the way the shape ends. Not with a link: a link at the end scores 0.91 across 194 creators, the worst ending in the library. The link goes in the first comment.

Inside Depost AI

The free analysis in Depost AI runs this test for your niche at your audience size and shows how the best creators there open their posts, with the real post behind each line, beside your own best and weakest posts as multiples of your own typical post. Every idea it suggests starts from the formats and first lines that won in your niche and names the post that proved it, so the evidence is one click away rather than taken on trust.

One honest line: it does not yet read the opening lines of your own posts, so it cannot tell you which of these kinds wins for you specifically. It says so on the screen until that changes. What it can tell you is which win in your niche, and which shapes of post win for you.

The hook generator on this site writes first lines from a topic you give it, in the kinds above. It is a starting point, not a measurement.

What this cannot tell you

Engagement is not revenue. Correlation, not cause: the writers who open with an order may differ from the ones who open with a question in ways the test cannot see. One season, one platform, English only. The kinds are labels assigned by a classifier to the first sentence of a post, and a classifier is sometimes wrong. The hot take rests on 23 writers and should be read as a direction. The scores are medians, so one viral post cannot carry a kind, but a kind with 35 writers rests on 35 people's habits. The first lines are quoted as published, with emoji removed.

Save this page

Save this page as a PDF

The print view keeps the article and every figure and drops the rest of the site.

FAQ

What is a hook on LinkedIn?

The first sentence of a post, the only part most readers see before they decide whether to click "more". On this page a hook is one of eleven kinds of first line as labelled by the library's classifier: an order, a how-to line, a hot take, a plain claim, a number, a contrarian line, a transformation, pain then promise, a curiosity gap, a statistic, or a question.

What is the best LinkedIn hook in 2026?

Measured against each writer's own typical post, an order ("Stop doing X.") and a how-to line, both at 1.11. A plain claim is the best first line for the personal shapes of post, where it scores 1.56 on a before-and-after and 1.44 on a vulnerable moment. The best hook depends on the shape of the post under it.

Do questions work as LinkedIn hooks?

No. A question as the first line scores 0.93 across 116 creators, and only 37% of them beat their own typical post with it. On a how-to it is 0.89 and on a mindset post 0.91. It is the weakest opener measured.

Should a LinkedIn hook start with a statistic?

Not as the first line. A statistic first scores 0.96 across 104 creators. The same finding stated as a plain sentence, with the number second, scores 1.07 on a data post. A number that is not yours reads as a pitch.

Does the pain-then-promise formula work on LinkedIn?

It breaks even on its own, 1.00 across 116 creators. It works in front of something concrete: 1.15 on a how-to with real steps, and 1.32 on a mindset post, where it is the strongest opener of all.

How was this measured?

13,104 LinkedIn posts published between 9 July and 6 October 2026 by 409 creators with four or more posts in that window, drawn from a library of 144,913 posts. Each post's engagement (reactions, comments and reposts) was divided by the same writer's median post over the three months, so 1.00 is that writer's typical post. Each kind of first line is scored as the median across writers who used it at least twice and did something else at least twice, with the share who beat their own median beside it. A kind needed twelve or more writers to appear; the shape-and-opener figures come from the formats test on the same posts at a ten-writer bar.

So we measured. We took 13,104 LinkedIn posts published between 9 July and 6 October 2026 by 409 creators, drawn from a library of 144,913 posts, and scored every one against its own writer's typical post. Not against other creators. A post at 1.50 did half again as well as that writer's normal post; a post at 0.80 did a fifth worse. Then we grouped the posts by format, meaning the shape of the post plus the kind of first line it opens with, and asked one question of each format: do the people who use it beat their own average with it?

Forty-three formats had enough writers to judge. Thirty win. Thirteen do not. The how-to post, the most common shape on the platform, only ever breaks even. A link at the end is the worst ending there is. And the best format in the set, a before-and-after that opens with a plain claim, was used twice in three months by only 12 of the 409 creators.

Below are the 30, in three tiers, each with a real post that proves it, what the first line, the body and the ending are doing, and the fill-in template. Then the 13 to stop writing, the opening line that wins for each shape, which shapes win as an image and which lose as a carousel, and how to end.

How this was measured

For every post, engagement (reactions, comments and reposts) divided by the same writer's median post over the three months. On that scale 1.00 is that writer's typical post. Impressions are not in the test, because nobody but the author can see them.

A format is a shape plus an opening line. The shape is what the post is, a leadership thought, a how-to, a milestone. The opening line is the kind of first sentence, a plain claim, an order, a question. "A leadership thought, opened with a plain claim" is one format; "a how-to, opened with a question" is another. The library's classifier labels both for every post.

A format's score is the median across the writers who used it at least twice and did something else at least twice, so one post that went viral cannot carry a format. Beside each score is the share of those writers who did better with the format than their own median, which is the number that says whether a format works for most people or for a loud few. A format needed ten or more writers to appear; 43 did.

The same method, on an earlier cut of the library with a higher bar, produced the 14 rules tested against each creator's own posts. The figures here come from a larger library and a later window, so they differ slightly from that post. Where the two disagree on a number, this page is the newer one.

The three mistakes that kept showing up

Defaulting to the how-to post. 188 of the 409 creators wrote one, more than any other shape, and the shape scores 1.00. It breaks even: 48% of the people who write how-to posts beat their own typical post with them, and 52% do not. The first line decides which side you land on, which is why four how-to formats are in the 30 and four are in the 13.

Ending with a link. 194 creators do it, and the ending scores 0.91. Only 31% beat their typical post with a link at the end, so seven in ten do worse than their own normal post. It is the worst ending in the library and the second most used. The honest trade-off: a link in the first comment protects the post and earns fewer clicks.

Never writing the format that wins most. A before-and-after opened with a plain claim scores 1.56, and 83% of its writers beat their typical post with it. Twelve creators used it twice in three months. The formats everyone writes are average because everyone writes them; the one at the top is open because almost nobody does.

Tier one: 1.20 and above

Twelve formats. Eight of them open with a plain claim, which is the whole lesson of the tier: the shapes that win most are personal, and the line that opens them best is a flat statement.

1. Before and after, opened with a plain bold claim

Seen working. Chenae Erkerd: "It took me until the 4th grade to learn how to read." 4,288 reactions, 494 comments and 19 reposts, 14 times their typical post.

Why it works. The claim is the before, stated without softening, and the reader supplies the gap. Only 12 creators used it twice in three months, which is why it is still open.

What sinks it. Softening the before. "I didn't start with much" is a claim; "things were tough" is not.

Image or text. As a single image it scores 1.20 (70% of 47 creators). The ending. A comment ask holds here (1.21 across 12 creators). Ask for the reader's own before.

2. Vulnerable moment, opened with a plain bold claim

Seen working. Alex Lieberman: "13 years ago, I lost my dad." 1,846 reactions, 91 comments and 1 reposts, 9.4 times their typical post.

Why it works. A plain statement of something hard, with no warm-up, reads as true because nobody writes it as a hook. 72% of 29 creators beat their own average with it.

What sinks it. Ending with a lesson. The moment is the post.

Image or text. As a single image it scores 1.19 (78% of 50 creators). The ending. A comment ask is the strongest ending for this shape (1.28, and 83% beat their average).

3. Personal milestone, opened with a plain bold claim

Seen working. Peter Walker: "Some personal news - today is somehow my last day at Carta." 3,617 reactions, 768 comments and 5 reposts, 14 times their typical post.

Why it works. The milestone is the shape people who sell underrate. 83% of the 29 creators who opened one with a plain claim beat their own typical post.

What sinks it. Leaving out the number, or thanking nobody.

Image or text. As a single image it scores 1.26 (69% of 68 creators). The ending. End on the point. A follow line (1.05) is the best general ending; a link (0.91) the worst.

4. Mindset post, opened with the pain, then the promise

Seen working. Matthew Wohl: "This is your friendly reminder that the people ghosting you in the interview process are probably not good at their job." 3,573 reactions, 196 comments and 136 reposts, 19 times their typical post.

Why it works. The pain line earns the second line. On its own this opener is average (1.00); inside a mindset post it is one of the four best formats in the set.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

5. How-to, opened with a how-to line

Seen working. Ben Meer: "Learn how to notice more good things in your life." 7,379 reactions, 472 comments and 494 reposts, 9.0 times their typical post.

Why it works. A how-to post breaks even across the library (1.00). Opened with a how-to line it rises to 1.30, because the first line promises exactly what the body delivers.

What sinks it. Ending with a link (0.97) or opening with a question (0.89).

Image or text. As a single image it scores 1.04 (58% of 158 creators). As video it scores 1.01 (55% of 31 creators). As text only it scores 0.98 (48% of 21 creators). As a carousel it scores 0.79 (36% of 14 creators). The ending. End on a sign-up or a save line (1.07). A link at the end runs under average (0.97).

6. Diary entry, opened with a plain bold claim

Seen working. Harry Stebbings: "A few weeks ago, a company we invested in shut down." 2,970 reactions, 234 comments and 21 reposts, 7.1 times their typical post.

Why it works. A diary entry reads as a record, not a lesson, and the plain opener keeps it that way. 76% of 21 creators beat their average.

What sinks it. Turning it into advice halfway through.

Image or text. As a single image it scores 1.09 (68% of 59 creators). The ending. End on the day's point. A comment ask holds (1.05).

7. Tools and resources, opened with a number

Seen working. Hamna Aslam Kahn: "I saved 99 of the most useful Claude commands into one cheat sheet." 1,567 reactions, 107 comments and 109 reposts, 4.8 times their typical post.

Why it works. The number is the promise and the list keeps it. Open the same list with a claim instead and it drops under average (0.99).

What sinks it. A list the reader has to click to understand, and a link at the end (0.93).

Image or text. As a single image it scores 1.04 (61% of 87 creators). As video it scores 0.96 (41% of 44 creators). The ending. 'Save this' wins (1.23, 71%). A link at the end loses (0.93, 40%).

8. Leadership thought, opened with a plain bold claim

Seen working. Nathan Hirsch: "People don't quit because it's hard. They quit because it's unfair." 5,692 reactions, 461 comments and 455 reposts, 18 times their typical post.

Why it works. 82% of 33 creators beat their own average with it, and as a single image it is the strongest shape-and-format pair in the whole test (1.31).

What sinks it. Advice instead of a record. The posts that win describe a decision, not a principle.

Image or text. As a single image it scores 1.31 (86% of 37 creators). The ending. A comment ask: the strongest ending in the whole set (1.33, 77%).

9. Before and after, opened with a transformation

Seen working. Nicole DeTommaso: "I grew up in NY. Went to university in NYC as a first-gen college student. And now I'm on a billboard in Times Square." 2,258 reactions, 138 comments and 2 reposts, 12 times their typical post.

Why it works. The transformation opener is average everywhere else (1.00). On a before-and-after it fits the shape exactly, and 79% of 19 creators beat their average.

What sinks it. Softening the before. "I didn't start with much" is a claim; "things were tough" is not.

Image or text. As a single image it scores 1.20 (70% of 47 creators). The ending. A comment ask holds here (1.21 across 12 creators). Ask for the reader's own before.

10. Contrarian opinion, opened with a contrarian line

Seen working. Sam Jacobs: "Unpopular opinion: The better the company, the less I trust the salesperson." 1,468 reactions, 292 comments and 13 reposts, 7.7 times their typical post.

Why it works. The opener names the fight and the body has to win it. 79% of 14 creators beat their average; the same shape opened with a plain claim only reaches 1.05.

What sinks it. A contrarian line on an ordinary body. The opener writes a cheque the evidence has to cash.

Image or text. As a single image it scores 1.12 (62% of 26 creators). As text only it scores 1.11 (60% of 15 creators). The ending. End on the point, or ask who disagrees. Not a link (0.91 across every shape).

11. Mindset post, opened with an order

Seen working. Shulin Lee: "Stop praying for just any job. Pray for the right one." 8,260 reactions, 440 comments and 536 reposts, 11 times their typical post.

Why it works. An order reads as a decision already made. Across every shape the order is the joint strongest opener (1.11); on a mindset post it reaches 1.21.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

12. Personal milestone, opened with a curiosity gap

Seen working. Simran Kaur: "Well, I wasn’t expecting my move to be on the front page of the New Zealand Herald!" 1,888 reactions, 65 comments and 0 reposts, 3.5 times their typical post.

Why it works. The gap works here because the payoff is real and immediate. The same opener on a how-to runs under average (0.92).

What sinks it. Leaving out the number, or thanking nobody.

Image or text. As a single image it scores 1.26 (69% of 68 creators). The ending. End on the point. A follow line (1.05) is the best general ending; a link (0.91) the worst.

Tier two: 1.08 to 1.19

Five formats. Two of them are how-to posts rescued by their first line, and one is the most common winning format in the library.

13. Hot take, opened with a plain bold claim

Seen working. Adriaan Dekker: "Thrilled to announce that I wrote this sentence without ChatGPT." 9,288 reactions, 186 comments and 18 reposts, 16 times their typical post.

Why it works. The second most used winning pair in the set: 58 creators, 64% of them above their own average. Short, arguable, no setup.

What sinks it. Explaining too much. This format wins short.

Image or text. As text only it scores 1.20 (76% of 21 creators). As a single image it scores 1.18 (68% of 56 creators). The ending. Ask who disagrees (1.06, 67%). End there.

14. How-to, opened with the pain, then the promise

Seen working. Chase Dimond: "Most people take months to bounce back. You can do it in minutes:" 4,150 reactions, 304 comments and 518 reposts, 17 times their typical post.

Why it works. Pain-then-promise is the most taught opener and only average on its own. In front of real steps it earns 1.15 for 62% of 26 creators.

What sinks it. Ending with a link (0.97) or opening with a question (0.89).

Image or text. As a single image it scores 1.04 (58% of 158 creators). As video it scores 1.01 (55% of 31 creators). As text only it scores 0.98 (48% of 21 creators). As a carousel it scores 0.79 (36% of 14 creators). The ending. End on a sign-up or a save line (1.07). A link at the end runs under average (0.97).

15. How-to, opened with a contrarian line

Seen working. Josh Braun: "You could spend 20 minutes explaining why lifting weights matters. Or you could show someone these two bones." 1,686 reactions, 147 comments and 30 reposts, 9.3 times their typical post.

Why it works. The contrarian line gives a how-to the one thing it usually lacks, a reason to keep reading. 67% of 18 creators beat their average.

What sinks it. Ending with a link (0.97) or opening with a question (0.89).

Image or text. As a single image it scores 1.04 (58% of 158 creators). As video it scores 1.01 (55% of 31 creators). As text only it scores 0.98 (48% of 21 creators). As a carousel it scores 0.79 (36% of 14 creators). The ending. End on a sign-up or a save line (1.07). A link at the end runs under average (0.97).

16. Mindset post, opened with a plain bold claim

Seen working. Johnny C. Taylor: "The most successful people I know never stop being students." 16,868 reactions, 638 comments and 1,038 reposts, 24 times their typical post.

Why it works. The most common winning format in the library: 126 creators, 62% of them above their own average. Average when the claim is vague, strong when it is specific.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

17. Mindset post, opened with a transformation

Seen working. Noemi Kis: "I used to be a huge introvert." 2,071 reactions, 309 comments and 99 reposts, 5.8 times their typical post.

Why it works. Transformation is average on its own (1.00). On a mindset post it earns 1.08, because the before and after is the whole argument.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

Tier three: 1.00 to 1.07

Thirteen formats that clear the writer's typical post by a little. Worth writing when the material fits; not worth forcing.

18. Data-backed insight, opened with a plain bold claim

Seen working. Daniel Pink: "A hug can lower your odds of catching a cold." 5,856 reactions, 465 comments and 866 reposts, 16 times their typical post.

Why it works. A data post from someone who sells usually reads as a pitch (0.97 for the shape). Opened with the finding as a plain sentence, not a statistic, it rises to 1.07.

What sinks it. Opening with the statistic (0.94). State the finding as a sentence; the number comes second.

Image or text. As a single image it scores 1.01 (52% of 27 creators). The ending. Source in the first comment. A link at the end runs under average (0.94).

19. Tools and resources, opened with the pain, then the promise

Seen working. Chris Donnelly: "Mastering AI doesn't have to cost a penny. These are the best 12 free AI guides I could find:" 1,682 reactions, 430 comments and 255 reposts, 2.7 times their typical post.

Why it works. 72% of 18 creators beat their average with it. The pain line says why the list exists; the list does the rest.

What sinks it. A list the reader has to click to understand, and a link at the end (0.93).

Image or text. As a single image it scores 1.04 (61% of 87 creators). As video it scores 0.96 (41% of 44 creators). The ending. 'Save this' wins (1.23, 71%). A link at the end loses (0.93, 40%).

20. Contrarian opinion, opened with a plain bold claim

Seen working. Chris Cunningham: "VP of Marketing: Our homepage has 14 approvers and I'm cutting it to two." 1,127 reactions, 81 comments and 24 reposts, 7.2 times their typical post.

Why it works. A contrarian opinion stated as a plain fact, with the reasoning in the body. 55% of 11 creators beat their average; the explicit contrarian opener does better (1.21).

What sinks it. A contrarian line on an ordinary body. The opener writes a cheque the evidence has to cash.

Image or text. As a single image it scores 1.12 (62% of 26 creators). As text only it scores 1.11 (60% of 15 creators). The ending. End on the point, or ask who disagrees. Not a link (0.91 across every shape).

21. Framework, opened with a plain bold claim

Seen working. Nathan Hirsch: "Being the loudest voice won't make you a leader." 1,942 reactions, 202 comments and 176 reposts, 6.2 times their typical post.

Why it works. Frameworks break even across the library (0.99). Opened with a claim rather than a name, they rise to 1.04 for 61% of 38 creators.

What sinks it. Naming the framework before earning it.

Image or text. As a single image it scores 0.99 (49% of 65 creators). The ending. A comment ask holds (1.04). A link is neutral here (1.02), one of the few places it is.

22. Case study, opened with a plain bold claim

Seen working. Dominick Namis: "The best CEO in America retired 14 years ago." 4,782 reactions, 202 comments and 183 reposts, 9.6 times their typical post.

Why it works. Case studies run under average as a shape (0.95). A plain opener about the subject, not about you, is what lifts them: 59% of 27 creators.

What sinks it. Making yourself the subject. The ones that win are about someone else.

Image or text. As a single image it scores 0.97 (47% of 55 creators). The ending. End on the lesson. A comment ask breaks even (1.03); a link runs under (0.97).

23. Trend read, opened with a curiosity gap

Seen working. Linas Beliūnas: "Ben Affleck quietly built a 16-person AI company. Netflix recently paid $587 million for it, and his view of AI is almost the opposite of Silicon Valley’s" 7,790 reactions, 365 comments and 339 reposts, 7.4 times their typical post.

Why it works. The gap is earned when the payoff is a real number. 59% of 22 creators beat their average; the same shape opened with a claim only just clears the line (1.01).

What sinks it. Summarising the news instead of reading it.

Image or text. As a single image it scores 1.05 (56% of 89 creators). As text only it scores 1.04 (53% of 15 creators). As video it scores 0.98 (45% of 20 creators). The ending. A newsletter sign-up line is the best ending here (1.24, 79%). A link at the end: 0.97.

24. How-to, opened with a plain bold claim

Seen working. Todd Hopwood: "Your physical and mental health is your most important asset." 2,289 reactions, 78 comments and 135 reposts, 12 times their typical post.

Why it works. The most used format in the library: 127 creators. It wins for 56% of them, better than the how-to shape on its own (48%).

What sinks it. Ending with a link (0.97) or opening with a question (0.89).

Image or text. As a single image it scores 1.04 (58% of 158 creators). As video it scores 1.01 (55% of 31 creators). As text only it scores 0.98 (48% of 21 creators). As a carousel it scores 0.79 (36% of 14 creators). The ending. End on a sign-up or a save line (1.07). A link at the end runs under average (0.97).

25. Vulnerable moment, opened with a curiosity gap

Seen working. Mel Robbins: "I was 47 years old when I found out I had ADHD. And you want to know how I figured it out?" 6,075 reactions, 424 comments and 120 reposts, 3.2 times their typical post.

Why it works. The gap holds here because the moment is real. 65% of 17 creators beat their average; opened with a plain claim the same shape reaches 1.44.

What sinks it. Ending with a lesson. The moment is the post.

Image or text. As a single image it scores 1.19 (78% of 50 creators). The ending. A comment ask is the strongest ending for this shape (1.28, and 83% beat their average).

26. Mindset post, opened with a curiosity gap

Seen working. Jen Blandos: "Success has a strange pattern most people miss." 6,696 reactions, 714 comments and 587 reposts, 9.1 times their typical post.

Why it works. 54% of 41 creators above their own average. The weakest of the mindset openers; the order (1.21) and the pain line (1.32) both beat it.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

27. Giveaway, opened with a plain bold claim

Seen working. Michel Lieben: "Claude Design dropped a couple of months ago. It's still not as good as Claude Code for actual design work." 644 reactions, 2,444 comments and 10 reposts, 9.3 times their typical post.

Why it works. The giveaway is the one shape where a keyword comment ask (1.12) and a link in the post (1.09) both hold. Opened with a claim, half of 16 creators beat their average.

What sinks it. A file nobody can picture. Say what is inside, in counts.

Image or text. As a single image it scores 1.10 (63% of 30 creators). The ending. A keyword comment ask (1.12). The one shape where a link in the post still holds (1.09).

28. How-to, opened with a transformation

Seen working. Saidul Islam: "My wife sent 85 applications. Zero replies. I uploaded her resume to Claude. 16 responses in 14 days." 1,280 reactions, 74 comments and 162 reposts, 5.7 times their typical post.

Why it works. A how-to with a before and after in the first two lines. It breaks even for half of 18 creators; the how-to line (1.30) is the better opener for this shape.

What sinks it. Ending with a link (0.97) or opening with a question (0.89).

Image or text. As a single image it scores 1.04 (58% of 158 creators). As video it scores 1.01 (55% of 31 creators). As text only it scores 0.98 (48% of 21 creators). As a carousel it scores 0.79 (36% of 14 creators). The ending. End on a sign-up or a save line (1.07). A link at the end runs under average (0.97).

29. Trend read, opened with a plain bold claim

Seen working. Elfried Samba: "Hats off to OpenTable" 33,957 reactions, 461 comments and 311 reposts, 53 times their typical post.

Why it works. The most used trend format (87 creators) and just above the line (1.01). The curiosity gap (1.04) edges it on the same shape.

What sinks it. Summarising the news instead of reading it.

Image or text. As a single image it scores 1.05 (56% of 89 creators). As text only it scores 1.04 (53% of 15 creators). As video it scores 0.98 (45% of 20 creators). The ending. A newsletter sign-up line is the best ending here (1.24, 79%). A link at the end: 0.97.

30. Mindset post, opened with a contrarian line

Seen working. Daniel Hartweg: "Most people think employees leave because of compensation." 558 reactions, 173 comments and 81 reposts, 3.4 times their typical post.

Why it works. The last format above the line: 53% of 17 creators beat their average with it. The order (1.21) and the pain line (1.32) are safer openers for the same shape.

What sinks it. Being general. "Keep going" is the average post; "the first month looks ordinary" is the one that wins.

Image or text. As a single image it scores 1.09 (60% of 133 creators). As video it scores 0.99 (42% of 26 creators). As text only it scores 0.90 (41% of 17 creators). The ending. A follow line (1.15) or a share (1.10). A comment ask only breaks even here (1.01).

The 13 formats that do not win

Below the line, in order. The last column is what to write instead, taken from the 30 above.

Three of the 13 are the same shape, the how-to, with the three openers that fail it: a statistic, a curiosity gap, a question. Two more are the openers that fail everywhere: a question as the first line scores 0.93 across 116 creators, and a statistic 0.96 across 104. The event invite is the worst post anyone in the library published, at 0.69 with 23% of its writers above their average, and no opener changes that.

The opening line that wins for each shape

The same shape opens best with different lines. The table gives, for every shape with ten or more writers, the opener that scored highest on it.

Across every shape at once, the order is: a how-to line 1.11 (60% of 35 creators), an order 1.11 (61% of 64), a hot take 1.06, a number 1.02, a plain claim 1.02 (56% of 366 creators, the most used opener by far), a contrarian line 1.01, a transformation 1.00, the pain then the promise 1.00, a curiosity gap 0.98, a statistic 0.96 and a question 0.93 (37% of 116 creators).

The plain claim is average on its own and the best opener for eight of the top twelve formats. That is not a contradiction. The claim does nothing by itself; it does a great deal in front of a personal shape, because a flat statement of something true about you is the thing the feed has least of.

Image, video, text or carousel, by shape

Across every shape, a single image scores 1.02 (65% of 237 creators beat their average with one), video 0.89, text only 0.88 and a carousel 0.83. By shape it is less even.

A leadership thought as a single image is the strongest shape-and-format pair in the whole test: 1.31, and 86% of the 37 writers who posted one beat their own average. A milestone as an image is 1.26, a before-and-after 1.20, a vulnerable moment 1.19. The hot take is the one shape that does better as plain text (1.20, 76% of 21 writers) than as an image (1.18). The how-to as a carousel is 0.79, and only 36% of the 14 writers who tried it beat their average.

One caution from the earlier test: for Depost AI users measured against their own posts, the carousel reversed and became the best format. The accounts in this library are mostly large; if yours is not, the only way to know is to measure your own.

How a post ends

A follow ask is the best general ending (1.05, 60% of 72 writers), a comment ask breaks even (1.02 across 206 writers, the most used ending), a save and a share sit at 1.02 and 1.00, a sign-up line at 0.99, a DM ask at 0.98. The link is 0.91, and it is the only ending where most writers do worse than their own normal post.

The pairs matter more than the averages. A leadership thought that ends on a comment ask is 1.33 (77% of 13 writers). A vulnerable moment with a comment ask is 1.28 (83% of 12). A trend read that ends in a newsletter sign-up is 1.24 (79% of 14). A tools post that ends on "save this" is 1.23 (71% of 14). The giveaway is the one shape where a link in the post still holds (1.09), because the link is the point of the post.

How to write any of these from your own facts

Four steps, in order.

1. Pick the shape from your own history, not from this list. Take your last twenty posts, divide each one's engagement by your median, and look at which shapes cleared 1.00 for you. The library says a leadership thought wins; your twenty posts say whether it wins for you.

2. Open with the line that wins for that shape. The table above. A plain claim for the personal shapes, a how-to line for a how-to, the number for a list, an order or the pain line for a mindset post. Not a question, whatever the shape.

3. Fill the template with a fact you have. Every first line in the 30 above carries one: a grade, a year, a price, a count, a name. Where you do not have the number, write around it. A number that is not yours reads as a pitch and is the fastest way to lose the trust the shape earned.

4. End the way the shape ends. A comment ask on a leadership thought or a vulnerable moment, "save this" on a list, a follow line on a mindset post, nothing at all when the point is made. The link goes in the first comment.

Inside Depost AI

The four steps are what Depost AI does when you give it an idea. It runs the same test as this page for your niche at your audience size, and its ideas start from the formats that won there rather than from a blank prompt. Each idea names the format it rests on and the post that proved it, so you can check the evidence rather than trust the suggestion. Once three of your own posts sit behind a kind of post, it scores that kind against your own typical post and says so when it cannot tell yet. And it writes around a number it does not have rather than inventing one.

The analysis of your own posts is the part that makes the rest true, and it is free: connect LinkedIn, and Depost AI reads your history in about a minute and shows your best and weakest posts as multiples of your own typical post, the kinds that work for you and the kinds to stop. On a profile with nothing imported it is working from the general patterns on this page, the same ones any model could be told. The difference starts when your history is in.

What this cannot tell you

Engagement is not revenue. A format that lifts reactions and comments may or may not lift the thing you post for. Correlation, not cause: the writers who use the winning formats may differ from the ones who do not in ways the test cannot see. One season, one platform, English only. The scores are medians, so one viral post cannot carry a format, but a format with twelve writers rests on twelve people's habits. "Creator" means an account in the library with four or more posts in the window; most are large accounts, and the carousel result above is the reminder that large accounts and small ones can disagree. The first lines are quoted as published, with emoji removed.

Save this page

Save this page as a PDF

The print view keeps the article and every figure and drops the rest of the site.

FAQ

What is the best LinkedIn post format in 2026?

Measured against each writer's own typical post, a before-and-after opened with a plain claim: 1.56, with 83% of the writers who used it twice or more beating their own average. A vulnerable moment opened with a plain claim is 1.44 and a personal milestone opened with a plain claim is 1.32. All three are personal shapes with a flat first line.

Which LinkedIn post formats get the most engagement?

Thirty of 43 formats beat the writer's own typical post in this test. The top tier, at 1.20 and above, is twelve formats: before-and-after, vulnerable moment, personal milestone, mindset post, how-to, diary entry, tools-and-resources, leadership thought and contrarian opinion, each with the opening line that suits it. The common thread is a personal shape opened with a plain claim, an order, or the pain followed by the promise.

Do how-to posts still work on LinkedIn?

They break even. The how-to is the most common shape in the library, written by 188 of 409 creators, and it scores 1.00: 48% of its writers beat their own typical post with it. The first line decides the outcome. Opened with a how-to line it scores 1.30; opened with a question, 0.89.

Should a LinkedIn post end with a link?

Not in the post. A link at the end scores 0.91 across 194 creators, and only 31% of them beat their typical post with it, the worst of any ending. A follow ask (1.05), a comment ask (1.02) or no ask at all do better. The trade-off is real: a link in the first comment protects the post and earns fewer clicks. The giveaway is the one shape where a link in the post still holds (1.09).

Is a question a good first line for a LinkedIn post?

No. A question as the opening line scores 0.93 across 116 creators, and 37% of them beat their typical post with it. On a how-to it is 0.89 and on a mindset post 0.91. A plain claim, an order or a how-to line all do better on every shape measured.

How was this measured?

13,104 LinkedIn posts published between 9 July and 6 October 2026 by 409 creators with four or more posts in that window, drawn from a library of 144,913 posts. Each post's engagement (reactions, comments and reposts) was divided by the same writer's median post over the three months, so 1.00 is that writer's typical post. A format is a shape plus an opening-line type as labelled by the library's classifier. A format's score is the median across the writers who used it at least twice and did something else at least twice, with the share of those writers who beat their own median beside it. A format needed ten or more writers to appear.

Most pages on this question blur the two. They open with a "reach is down 50%" figure that nobody sources, and close with a shadowban nobody has ever confirmed. People on r/linkedin asked the question three times in the week this was written. Here is the answer in the order to check, with LinkedIn's own words at each step and the real sources for the platform-wide numbers.

The part that is not your fault

LinkedIn's own figures show more posts than time to read them. In its results for the quarter to March 2026, LinkedIn said "original posts increased 14% year-over-year." In the next quarter's results it said "time spent on content grew 10% year over year" and "knowledge-oriented posts increased 24%." Posts are growing faster than the attention available to read them. The arithmetic of that is fewer impressions per post, before anything about your writing changes.

The largest third-party study says the same thing, with a number. Richard van der Blom, whose team analyzed 1.3 million posts from 50,000 creators, said on the Creator Science podcast in June 2026: "reach is down 60% for active creators over the last two years." He added: "Engagement is down 20, 25%." Reach fell faster than engagement, which means the people who still see a post act on it more often. His report sells for €199 and we have not read it, so those are his spoken figures, not ours.

A monthly benchmark shows the slope. MagicPost, a scheduling tool, publishes a median every month from public posts. For September 2026, on 26,983 posts, the typical post got 572 impressions, "−6% in a month, −21% since October 2025." A vendor's sample, but a dated one, and it moves in the direction LinkedIn's own figures predict.

The "50% drop" you have read elsewhere is this same evidence, passed along. Where the pages that quote it give any source at all, it is van der Blom's 2025 report at second hand. The figures above are the ones with a date and a sample behind them.

What a normal number looks like

Before diagnosing a drop, know what you are comparing against. Three datasets published medians in 2026, and they disagree, which is itself the useful fact.

They differ because the samples differ. One is the users of a writing tool, one is accounts that connected an analytics tool, one is posts visible to the public. None is LinkedIn. All three say a post in the hundreds of impressions is a normal post in 2026, and a post over 1,000 is above most. AuthoredUp puts it precisely: 1,000 impressions "beat 56% of them," and "the typical post earns its impressions alongside just 11 reactions and 2 comments at the median."

Audience size moves the number more than anything else. MagicPost's September 2026 medians by follower count:

The 6,651-post set gives the same thing as a ratio: "the median post got impressions equal to 8% of the author's follower count." Two more numbers from these sets are worth keeping. The median post reached "46.5% as many unique people as it had impressions," so about half of any impression count is the same people seeing a post again. And impressions are counted every time a post is shown, which LinkedIn's own definition makes clear: "Number of times your post was shown on LinkedIn."

None of those is your benchmark. Your benchmark is your own median. Take your last twenty posts, find the middle value for impressions, and compare the last five against it. A drop is a run of posts under your own median, not a post under someone else's.

The checklist, in the order to look

Nine causes. The first six are ordinary and account for most drops. The next two are rarer and come with their own evidence. The last is the one everybody suspects and nobody has confirmed.

1. Your first line stopped earning the click

Scrolling past a post is a signal, not an absence of one. LinkedIn's engineering team, describing the feed it rebuilt in March 2026, said the ranking model trains on "passive tasks (click, skip, long-dwell) and active tasks (like, comment, share)." A weak opening line does not merely fail to earn "see more." It feeds the system a skip from everyone who scrolls by. How the LinkedIn algorithm works in 2026 has the detail.

Measured against each writer's own average, a plain bold claim was the only opening line that beat it, at 1.06. A question or a pain-then-promise opener earned 0.82. Both figures are from our test of 12,791 posts. If your recent posts open with a question, that is the first thing to change.

2. You changed subject

The feed decides who to test your post on, and it learns that from your history. LinkedIn's March 2026 announcement says the new system reads "what a post is actually about and how your professional interests evolve," and matches posts to readers by meaning rather than keywords. Change subject and your post goes to a colder test group, people who followed you for something else. Reach recovers when you go back to one subject and stay there for a few weeks.

3. You changed format

Format moves a post more than most writers expect. In the fair test, a single image lifted a post to 1.39 of the writer's typical result. It did so for 72% of the 222 creators who posted both with and without one. Text with no picture earned 0.69. On my own account over three months, image posts averaged 1,142 impressions and text-only posts 241. If you drifted from images to plain text, that alone explains a halving.

4. You put a link in the post

A link in the body costs reach, and nobody agrees on how much. In the fair test a post ending with a link earned 0.80 of the writer's typical post. On 27 Depost AI accounts measured against their own posts, the link cost 18%. The vendor studies on the first page of Google put the penalty anywhere from 7% to 68%. The one page that lines them up concludes they cannot be reconciled. LinkedIn has never confirmed a link penalty. What is consistent is the direction: a post whose job is to send people away earns fewer of the actions that carry it.

5. You posted on the wrong days

One account's numbers, offered as a pattern to check rather than a rule. On mine, the median post published Monday to Thursday earned 766 impressions. The median post published Friday to Sunday earned 390. Four of my five worst posts in the quarter went out on a Friday or a Sunday. If your drop coincides with a change of day, that is the cheapest test there is. The posting-time studies disagree with each other by up to twelve hours, so check your own.

6. The post's visibility setting

A post set to "Connections only" cannot reach beyond your first-degree connections. LinkedIn's help page on post visibility lists three settings: "Anyone," which is "visible to anyone on or off LinkedIn, including your extended network"; "Connections only," which is "visible to only 1st-degree connections of the post author"; and "Group." It is one click in the composer and easy to change by accident. Check the setting on the posts that dropped.

7. It is noise, and you are reading one post

Reach is noisy and one quiet post is not a signal. Four posts carried half of my own reach last quarter. Compare your median across a month against the month before, not your last post against your best. Since June 2026 LinkedIn also shows what share of a post's impressions came from outside your network, which its director of creator products described as "a percentage breakdown of who has seen your content, based on if they're already in your network or not." A post with a low out-of-network share was never shown beyond your followers. That is a post that lost the first test, not a post that was hidden.

8. Readers flagged it as AI-like

This one leaves a mark you can see. Since 30 July 2026 readers can flag a post with "Seems like AI slop." A flagged post, per Engadget's report of LinkedIn's words, gets "reduced algorithmic reach similar to users' flagging them as 'not interested,'" and the author is told privately in analytics. LinkedIn's Chief Product Officer said in August that content it classifies as AI slop was getting "around 40 percent fewer views" than a few weeks earlier, and that "no single piece of feedback is used to adjust how widely a post travels," per The Register. If the note is in your analytics, the cause is the writing, and what LinkedIn has said about AI-written posts covers what to do.

9. Enforcement, which comes with a notice

The only published mechanism that limits a post's visibility is policy enforcement, and LinkedIn says it tells you. Its help page How we enforce our Professional Community Policies reads: "Depending on the severity of the violation, we may limit the visibility of certain content, label it, or remove it entirely." And: "When we do this, we also generally send you a notice that your content violates our policies, how your content violates our policies, and the action we're taking." Appeals are offered "after each violation."

Account-level restrictions are listed too. The account restrictions page names four categories of violation: content, profile, identity and automated tools. For the last, a separate page says LinkedIn does "not allow the use of third-party software or browser extensions that scrape, modify the appearance of, or automate activity on LinkedIn's website," and that a restricted account is re-enabled once the software is removed. Engagement pods sit here as well: LinkedIn's VP of Product Management said in November 2025 that it is "flagging any artificially boosted content internally" and "limiting the reach of this content," which the pods post quotes in full.

So the test for this bucket is simple. Did you get a notice, a label, a removal, or a restriction screen? If not, you are not in it.

So, is it a shadowban?

LinkedIn has never confirmed a mechanism called a shadowban, and no help page uses the word. Search its help center and the term does not appear. What it has published is the list above: ranking that reads skips and meaning, reader signals such as "not interested" and the AI flag, and enforcement that comes with a notice.

The pages that rank for "LinkedIn shadowban checker" are written by companies that sell automation or tools to hide it. They have a commercial reason for you to believe that a reach drop is a ban that their product can detect or dodge. Several of them concede, lower down, that LinkedIn has never confirmed the thing they are offering to check for.

What a drop looks like from the inside is identical either way. A post whose first line earned skips and a post that was quietly suppressed would show the same low number. The difference is that LinkedIn has described the first in its own engineering writing and has described the second only as enforcement with a notice. The honest reading of a drop with no notice, no flag and no restriction is that you have been outranked, not banned.

One check that costs nothing. Open one of the dropped posts logged out, or ask someone outside your network to open its link. A post set to "Anyone" is, in LinkedIn's words, "visible to anyone on or off LinkedIn," including people who are not members. If it is there, it has not been hidden. It has been shown to fewer people than you hoped, which is what the nine steps above are for.

What to do this week

1. Find your own median. Twenty posts, impressions and reactions-plus-comments-plus-reposts, the middle value of each. That is your 1.00.
2. Mark the posts under it. Not the ones under 840 or 581; the ones under your number.
3. Walk the list above against those posts. The first line, the subject, the format, the link, the day, the setting. One of them usually changed at the same time the numbers did.
4. Change one thing for the next three posts and watch the out-of-network share. A post that travels again has a share above your recent normal.

Depost AI does the first two steps against your whole history. Connect LinkedIn and the Performance Analyzer scores every post you have published against your own typical post. It marks the ones that fell under it and shows what the winners shared: the shape, the opener, the format. The analysis is free and needs no card. It can only read what is there. On a profile with a dozen posts it has less to say than on one with a hundred.

FAQ

Why did my LinkedIn impressions suddenly drop?

Check six things, in order. A weaker first line, since scrolling past a post is a signal LinkedIn's ranking model trains on. A change of subject. A change of format, such as images to plain text. A link in the post body. A change of posting day. The post's visibility setting. If none changed, compare a month's median against the previous month's rather than one post against your best. Reach is also down across the platform: LinkedIn reports posts growing 14% in a year while time spent on content grew 10%.

How many impressions is good on LinkedIn in 2026?

The three public datasets put the median post at 572, 581 and about 840 impressions, depending on the sample. The median post has 11 reactions and 2 comments. By audience size, MagicPost's September 2026 medians run from 190 impressions for accounts under 1,000 followers to 10,272 above 100,000. Accounts with 10,000 to 25,000 followers sit at 1,414. A good number is one above your own median over your last twenty posts.

Does LinkedIn shadowban accounts?

LinkedIn has never confirmed or published a mechanism called a shadowban, and the term does not appear in its help center. What it publishes is ranking that reads skips and meaning, reader signals such as "not interested" and the "Seems like AI slop" flag, and policy enforcement that limits, labels or removes content, with a notice. A drop with no notice, no flag and no restriction is a ranking outcome.

How do I know if my LinkedIn post has reduced visibility?

Two places. Your post analytics, where LinkedIn adds a private note when readers flag a post as AI-like. The out-of-network share there shows whether a post travelled beyond your followers. And your notifications, because LinkedIn says that when it limits, labels or removes content for a policy violation it "generally" sends a notice explaining the action. A post set to "Anyone" that opens when you are logged out has not been hidden.

Is LinkedIn reach down in 2026 for everyone?

Yes, on every measure that has a source. LinkedIn's own results show original posts up 14% year on year and time spent on content up 10%. The largest third-party study, 1.3 million posts from 50,000 creators, puts reach down 60% for active creators over two years. A monthly public-post benchmark shows the median post down 21% between October 2025 and September 2026.

Do links reduce LinkedIn impressions?

In our test a post ending with a link earned 0.80 of the writer's typical post. On 27 Depost AI accounts a link cost 18%. Vendor studies put the penalty anywhere from 7% to 68% and cannot be reconciled. LinkedIn has never confirmed a link penalty. A link in the first comment protects the post but earns fewer clicks, so decide which the post is for.

Why do my connections not see my posts?

Most often because the feed is ranked by predicted interest, not by connection. A post that lost its first test is shown to few people, connected or not. Check that the post's visibility is "Anyone" rather than "Connections only," check the out-of-network share in post analytics, and check whether the post's first line, subject or format changed from the posts your connections did respond to.

How do I get my LinkedIn impressions back?

Find your own median over twenty posts and mark the posts that fell under it. Then find the step in the checklist that changed when they did. Change one thing for the next three posts. A real image on every post, a first line that is a claim rather than a question, the link moved to the first comment, or Monday to Thursday. Measure the out-of-network share on those three. Reach comes back post by post, not account by account.

They cannot all be right, and the reason they disagree is more useful than any of them. This post gives the limits in LinkedIn's own words and explains why the big datasets contradict each other. Then it measures length the fair way: 13,104 posts, each scored against its own writer's typical post.

The limits, in LinkedIn's words

A post can hold 3,000 characters. LinkedIn's help page on posting, updated days before this was written, says: "Posts can contain up to 3,000 characters." In the library this post is built on, the median post runs 5.9 characters a word, so the limit is about 500 words.

The feed shows roughly three lines before "see more". LinkedIn publishes no character count for the cut. AuthoredUp, a LinkedIn writing tool, measured it at "only the first ~210 characters on desktop (or ~140 on mobile)." Our own post preview draws the cut so you can see where your first line falls. Those 140 characters are the only part of a post most people read, which is why the first line matters more than anything below it.

LinkedIn's own advice is 200 to 300 words. Its marketing blog, in June 2026, told writers to "keep posts between 200 and 300 words." That is advice, not a measurement, and LinkedIn gave no data beside it. Keep it in mind for the end of this post. It turns out to be a reasonable place to land, for a different reason than LinkedIn gives.

Why the big datasets disagree

Three datasets published length findings in 2026, and the two largest contradict the two smallest. The contradiction is in the method.

MagicPost, 1,194,021 posts, compared across all posts. Its study, updated June 2026, sorted "1,194,021 LinkedIn posts published over the last 12 months" by word count and looked at median likes per band. Under 25 words: 19 likes. At 400 words and over: 47. In its words, "every band beats the one before it" with "no peak in the middle." It also names 200 to 400 words as the sweet spot, which is a different claim, and both are on the page.

AuthoredUp, 372,126 posts, compared across all posts. Its data, from September 2025 to February 2026, found that posts "between 1,301 and 2,500 characters generate 27% higher engagement than posts under 400 characters," with "the highest median engagement rate (2.61–2.67%), well above the 2.10% seen for posts under 400 characters." In words, that band is roughly 220 to 420.

Mylance, 895 posts, each against its own author. Its backtest of 895 posts across 37 creators scored every post against that author's median. "33% of posts under 400 characters landed at or above that author's median," the worst length by far. Above that, "53-59% beat rate for every post 400 characters and up." Once a post clears about 70 words, length stops mattering.

The method is the finding. When you compare all posts against each other, you are also comparing all writers against each other. Established writers with large audiences write longer posts, and their posts earn more for reasons that have nothing to do with word count. A ladder of "longer earns more" is partly a ladder of who writes long. Compare each writer with themselves and the ladder nearly disappears. Our library shows the same thing from the other side. Measured the usual way, its median engagement by band runs from 338 to 451 with no trend at all. Every writer in it is already established, so there is no ladder of writers to climb.

The fair test, by length

The same library and method as our test of what works on LinkedIn. 13,104 posts, published between 9 July and 6 October 2026 by 409 creators with four or more posts in the window. Each post's engagement, meaning reactions plus comments plus reposts, is divided by the same writer's median post. A band's score is the median across creators who wrote that length at least twice and wrote other lengths at least twice. "Beat" is the share of those creators for whom the band beat their own median.

Four things to read out of that table.

Nothing is far from 1.00. The whole range runs from 0.97 to 1.05. For comparison, in the same library a single image lifts a post to 1.39 and a webinar invite drops it to 0.60. Length is a small lever next to what the post is and what it opens with.

The weakest length is the medium-short post. Fifty to 149 words scores 0.97 to 0.98. Only 40 to 44% of the writers who use it beat their own typical post with it. That is a quarter of everything published. It is the post that is too long to be a one-liner and too short to say anything.

The one-liner is fine, and the long post is best. Under 50 words scores 1.02. Four hundred words and over scores 1.05, and 58% of the 111 writers who use it beat their own median with it. That is the highest share in the table.

Cut in two, longer edges shorter for most writers. Across the 295 creators who wrote both, posts of 200 words and over scored 1.02 and were the better band for 57% of them. Posts under 200 words scored 0.99 and were the better band for 38%. A real tilt, by a small margin.

Your best post can be any length

For creators with eight or more posts, where does their single best post sit? In the band they write most often.

The second column tracks the first. A writer's best post of the quarter lands where their posts land. The one-liner slightly over-delivers on best posts, which fits the one-liner's 1.02 above. No length owns the winners.

Sellers and everyone else

The library splits into 262 creators who sell something by their headline (founders, consultants, coaches, agencies) and 147 who do not. Length treats them a little differently.

For sellers the medium-short post is the one to stop. Fifty to 149 words scores 0.96, and only 40 to 42% beat their own typical post with it. A seller's post at that length tends to be an offer or a tip with nothing behind it, and readers treat it that way.

The long post pays non-sellers more. Four hundred words and over scores 1.16 for 68% of non-sellers, against 1.04 for sellers. Thirty-one creators is a small group, so hold that number loosely. The direction fits what we found before: a reader gives a long post from someone with nothing to sell more benefit of the doubt.

On my own account

One account is an anecdote, so here it is as one. Fifty posts from my own LinkedIn account over the three months to 12 October 2026, read through Depost AI's analytics. One post is left out because its metrics never synced. The median post is 211 words and 689 impressions.

The small bands prove nothing. Three posts and four posts cannot carry a rule, and the 300-and-over median rests on four values that run from 362 to 5,451.

The middle bands match the library. My 100-to-199-word posts ran at 0.86 of my median, and four of my five weakest posts of the quarter sit in that band. My 200-to-299-word posts ran at 1.02.

My best posts share nothing about length. The top three by impressions were 214, 242 and 138 words, in three different bands. All three were image posts with a giveaway or a milestone in them. What they had in common was the shape, not the count. Which is the whole argument of this post.

Why the medium-short post loses

This part is our reading, not a measurement, because the library cannot see clicks.

A post under 50 words sits entirely above the "see more" cut. Nobody has to click, so nobody is let down. A post of 200 words and over asks for the click and pays it back with something. A post of 50 to 149 words asks for the click and then ends, which is the one experience a reader punishes. If that reading is right, the fix is not a word count. It is the question of whether what is below the cut is worth the click.

So how long should it be

The honest answer is that length is not the lever, and four rules come out of the data anyway.

1. The first line decides the click. About 140 characters show on a phone. A plain claim a reader could disagree with is the only opener that beat the writer's own average. Spend your effort there, not on the word count.
2. If it needs more than a line, give it 150 words or more. The band that loses is 50 to 149. Either say it in a line or say it properly.
3. Do not stop at 100 words to "keep it short". That advice produces the one length that reliably underperforms. The long post, 400 words and over, is the best band in the table and the most under-used, at 7% of posts.
4. Write to the point and stop. The most common length, 200 to 299 words, scores 1.01, and LinkedIn's own 200-to-300-word advice lands there. It is a fine place to end up when the content fills it. It is not a target to pad toward.

The character counter shows the limit and the cut as you type. The 30 formats that keep winning show what to put inside the length, which is the part that moves the number.

What this test cannot tell you

Engagement is not reach. Impressions are visible only to the author, so the library scores reactions, comments and reposts. A length that earns more comments is not necessarily one that is shown to more people.

Correlation, not cause. A long post may do well because it is long, or because the writer who had 400 words to say also had something to say. The data cannot separate those. What it can say is that writers who write long do not pay for it, and writers who write medium-short do.

One season, one library. July to October 2026, and creators who already post well. The direction transfers better than the decimals. Your own median is the number that matters, and it is the one no benchmark can give you.

Depost AI runs this comparison on your own posts. Connect LinkedIn and the Performance Analyzer scores every post you have published against your own typical post. You can see whether length, shape or opener moved anything on your account. The analysis is free and needs no card.

FAQ

How long should a LinkedIn post be?

Long enough to say the thing, and no longer. Measured against each writer's own median across 13,104 posts, every length band scored between 0.97 and 1.05 of a typical post. The weakest band was 50 to 149 words, which only 40 to 44% of writers beat their own average with. The strongest was 400 words and over, at 1.05 for 58% of writers. LinkedIn's own advice is 200 to 300 words, which is the most common length and scores 1.01.

What is the LinkedIn post character limit?

3,000 characters. LinkedIn's help page says "Posts can contain up to 3,000 characters." At the library's median of 5.9 characters a word, that is about 500 words.

How much of a LinkedIn post shows before "see more"?

LinkedIn publishes no figure. AuthoredUp measured about 210 characters on desktop and about 140 on mobile, which is roughly three lines. The first line is the only part most readers see, which is why it matters more than the length.

Do longer LinkedIn posts get more engagement?

Across all posts compared with each other, yes. MagicPost's 1,194,021-post study found median likes rising from 19 under 25 words to 47 at 400 and over. Against each writer's own median, barely. Posts of 400 words and over scored 1.05, and posts of 200 and over scored 1.02 against 0.99 for shorter ones. Most of the across-the-board ladder is who writes long, not length itself.

Are short LinkedIn posts bad?

A one-liner is not. Posts under 50 words scored 1.02 against the writer's own median and were the better band for 51% of writers. The post that underperforms is the medium-short one, 50 to 149 words, at 0.97 to 0.98. Mylance's own-median test found the same break at 400 characters, about 70 words. 33% of posts under it beat the author's median, against 53 to 59% above it.

How long should a LinkedIn post be for a founder or consultant?

For the 262 sellers in the library, the medium-short post is the one to stop. Fifty to 149 words scored 0.96, and only 40 to 42% of sellers beat their own average with it. Every other length scored 1.00 to 1.04. A seller's one-liner and a seller's 400-word post both work; the tip with nothing behind it does not.

What does LinkedIn recommend for post length?

Its marketing blog said in June 2026 to "keep posts between 200 and 300 words." LinkedIn gave no data beside the advice. In the fair test that band scores 1.01. That makes it a reasonable place to land when the content fills it, not a target to pad toward.

How was this measured?

13,104 LinkedIn posts from Depost AI's post library, published between 9 July and 6 October 2026 by 409 creators with four or more posts in that window. Each post's engagement (reactions, comments and reposts) was divided by the same writer's median post in the window. A length band's score is the median across creators who wrote that length at least twice and other lengths at least twice; "beat" is the share of those creators for whom the band beat their own median. Sellers were identified by their headline.

The first page of Google answers with two datasets that contradict each other and a penalty that nobody sources. Buffer says each step up in frequency pays. Hootsuite says once or twice a day. Somewhere in between, a "daily posting penalty" of 45% or 26% circulates with no study behind it. This post gives LinkedIn's own words and the one large study that compared accounts with themselves. Then it runs a new test on 13,104 posts, each scored against its own writer. It ends with a cadence for a founder who writes their own posts, and with the thing I got wrong about this a week ago.

What LinkedIn itself says

Consistency, in LinkedIn's words. In an in-app post reported by Social Media Today in July 2026, LinkedIn wrote: "Consistency is one of the biggest drivers of reach on LinkedIn. Posting regularly signals to the algorithm that you're an active, valuable contributor." The same guidance carried a checklist recommending "two to three posts per week."

Two to three a week, twice over. LinkedIn's marketing blog, in June 2026, told writers to "publish consistently at least two to three times per week." Davang Shah, its VP of Marketing, added the timing that matters within a week: "Engagement signals in the first 24 hours influence how widely a post is distributed."

No frequency penalty, reportedly. LinkedIn has published nothing that says posting often is penalized. A creator who put the question to LinkedIn staff in March 2026 reported their answer: each post is judged on its own, however many you publish. That is a paraphrase of a conversation, not a LinkedIn statement, and it is the closest thing on record.

So LinkedIn's position is three things: be consistent, two or three a week is enough, and be there in the first day.

The one large study that compared accounts with themselves

Buffer, 2 million posts, within the same accounts. Buffer's study of more than 2 million LinkedIn posts from 94,000+ accounts, published in August 2025, did the thing most frequency studies skip. It compared "each account's high- and low-frequency weeks against its own average," so a big account's busy weeks were measured against that account's quiet weeks, not against a small account.

The result. Moving from one post a week to two to five brought "+1,182 more impressions per post" and "+0.23 percentage points in engagement rate." Six to ten a week: "+5,001 more impressions per post." Eleven or more: "nearly 17,000 more impressions per post, 3x more engagements, and a 1.4 percentage point jump in engagement rate." Buffer's conclusion, in its words: "posting more often helps your performance on LinkedIn. Each step up in frequency delivers better per-post results."

Two things to hold beside it. Buffer sells scheduling, so it gains when people post more. And a within-account design controls for the account but not for why a week was busy. An account's heavy weeks may also be its weeks of more effort, more commenting and more to say. The direction is still the best evidence on the first page, and it is the opposite of a penalty.

Where the other advice comes from

"Once or twice a day" is advice for company pages. Hootsuite's guide, updated April 2025, says "Post 1-2 times per day on LinkedIn." Read on and the context is a business account: Hootsuite's own page posts "2-3 times per day," businesses average 5.5 posts a week, and the supporting figure is a LinkedIn Pages claim about weekly posting. None of that is about a person writing their own posts.

The daily penalty has no source. The figures that circulate are a 45% or a 26% reach cut for daily posting. They are attributed to a paid report at second hand, they disagree with each other, and they contradict Buffer's within-account result. We could not trace either to a study. Treat them as a rumor until someone publishes a method.

The sharpest version comes from the author of the largest reach study. Richard van der Blom, whose team analyzes over a million posts a year, said in June 2026 that for most creators "structuring two to three posts a week would be enough." He posts six times a week himself. His warning was about what fills the extra days: people "publishing memes, quotes to get to a frequency of daily. And that is not very good." The penalty, if there is one, is for filler, not for frequency.

The fair test, three ways

Our library: 13,104 posts published between 9 July and 6 October 2026, thirteen weeks, by 409 creators with four or more posts in the window. Each post's engagement, meaning reactions plus comments plus reposts, is divided by the same writer's median post. 1.00 is that writer's typical post. The same method as our test of what works on LinkedIn.

Per post: busy weeks and quiet weeks

Take each post and ask how many posts its writer published that same week. Then score the post against the writer's own median.

The single post in a quiet week does a little better, at 1.04. That fits: a writer who posts once that week posted the one thing they had. From two to six a week, nothing moves. A post in a four-post week is the same post as one in a two-post week. At daily or more, the median barely dips, but the share of writers it works for drops to 36%. For most people who post seven or more times a week, those weeks are below their own normal per post. Van der Blom's filler, measured.

The week's total

Per post is the wrong unit for a writer deciding how often to post. The week is the unit. Take the 166 creators who had both one-post weeks and three-or-more-post weeks. Here is what each kind of week earned in total, in units of that writer's typical post.

A one-post week earned 1.07 typical posts. A two-post week earned 2.18. A week of three or more earned 4.11. The busy week beat the quiet week for 99% of the 166 creators. There is no point at which the extra post costs the others. Posting more earns more, almost one for one, up to the point where the posts stop having anything in them.

Across creators, and what I got wrong

Now compare creators with each other, the way most studies do.

Read across and two stories appear. Engagement per post rises with cadence, from 252 to 590. Engagement per thousand followers falls with it, from 5.00 to 1.44. The last column explains both: the creators who post most have the biggest audiences, and big audiences earn more per post and less per follower. Cadence is tangled up with size. That is why the within-writer test above is the one to trust.

A week ago I read this wrong. In our seller analysis on 9 October I wrote that the best-performing sellers post about three times a week. The top quarter of sellers by engagement per follower posted 2.8 times a week, and the bottom quarter 4.1, so that looked like the finding. Re-run today with audience size in view, it is not. The top quarter by that measure has a median of about 36,000 followers and posts 0.8 times a week. The bottom quarter has about 240,000 followers and posts 2.5 times a week. Small accounts earn more per follower and post less. That is a fact about size, not about cadence, and the sentence has come out of our guidance.

Does consistency matter?

LinkedIn says it is "one of the biggest drivers of reach." Our library cannot test reach, because impressions are visible only to each author. It can test engagement, and the engagement view is inconclusive for the same reason as the table above.

The 197 creators who posted in eleven or more of the thirteen weeks earned a median 391 engagement per post. The 128 who posted in six weeks or fewer earned 271. But the consistent group also has the larger audiences, and per thousand followers the order reverses: 2.87 against 4.81. Our data cannot separate consistency from size. Buffer's within-account result is the best evidence on the question: more weeks with more posts earned more impressions per post. It points the way LinkedIn says.

Which days

The one scheduling choice the library can settle. Each post against its writer's median, by the day it went out.

Monday to Thursday, a post beats its writer's median for 54 to 58% of writers. Friday to Sunday, for 41 to 46%. The difference is small per post, two to eight points, and it is consistent across 300 creators a day. If you post three or four times a week, the days are Monday to Thursday. The posting-time studies disagree with each other on the hour, so test your own.

On my own account

One account, offered as one account. Fifty posts over the thirteen weeks to 12 October 2026, through Depost AI's analytics, with one post left out because its metrics never synced. My median post earned 688 impressions, and I posted three times in a typical week.

Three and four posts a week were my best weeks per post. Five or more were my worst, at 0.86 on four such weeks. Small numbers, and the weeks with five or more are the weeks I was pushing posts out to keep a schedule. By day, my Monday-to-Thursday posts earned a median 777 impressions and my Friday-to-Sunday posts 568, which is the library's pattern on one account. My three Sunday posts earned a median of 91.

So how often should you post?

For a founder, consultant or marketer who writes their own posts, four rules come out of all of this.

1. Count is a total-engagement lever, not a per-post one. A post in a busy week scores the same as a post in a quiet week, and the week earns almost one for one. If you can write three posts with something in them, three posts earn three posts' worth.
2. The ceiling is specifics, not days. The only cadence that scored below the writer's own median for most people was seven or more a week. Van der Blom's memes and quotes, Buffer's unexplained busy weeks and LinkedIn's "Seems like AI slop" button all describe the same failure: a post published to fill a slot. What LinkedIn has said about AI-written posts is the same rule from the platform's side.
3. Monday to Thursday. Three or four posts a week fit the four days that beat the writer's median. Leave the weekends unless your own numbers say otherwise.
4. Be there for the first day. LinkedIn's own VP says the first 24 hours of engagement shape distribution. A post you can answer comments on at 3pm beats a post that goes out at midnight to hit a quota.

Three or four a week, then, Monday to Thursday, and each one with a number, a decision or an opinion in it. The frequency is the easy part.

Depost AI is built for that week. It plans from what has beaten your own average and drafts each post in your voice. It schedules each one once you approve, at the times your own audience reads. The Performance Analyzer shows your own busy-week and quiet-week numbers. The cadence you settle on is measured on your account, not borrowed from this page.

What this cannot tell you

Engagement is not reach. The library scores reactions, comments and reposts. Buffer measured impressions. The two agree on direction, and neither can see what the other sees.

Busy weeks may be effort weeks. A within-writer test controls for the writer but not for why a week was busy. Weeks with more posts may be weeks with more replies, more commenting and more to say.

Correlation, not cause, and one season. July to October 2026, creators who already post well. The direction transfers better than the decimals. The number to beat is your own median, over your own weeks.

FAQ

How often should you post on LinkedIn?

Two to three times a week is LinkedIn's own advice. Three or four, Monday to Thursday, fits the data for someone who writes their own posts. Across 13,104 posts scored against each writer's median, a post in a busy week scored the same as a post in a quiet week. The week's total engagement rose almost one for one with the number of posts. The ceiling is how many posts you can fill with something specific.

Is posting every day on LinkedIn bad?

Not per post, by the median: a post in a week of seven or more scored 0.99 of the writer's typical post. But only 36% of the writers who posted that often beat their own median in those weeks. At two to six a week, about 50% did. Daily works for the few who have a post's worth of material every day and costs the rest. The "daily penalty" figures quoted online have no published source.

Is there a LinkedIn posting frequency penalty?

LinkedIn has published none, and its staff have reportedly told creators that each post is judged on its own. Buffer's within-account study of 2 million posts found each step up in frequency improved impressions per post, with no penalty threshold. Our own test found a post in a busy week scores the same as one in a quiet week. What is penalized is filler, which reads the same at any cadence.

What does LinkedIn recommend for posting frequency?

"Two to three posts per week," in a July 2026 checklist, and "publish consistently at least two to three times per week," on its marketing blog in June 2026. It also said in July 2026 that "consistency is one of the biggest drivers of reach on LinkedIn."

Should you post on LinkedIn at the weekend?

Measured against each writer's own median, Saturday posts scored 0.96 and Sunday posts 0.97, beating the writer's median for 41% and 46% of writers. Monday to Thursday scored 1.02 to 1.04 and worked for 54 to 58%. Only 18% of posts in the library went out at the weekend. Unless your own numbers say otherwise, use the four weekdays.

Is it better to post once a week with quality or every day?

The single post in a one-post week scored 1.04, the best per-post figure in the test. A one-post week earned 1.07 typical posts in total. A three-or-more-post week earned 4.11. Quality per post is not what falls when you post more; the week's total is what rises. Post as often as you can write something with a number, a decision or an opinion in it, and stop there.

How often should a founder post on LinkedIn?

Three or four times a week, Monday to Thursday, each post with something only the founder could say. Our earlier reading that the best-performing sellers post about three times a week turned out to be audience size rather than cadence. The honest basis is the within-writer result. The week earns what you put in it, and the limit is material, not days.

Does consistency matter on LinkedIn?

LinkedIn says it is one of the biggest drivers of reach. Our library cannot test reach, and its engagement view cannot separate consistent posters from large accounts. Buffer's within-account study is the best evidence on the question: more weeks with more posts earned more impressions per post. It points the way LinkedIn says.

This is a tour of every number LinkedIn gives a personal profile, in LinkedIn's own words from its help pages, read on 14 October 2026. Then the gaps, and how to fill the one that matters.

Where the numbers live

A personal profile has three places to look. Most people only ever open the first.

Post analytics, under each post. Click the impressions count at the lower left of your post, or "View analytics" at the lower right. LinkedIn's help page adds a line worth knowing: "Only you can view your post analytics. Your own views, social engagements, link engagements, saves, and sends are counted towards the analytics of your content." Your own clicks are in your numbers.

Analytics and tools, on your profile. LinkedIn describes it as "a central place to view your performance on LinkedIn and access available creation tools." It holds creator analytics (combined post analytics and audience analytics) and profile insights (profile viewers and profile appearances). Newsletter authors also get newsletter analytics here. On desktop: the Me icon, View Profile, then "Show all" in the Analytics section.

Your data download, in Settings. Not an analytics screen, but the only copy of your history that does not expire. More on that below.

Post analytics, line by line

These definitions are quoted from LinkedIn's post analytics help page. LinkedIn last updated it about two months before this was written.

Four things in that table are worth more than they look.

Members reached is the honest reach number. Impressions count displays. One person scrolling past your post three times is three impressions and one member reached. The glossary entry on impressions versus reach has the distinction. The short version: impressions flatter, reach does not.

The in-network and out-of-network split is new. LinkedIn began rolling it out in June 2026. Sam Corrao Clanon, its director of creator products, described it to Social Media Today: "you'll begin to see a percentage breakdown of who has seen your content, based on if they're already in your network or not." A post with a high out-of-network share was carried by the feed's recommendations, reshares or search, not by the people who already follow you. That is the number that tells you whether a post travelled.

Saves and sends are shown. People repeat that LinkedIn hides saves. It does not. They are on the post analytics page, and so are sends, which is a private share by message. Both are costly actions for a reader and worth reading.

Profile viewers and followers from a post are the business numbers. A post that earned 40 reactions and 2 new followers did one job. A post that earned 15 reactions and 30 profile views did a different one. If you post to sell, the second column is the one to watch. Since 2025 LinkedIn sends a notification when a post drives either.

How long each number is kept

This is the part nobody reads until a number has gone.

From the same help page: "For all other content types, discovery and social engagement counts are available for 1,000 days. Members reached data is available for 400 days. Demographic breakdowns are available for 180 days." Articles and newsletters keep their analytics for two years. Video is the one LinkedIn's own pages disagree on. One page says video performance is available for 365 days and another says 180, so assume the shorter.

The practical consequence: the "who saw this" breakdown on a post from seven months ago is already gone. The reach figure on a post from last year goes next. If you want your history, export it while it exists.

Creator analytics: the trend over time

Combined post analytics sits under Analytics and tools. LinkedIn says it shows "how your content (including short form posts, images, videos, events, polls, and articles) performs over time." You pick a range "from the past seven days up to the past 365 days." A toggle decides whether the trend graph shows impressions or engagements.

LinkedIn defines the two for this screen as: "Impressions: The number of times your post was displayed on screen. This number is an estimate and may not be precise." And: "Engagements: The total number of engagements on your posts, including reactions, comments, saves, sends, and reposts." Note what that list includes. Saves and sends count as engagements here, and clicks do not. That matters for the rate question below.

Audience analytics sits beside it. LinkedIn describes it as "insights into your follower growth trends and follower demographics," over the same 7-to-365-day ranges. You can switch the breakdown between job title, location, industry, seniority, company size and company, and "the top five results will be displayed." LinkedIn adds: "All numbers displayed are estimates and may not be precise."

Both screens have an Export button at the upper right that produces an .XLSX file. That spreadsheet is the cheapest analytics tool you will ever own.

Profile insights: viewers and searches

Profile viewers depend on your plan. LinkedIn's comparison gives a basic account the "three most recent viewers in the last 90 days." A Premium account gets "up to 365 days of viewer data," the "full list of viewers," and a "weekly viewer trend graph."

Search appearances are a separate panel. LinkedIn says it "displays statistics on how many people found you through a search on LinkedIn." It includes "member insights about those who searched for you, such as their companies and job titles," and a list of the "job titles you were found for." It updates "weekly on Wednesdays."

Do not confuse appearances with views. In LinkedIn's words: "Profile appearances refer to the instances where your name and headline appear across LinkedIn. Profile Views show how many people have visited your profile."

The engagement rate LinkedIn never gives you

Here is the first gap. LinkedIn publishes a definition of engagement rate for company Pages, and none for personal profiles.

The Pages definition counts clicks. It reads: "the ratio of interactions per impressions on your post. Interactions include clicks, reactions, comments, and shares." Clicks are the important word. On a Page, a click to expand "see more" or a click on the company name counts. That makes LinkedIn's own figure the highest of the three formulas in circulation.

A personal profile gets no rate at all. It gets the counts above, and a creator-analytics "engagements" total that includes saves and sends but not clicks. So every rate you have ever seen quoted for a personal profile was computed by someone, with some formula. The formula is usually not stated.

The benchmark formula is reactions plus comments plus reposts, divided by impressions. Socialinsider's 2026 figure of 5.20% is that formula on 1.3 million company-Page posts. Metricool's 2026 study of 673,658 posts reports personal profiles at 2.60% against 1.60% for Pages, with the formula not stated. Those two numbers are not comparable with each other. Neither is comparable with the Pages figure LinkedIn shows, because the numerators differ.

What a good LinkedIn engagement rate looks like in 2026 goes through the three formulas and why they disagree. The engagement rate calculator gives you both comparable versions from your own counts.

What changed in 2025 and 2026

LinkedIn added more to personal analytics in the last eighteen months than in the five years before.

• Profile viewers and followers per post (May 2025), with notifications when a post drives either (August 2025). The business numbers, finally at post level.
• Member Analytics APIs (July 2025). In LinkedIn's words: "They will allow you to track your LinkedIn profile and post analytics within the dashboards of your favorite third-party social media management tools." This is how a tool can read your analytics with your permission, through the official API, rather than by signing in as you.
• The in-network and out-of-network split (June 2026), described above.
• A private note when readers flag a post as AI-like (July to August 2026). Hari Srinivasan, LinkedIn's Chief Product Officer, said LinkedIn would "test a way to privately flag, in your analytics dashboard, when members feel your post may have come off as inauthentic or heavy use of AI." By late August he reported that "those who copy-paste AI-written posts are seeing around 40 percent fewer views," and that "no single piece of feedback is used to adjust how widely a post travels." What LinkedIn has said about AI-written posts has the full dated record.

What LinkedIn does not show you

Four things. The last is the one this post is really about.

Other people's impressions. Only the author sees a post's analytics. Every "this post got a million views" figure you have read was reported by the author or guessed from reactions. Reactions, comments and reposts are the only public counts.

Dwell time. LinkedIn's ranking system reads how long people spend on a post, and says so. No analytics screen reports it. You can infer it from saves and sends, which are what a reader does after staying.

A native engagement rate. Covered above. Compute your own, and write down the formula.

How a post compares with your own typical post. This is the gap that matters. LinkedIn shows every post's numbers in isolation, plus a trend line across months. It never puts a post against your own median and says "this one did 1.4 times your normal, that one did 0.6." Without that, the numbers are trivia. A post with 2,000 impressions is a triumph for one account and a bad week for another, and LinkedIn's screens cannot tell you which you are.

Getting all of it out

Two routes, and they are not the same.

Export from creator analytics. This gives you a spreadsheet of combined post analytics or audience analytics for the range you chose, up to 365 days. Good for a quick look at the year.

Download your data. This gives you everything LinkedIn holds about your account, including your posts and activity. The help page gives the route: the Me icon, Settings and Privacy, Data Privacy, then "Download your data" under How LinkedIn uses your data, then "Select the data that you're looking for and Request archive." Timing: "If you select a specific type of data, we'll email you within minutes. If you select the larger download, you'll receive an email within 24 hours." Then "the data will be available for download for 72 hours."

Do the second one once a quarter. It is the only copy of your history that does not expire on LinkedIn's schedule.

Reading the numbers so they change what you write

The exercise takes twenty minutes, and most people never do it.

1. Take your last twenty posts. For each, note impressions, and reactions plus comments plus reposts.
2. Find the median of each column. Not the mean: one post that travelled will drag an average up and make every normal post look like a failure.
3. Divide each post by the median. A 1.00 is your typical post. Mark everything above 1.20 and everything below 0.80.
4. Read the two piles. What did the winners share: the first line, the format, the subject, the ending? What did the losers share?

That table says more about your readers than any benchmark, because it is measured on exactly the people who see you. It is also the comparison LinkedIn's screens never make, and the one that decides what to write next.

Depost AI does this step for you. Connect LinkedIn and it syncs your analytics history, then runs every post against your own typical post: your best and weakest as multiples of your normal, the kinds of post that work for you and the kinds to stop, and you against your niche. The Performance Analyzer is where it lives, and the analysis is free and needs no card. It can only read what is there. On a profile with a handful of posts it has less to say than on one with a hundred.

FAQ

What do impressions mean on LinkedIn?

LinkedIn defines impressions as the "number of times your post was shown on LinkedIn." It is a count of displays, so one person who sees your post three times produces three impressions. The distinct-people figure is "members reached," which LinkedIn calls an estimate that "does not include repeat views."

Can I see who viewed my LinkedIn post?

No. Post analytics show demographics of viewers (job title, location, company, company size, industry and seniority) in aggregate, never names. Named viewers appear only in the separate profile viewers panel, limited to three recent viewers on a basic account and a full list over 365 days on Premium.

How do I see analytics for a LinkedIn post?

Click the impressions count at the lower left of your post or "View analytics" at the lower right. Only you can see it. For all posts together, open your profile, find the Analytics section and click "Show all" to reach creator analytics.

Does LinkedIn show engagement rate for personal profiles?

No. LinkedIn publishes an engagement rate definition for company Pages only, and that one counts clicks as interactions. A personal profile gets the raw counts and an "engagements" total in creator analytics that includes reactions, comments, saves, sends and reposts. To compare with published benchmarks, compute reactions plus comments plus reposts divided by impressions yourself.

How long does LinkedIn keep post analytics?

By LinkedIn's help page: engagement and discovery counts for 1,000 days, members reached for 400 days, and demographic breakdowns for 180 days. Article and newsletter analytics last two years. Video analytics are listed as 365 days on one help page and 180 on another.

Can I export LinkedIn analytics?

Yes, two ways. Creator analytics has an Export button that produces an .XLSX file for any range up to 365 days. Settings and Privacy has "Download your data," which returns an archive of your account within minutes for a specific category or within 24 hours for the full set, available for 72 hours.

What does "out of network" mean in LinkedIn analytics?

The share of a post's impressions that came from people who neither follow you nor are connected to you, reached through the feed's recommendations, reshares or search. LinkedIn added the split in June 2026. A high out-of-network share means the post travelled beyond your audience.

Does LinkedIn show saves on posts?

Yes. Post analytics list saves ("the number of times members saved your post") and sends ("the number of times members sent your post to others on LinkedIn"). Both count as engagements in creator analytics.

What a pod is

An engagement pod is a group that agrees to react to and comment on each other's posts, usually within the first hour, to push each post further than it would travel alone. The manual kind lives in a WhatsApp, Telegram, Slack or Discord group. Someone drops a link, the others like and comment. The automated kind is a browser extension or a tool that signs in as each member and does the clicking.

Both rest on the same bet. That LinkedIn's ranking system treats early engagement as a signal of quality, and cannot tell a reader from a member of a group. In 2026 LinkedIn has said, in several ways, that it can.

What LinkedIn has said, dated

November 2025: "entirely ineffective." Gyanda Sachdeva, LinkedIn's VP of Product Management, told Social Media Today: "Our goal is to make engagement pods entirely ineffective." On detection: "We are increasing the number of ways we detect these pods and the suspicious behavior that happens in these pods." On what happens to a post: "We are increasingly flagging any artificially boosted content internally, and then also, we are limiting the reach of this content." On tools: "we're going to crack down on any third party tools, like a browser extension or a plug-in, that's automating any kind of manipulation by commenting on a bunch of posts at the same time."

12 March 2026: pods named in the feed rebuild. In the post announcing its rebuilt feed, LinkedIn wrote: "We're working to make engagement pods ineffective and curb comment automation or third party tools that create fake conversations, so real people and real perspectives stand out." How the LinkedIn algorithm works in 2026 covers the rest of that announcement. The line that matters here is that pods and comment automation are named as enforcement, apart from the ranking changes.

30 July 2026: the scale of the automated side. Hari Srinivasan, LinkedIn's Chief Product Officer, told The Register: "Everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

August 2026: comments ranked per reader. LinkedIn began ordering the comments under a post by relevance to each reader, instead of showing everyone the same order. The old pod playbook relied on early comments pinning to the top for every viewer. Now the comment at the top depends on who is looking. A comment from someone with no connection to the subject or the reader has no reason to be there. The algorithm post above has the detail and LinkedIn's own figure on comment reading time.

The rules a pod breaks

Three documents. The first is more direct than most people expect.

The Professional Community Policies, which every member agrees to, say: "Don't do things to artificially increase engagement with your content." They also carry the plainest description of a manual pod LinkedIn has written: "Respond authentically to others' content and don't agree with others ahead of time to like or re-share each other's content." That sentence covers the WhatsApp group with no software involved at all.

The User Agreement, effective 3 November 2025, section 8.2, bars members from using "bots or other unauthorized automated methods to access the Services, add or download contacts, send or redirect messages, create, comment on, like, share, or re-share posts, or otherwise drive inauthentic engagement." The same section bars using "browser plugins and add-ons or any other technology" to scrape the service. It also bars using "another's account (such as sharing log-in credentials or copying cookies)," which is how some pod tools operate.

The help page "Automated activity on LinkedIn", updated two weeks before this was written, says: "We don't allow the use of third-party software or browser extensions that scrape, modify the appearance of, or automate activity on LinkedIn's website." It describes the consequence, an account restriction, and the way out. Once the software is removed, the account is "automatically re-enabled at the time specified on the suspension notification." The glossary entry on account restrictions explains the range, from one feature refusing to work to permanent closure.

So a manual pod breaks the community policies. An automated one breaks the User Agreement as well. The distinction matters for what happens next.

What a pod costs

Two different costs, for the two kinds of pod.

The post, for a manual pod. The stated consequence is on the content. LinkedIn says it flags "artificially boosted content" and limits its reach. A post lifted by people who did not read it earns engagement from the wrong people. A ranking system that reads who engaged, not just how many, learns the wrong thing about who the post is for. Since August 2026 that group's comments do not even hold the top of the thread.

The account, for an automated pod. The consequence LinkedIn describes is enforcement: an account restriction for the software, and the tool itself being targeted. Sachdeva's "crack down on any third party tools" and Srinivasan's "hundreds of thousands of automated comment attempts" a day describe that side. This is the same category as comment bots and unauthorized schedulers. It is the one that can cost you the account rather than the post.

What nobody has published. You will read that LinkedIn detects pods with 97% accuracy. That flagged accounts lose 96% of their reach overnight. That a shadowban lasts sixty to ninety days. That a named pod tool was removed from a browser store on a given date. We could not trace any of those to a LinkedIn statement or to a documented test. They come from pages that sell automation, or sell protection from it. The honest version is weaker and still enough: LinkedIn says it limits the reach of boosted posts and restricts accounts that use automation, and it has not published a number for either.

Why pods stopped paying before LinkedIn said so

Even without enforcement, the economics changed.

A comment count without readers does not lift a post. Our test of 12,791 posts against each writer's own average found that asking for a comment earns 0.96 of a typical post once the writer is compared with themselves. Engagement that does not come from readers is the same thing measured from the other end. A pod produces the count without the reader. The count is not what moves a post any more. Who engaged, and whether they stayed, is.

LinkedIn now counts the comments that pay. It reports "knowledge-oriented comments" as a headline figure in its quarterly results, up 11% year on year in the quarter it reported in April 2026. It has also said that time spent reading comments rose 18%. The comment that pays is one a reader stops for. "Great post!" from a pod member is the opposite, and since August it is ranked accordingly.

What to do instead

Four things that produce the signal a pod imitates, from real readers.

1. Write the post that earns the comment. The shapes that beat a writer's own average in the test above are a personal story, a plain claim a reader could disagree with, and a before-and-after with numbers. Those are the ones people answer without being asked. A pod exists to rescue the post that gave them nothing to say.
2. Be in the comments yourself for the first hour. Replies from the author are fresh engagement while the post is still being tested. A thread the author is answering pulls other readers in. This is the one thing pods got right about timing, done honestly.
3. Comment on other people's posts, by hand, where you have something to add. LinkedIn's own metric is "knowledge-oriented" comments, and its ranking now puts a comment near the top for the readers it is relevant to. Twenty comments on posts you have nothing to say about do less than three where you do. How many comments a day is safe covers where the line sits for a person commenting by hand.
4. Use tools only on the drafting side. A tool that drafts a reply you read and send is a writing tool. A tool that sends it for you is the thing in section 8.2. The difference is who presses the button.

That last line is how Depost AI is built. It plans and drafts from what has already worked on your own account. It builds targeted feeds of the conversations worth joining and drafts replies in your voice. Every reply, comment and connection note is sent by you, never by a bot, and nothing publishes without your approval. It is also why we will not claim a post from it cannot be flagged, or that any competitor gets people banned. LinkedIn has named no tool. The rule is about what the tool does, not whose it is.

FAQ

Are LinkedIn engagement pods against the rules?

Yes. LinkedIn's Professional Community Policies say "don't agree with others ahead of time to like or re-share each other's content," which covers a manual pod. The User Agreement bars "bots or other unauthorized automated methods" to comment on, like or share posts, which covers an automated one. LinkedIn's VP of Product Management confirmed in November 2025 that pod activity violates its terms.

Do LinkedIn engagement pods still work in 2026?

LinkedIn says it is "flagging any artificially boosted content internally" and "limiting the reach of this content." Since August 2026 it ranks comments per reader, so a pod's early comments no longer hold the top of a thread for everyone. It has published no figure for how much reach a pod costs. The measured finding on our side is that comment counts without readers do not lift a post against the writer's own average.

Can you get banned for using an engagement pod on LinkedIn?

For a manual pod, the consequence LinkedIn describes is on the post: flagged and reach-limited. For software that automates liking or commenting, LinkedIn's help page describes an account restriction that is lifted once the software is removed, and its executives have said it will act against the tools themselves. LinkedIn has not published penalty durations, and the "60-to-90-day shadowban" figure has no source.

How does LinkedIn detect engagement pods?

LinkedIn has said it is "increasing the number of ways we detect these pods and the suspicious behavior that happens in these pods," and that it catches hundreds of thousands of automated comment attempts a day. It has not published how detection works or how accurate it is. The 97% accuracy figure circulating online is not LinkedIn's.

Is a WhatsApp or Telegram pod safer than a pod tool?

Safer for the account, not for the post. A manual group involves no unauthorized software, so the User Agreement's automation clause does not apply. The community policies' line against agreeing in advance to engage does apply, and LinkedIn says it limits the reach of boosted content however it was boosted.

What should I do instead of joining a pod?

Write posts that give readers something to answer, reply to comments yourself in the first hour, and comment by hand on posts where you have something to add. Keep any tool on the drafting side, so that you are the one who sends. Those produce the signal a pod imitates, from the readers LinkedIn's ranking is actually looking for.

The difference between an AI-assisted post that reads as a person and one that reads as a machine is not the model. It is not the prompt or the hidden characters either. It is what the model was given to work with. Here is the evidence, what LinkedIn has said it will and will not tolerate, and a method that starts from your material instead of a blank chat.

Why AI-written posts read as AI

Three independent descriptions of machine prose agree on the pattern. They come from a journalism institute, an encyclopedia's editors and a detection company.

Reads as generated: no number, name or event only you could know; lists of three where two would do; "it is not X, it is Y"; em dashes and balanced clauses; words like "delve", "resonate", "navigate" and "unlock"; a tidy lesson, then a closing question.

Reads as a person: a figure from your own work; a decision, and what it cost; a named thing, such as the client, the tool or the month; an opinion a reader could disagree with; a first line that is a claim; an ending on the point.

The Reuters Institute at Oxford described the fingerprints in December 2025: "previously rare words that have quickly become clichés or a formulaic sentence structure repeatedly used in the same text." It names the vocabulary, "semi-formal and corporate or academic-sounding, such as 'delve', 'resonate', 'navigate', and 'commendable'." It names the absences too: models "avoid slang, contractions (like 'gonna' and 'wanna'), colloquial language and passive voice." One editor's summary in the piece is the whole problem in a sentence: "The dead giveaway for me is just that it's a lot of words that don't say anything."

Wikipedia's editors keep a field guide for catching undisclosed machine text. Its headings read like a LinkedIn feed: "Undue emphasis on significance, legacy, and broader trends," "Superficial analyses," "Promotional and advertisement-like language," "Vague attributions and overgeneralization of opinions," "Negative parallelisms," "Rule of three." One is dated as a historical marker: "Overuse of em dashes (2022–September 2026)."

Pangram, which sells detection, adds the one that matters most for a post: "AI does not create specific details when writing creatively, and tries extremely hard to avoid proper nouns."

That last line is the mechanism. Asked to write about a subject with nothing to work from, a model produces the average of everything already written about it. Readers recognize the average because they have read it many times. The glossary entry on AI slop makes the same point: the problem is not the model, it is the absence of input.

What reads as human, measured

Two measurements. Both score each post against its own author's typical post, so audience size cannot skew the result.

Mylance, a LinkedIn content tool for consultants, backtested 895 posts across 37 creators. Its findings, in its words:

• "68% of posts high in specificity beat their baseline. Versus about 50% for vague posts. Concrete details, real numbers, and named things beat abstraction."
• "70% of posts high in emotional pull beat their own baseline."
• "61% of posts with a number in the first line beat their baseline. Versus 52% for posts that don't open with a concrete result."
• "67 posts a blind AI judge scored 4 or 5 out of 5 then landed in the bottom half of that author's baseline. The judge over-credited polish."

That last finding should worry anyone who judges drafts by how polished they look.

Our own test of 12,791 posts from 655 creators, measured the same way, found the personal story the only kind of post above the writer's own average, at 1.15 for 64% of the creators who wrote one. A plain bold claim was the only opening line above average. The question and pain-then-promise openers, the two most taught and the two a model reaches for first, came last at 0.82 each.

Both studies are small by the platform's standards, and both come from companies that sell writing tools, ours included. They point the same way regardless. Specifics beat polish, and a post that costs the writer something to say beats one that cost nothing.

What LinkedIn tolerates, in its own words

The record is short and worth having exactly.

May 2026. Laura Lorenzetti, LinkedIn's Global Editorial VP, wrote in the app: "It's ok to use AI to help you write, but your posts and comments need to represent your voice and your perspectives. The ultimate value comes from the human behind the tool." Social Media Today summarized the accompanying measure as restrictions on the reach of content "that appears to be generated by AI and lacks clear perspective."

July 2026. LinkedIn added a "Seems like AI slop" option to the menu on posts and comments. Hari Srinivasan, its Chief Product Officer, told The Register the aim was for writers "to get feedback from real humans on what sounds authentic – not just have an AI detector review it and get it wrong." He drew the line that matters: "AI and slop are not the same thing. Many people refine thoughts with AI, and we believe they want to know when they sound inauthentic." A flagged post, per Engadget, sees "reduced algorithmic reach similar to users' flagging them as 'not interested.'" The author is told privately in analytics.

August 2026. Srinivasan reported that "members are now experiencing 40% less views on what we classify as AI slop from just a few weeks ago." He added that LinkedIn had "built safeguards to help prevent individual feedback from being used to unfairly target other members." In the same period LinkedIn removed its own "enhance your post" writer. Its replacement, in his words, "proofreads your words, but does not change your voice."

So the platform's position matches the measurements. Help is fine, substitution is not. The full dated record is here.

A word on watermarks

Some of the advice on "making AI text undetectable" is now out of date. It is worth knowing why.

Watermarks now live in word choice, not hidden characters. Anthropic announced in August 2026 that future Claude models watermark their text this way: "Nothing is added to the text and there are no hidden characters." The pattern is "undetectable to the reader, but is detectable to anyone who has a key." Google DeepMind described the same approach for Gemini text in 2024. Anthropic adds: "light editing probably won't remove the watermark completely; a complete rewrite where every word is replaced will."

Two consequences. An invisible-character remover cleans pasted formatting, which is useful, and does nothing to a word-choice watermark. And none of this is what readers react to. A reader who taps "seems like AI slop" is not running a detector. They are reacting to the pattern in the first section, which a watermark neither causes nor cures.

The method

Seven steps. The first one does most of the work, and it happens before the model is opened.

Step 1: Write the material before you open anything

On paper, one line each: the number from your own work, the decision you made, the thing that went wrong, the opinion a reader in your field could disagree with. A post needs at least one of those. If you have none, the post is not ready, and no tool will make it ready. This is the step that separates the two columns above.

Step 2: Brief the model with your material and your own past posts

Not a topic. Paste the lines from step one, then two or three of your own posts that did well. Say: write it the way these are written, using only the facts above. The past posts carry your voice. The facts carry the specifics. A model given both rarely produces the average, because it has been given something other than the average.

Step 3: Ask for a tightening, not a draft

LinkedIn's own product decision is the right instruction: proofread, do not change the voice. Ask the model to cut, to fix grammar, to find the weak line, to shorten a sentence. Do not ask it for the point. The point is the part only you can supply, and the part the model is worst at.

Step 4: Open with a claim and end on the point

The first line is a statement a reader could disagree with from their own experience. Not a question, not a pain-then-promise. The last line is the point. Not a question, not a link, not "comment X for Y." Both rules come from the fair test, and both are the opposite of what a model does by default.

Step 5: Strip the tells

Read the draft once for these, in this order:

• em dashes
• lists of three where two would do
• "it is not X, it is Y"
• a sentence that says what the post is about instead of saying something
• "delve," "resonate," "navigate," "unlock," "game-changer"
• a tidy lesson in the last paragraph
• a question at the end

Replace each with something plainer, or delete it. The AI content detector for LinkedIn checks a draft for the patterns and names the line that gave it away. If you copied from a chat window, also clean the formatting it brought along. Our test of sixteen copied answers found Markdown bold, em dashes and curly quotes in most of them.

Step 6: Read it as a stranger

One question: after reading this, what does a stranger know that only I could have told them? If the answer is nothing, go back to step one. If the answer is a number, a decision or a name, the post is done.

Step 7: Watch the analytics note

If readers flag the post, LinkedIn tells you privately in your analytics. LinkedIn calls that feedback rather than a penalty, and that is the right way to read it. It is a reader saying the post sounded like everyone's. The fix is always the same: more of step one.

The same post, two ways

Both versions below were written for this article, to show the pattern. Neither is a tool's output. The founder is made up, and the figures in the second version are placeholders for your own.

The brief: a founder writes about a launch that did not work.

Built from nothing:

Launching a product is never easy. We recently launched a new feature, and while the results weren't what we hoped for, the lessons were invaluable. It's not about the outcome, it's about the journey. Here are three takeaways every founder should remember: stay resilient, listen to your users, and keep iterating. What's the biggest lesson you've learned from a launch? Let me know below.

Built from material:

We launched the annual plan in March. Twelve people bought it in the first week. Then nobody did for a month.

So I read the twelve support threads from that week. Nine of them had the same sentence in them: "I didn't know this was included."

The feature everyone had been asking for was already in the plan. The launch post never mentioned it.

This month I am rewriting the pricing page from those nine sentences, not from our roadmap.

Same founder, same event. The first version could be posted by anyone about anything, which is why a reader scrolls past it. The second has two numbers, a quoted sentence, a named mistake and a decision. A model could have tightened it in a minute.

Where Depost AI fits

This is the problem Depost AI was built around: step one and step two, done for you. It reads your published posts, your numbers and your brand voice before it writes, so the draft starts from your material rather than a blank prompt. It knows what you have already posted, so it does not hand you the same angle twice. Ask it to sharpen the hook, make a draft less salesy or humanize it, and it rewrites rather than regenerates. And nothing publishes until you approve it.

No tool can promise a post will never be flagged, because a reader decides that. What a tool can do is make sure there is something of yours in the post before the model touches it.

FAQ

Can LinkedIn tell if a post was written with AI?

LinkedIn has not published how its detection works or how accurate it is, and its Chief Product Officer has said "AI and slop are not the same thing." The signal LinkedIn is building from is readers flagging posts, which respond to how a post reads rather than to which tool wrote it. A post with nothing specific in it reads as generated whether or not it was.

Does AI-written content get less reach on LinkedIn?

Posts that readers flag as AI slop get reduced reach, in the way a "not interested" signal reduces it. LinkedIn reported in August 2026 that content it classifies as AI slop was getting around 40% fewer views than a few weeks earlier. It has published no figure for AI-assisted posts in general. The measured difference in the studies above is between specific posts and vague ones, not between tools.

Is it OK to use ChatGPT or Claude for LinkedIn posts?

By LinkedIn's own account, yes, if the result represents your voice and your perspective. Its Global Editorial VP said in May 2026: "It's ok to use AI to help you write, but your posts and comments need to represent your voice and your perspectives." Use the model to tighten what you wrote, not to produce the point.

Does removing hidden characters make AI text undetectable?

No. Anthropic says Claude's text watermark adds "no hidden characters" and lives in word choice, and Google describes the same approach for Gemini. An invisible-character remover cleans pasted formatting and does not touch that. More to the point, readers do not run detectors. They react to vague, polished, specific-free prose, which no cleaner fixes.

What are the signs a LinkedIn post was written by AI?

The ones detection guides agree on: no specific number, name or event; lists of three; "it is not X, it is Y"; em dashes; vocabulary like "delve," "resonate" and "navigate"; a tidy lesson at the end; and a closing question. The underlying sign is simpler. The post could have been written by anyone about anything.

How long should an AI-assisted LinkedIn post be?

LinkedIn's marketing blog recommends 200 to 300 words. Our own test of 12,791 posts found no length rule that survived measuring against each writer's own average. Mylance's test found posts under 400 characters the most reliable underperformers. Write to the point and stop. A post that is short because it has nothing in it is the one to worry about.

Should I tell readers a post was written with AI?

LinkedIn does not require a label for AI-assisted posts, and its own guidance focuses on whether the post represents your voice and perspective. If the specifics, the decision and the opinion are yours, the post is yours. If none of them are, a label will not help it.

Most of what you have read about "the LinkedIn AI penalty" is a guess built on those three facts. Usually it comes with a percentage nobody can trace. This post is the record instead: what LinkedIn said, in its own words and with dates, what happens to a flagged post, and what to do if you use AI to write.

What LinkedIn has said, dated

Every item below is LinkedIn describing its own behavior. It comes either from its newsroom or from a named executive quoted by a named publication. Nothing is inferred from a reach chart.

12 March 2026: the feed rebuild names what gets pushed down. LinkedIn announced its rebuilt feed and listed what it is reducing: "We're reducing recycled and click-driven posts to make the Feed feel more useful" and "repetitive, low-substance posts and engagement bait, such as 'comment to agree' prompts or videos that don't match the text." It named enforcement separately: "We're working to make engagement pods ineffective and curb comment automation or third party tools that create fake conversations." It also said: "This isn't about limiting reach or discouraging participation. It's about making sure that when you engage with a post, you're interacting with a real person and a real point of view." How the LinkedIn algorithm works in 2026 covers the ranking side.

21 May 2026: AI help is fine, AI substitution is not. Laura Lorenzetti, LinkedIn's Global Editorial VP, wrote in an in-app post, reported by Social Media Today: "When AI is overused, especially at scale and in an automated way, it dilutes the valuable insights that real human conversations can spark." Then the line that settles most of the argument: "It's ok to use AI to help you write, but your posts and comments need to represent your voice and your perspectives." The measures described were two. Limits on the reach of AI-generated content that lacks a clear perspective, and detection of automated or AI-generated comments.

30 July 2026: the button. LinkedIn added a "Seems like AI slop" option to the three-dot menu on posts and comments. Engadget reported that flagged posts "will also see reduced algorithmic reach similar to users' flagging them as 'not interested.'" Hari Srinivasan, LinkedIn's Chief Product Officer, told TechCrunch: "AI slop is a top priority for all of us. We really care about this." The Register quoted him on the automated side: "Everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

3 August 2026: LinkedIn removes its own AI writer. Srinivasan, per ACS Information Age, described "removing the 'enhance your post' feature you see when you write a post or message and replacing it with a feature that proofreads your words, but does not change your voice." The AI assistant for profile headlines and About sections stayed.

24 and 25 August 2026: a million flags, and a number. IBTimes UK reported that the button "was used more than one million times in its first two weeks." It also reported that Srinivasan "framed the alert as feedback rather than a penalty." Fortune carried his figure for the effect: "members are experiencing 40% fewer views on what the platform classified as AI slop compared to a few weeks prior." That 40% is LinkedIn's own number with no method beside it. Treat it as a direction, not a measurement you could reproduce.

What "AI slop" means to LinkedIn

It is not an AI detector in the usual sense. Srinivasan was explicit about that to The Register: "AI and slop are not the same thing. Many people refine thoughts with AI, and we believe they want to know when they sound inauthentic."

The target is content with no perspective in it. Social Media Today summarized the May measures as limits on content that "appears to be generated by AI and lacks clear perspective." Both halves matter. A post that reads as generated and says nothing only its author could say is the target. A post drafted with help that carries a real number, a real decision and a real opinion is, by LinkedIn's own words, fine.

The definition moves, and readers set it. Srinivasan to ACS: "Slop is hard to define and the definition changes; this lets us tune our models and make better feeds." The button collects reader judgments and the models train on them. So the reader, not a classifier, is the thing to write for. The glossary entry on AI slop lists the tells readers react to.

What happens to a flagged post

Putting the reports together, the sequence is this.

1. A reader taps "Seems like AI slop" in the three-dot menu on a post or a comment.
2. The post is hidden from that reader's feed, with the message "Thanks for letting us know. Your feedback helps improve the feed."
3. The post's reach is reduced in the way a "not interested" signal reduces it. That is a ranking effect on the post, not an action against the account.
4. The author sees a note in analytics. LinkedIn is adding analytics that show when readers flag a post as possibly AI-generated. The aim, in The Register's report of Srinivasan's words, is for authors to "get feedback from real humans on what sounds authentic."
5. The flag trains the models. Each report is a labeled example of what readers consider slop. That is how LinkedIn tunes detection as the definition shifts.

What LinkedIn has not said matters as much. It has not published how detection works, how accurate it is, or how many flags it takes before reach moves. It has not described any account-level penalty for AI-written posts. The enforcement language, the kind that can affect an account, is reserved for automation: pods, comment bots and unauthorized third-party tools. LinkedIn says it blocks those by the hundreds of thousands a day.

So, does LinkedIn penalize AI content?

It limits the reach of posts that read as AI and carry no perspective. It also says plainly that using AI to help you write in your own voice is acceptable. LinkedIn itself rejects the word penalty. The author's note is "feedback rather than a penalty," and the March announcement said the feed changes were not "about limiting reach or discouraging participation."

The honest reading: LinkedIn penalizes generic, not AI. A post written entirely by hand with no number, no decision and no opinion reads as slop to the same reader. The same button is waiting for it. A post drafted with help that carries something only its author could know does not look like slop, because it is not.

That matches the data on posts in general. In a test of 12,791 LinkedIn posts against each writer's own average, the personal story was the only kind of post that beat the writer's typical result. A plain claim a reader could disagree with was the only opening line above average. The things LinkedIn says it wants in the feed were already the things that worked.

How much of the feed is AI-written

Nobody outside LinkedIn knows, and LinkedIn has not said. The estimates in circulation come from companies that sell detection, and they disagree. Pangram's figures, reported by ACS, put nearly a third of short-form LinkedIn content and more than 40% of long-form content as AI-generated. Originality.ai told The Register that 81% of the 5,000 posts it sampled in one month were. Neither method is published in enough detail to check.

The number on the record came from readers. More than a million flags in the button's first two weeks. Whatever the true share, readers think it is high enough to be worth a tap.

What the removal of LinkedIn's own writer tells you

This is the most telling move of the year, and it gets the least attention.

LinkedIn built "enhance your post", promoted it, and took it out. It replaced it with something that corrects your words without changing them. It kept the assistant for profile headlines and About sections, which are not the feed. A platform that sells AI tools decided that generated posts were costing its feed more than the feature was worth.

The lesson is not that AI is bad at writing. It is that generation was never the valuable part. The value in a post is the material: what happened, what it cost, what you decided, what you think. A model given none of that produces the average of everything already written on the subject. Readers recognize the average on sight, because they have read it many times. A model given your material can tighten it. LinkedIn's own product decision says the same thing: proofread, do not generate.

What to do if you use AI to write

Five moves, in order of how much they matter.

Start from your material, not from a blank prompt. Before anything is drafted, write down the number, the decision, the thing that went wrong, or the opinion someone could argue with. If there is none, the post is not ready, with or without AI.

Use the model the way LinkedIn now uses it on itself. Tighten, cut, fix the grammar, find the weak line. Do not ask it for the point. The point is the part only you can supply.

Read the draft for the tells before you post. The common ones:

• an opening that sets a scene without making a claim
• lists of three where two would do
• "it is not X, it is Y"
• a tidy lesson at the end
• a question as the last line

The AI content detector for LinkedIn checks a draft for these patterns and names the line that gave it away. Copying from a chat window also brings formatting along. Our copy-button test found Markdown bold, em dashes and curly quotes in most copied answers, which mark a post before anyone reads a word of it.

Never automate comments. Reach limits are a ranking matter. Comment automation, pods and unauthorized tools are enforcement, and LinkedIn names them as such. How many comments per day is safe covers where the line sits for commenting by hand.

Watch your analytics. If the note appears, it is not an algorithm's verdict. It is a reader saying your post sounded like everyone's. LinkedIn calls it feedback, and that is the useful way to treat it.

Where Depost AI fits. This is the problem it was built around. It drafts from your own published posts, your numbers and your brand voice rather than from a blank prompt, so the material in the draft is yours before any model touches it. Nothing publishes until you approve it. No tool can promise a post will never be flagged, because a reader decides that. What a tool can do is make sure there is something of yours in the post to begin with.

What is still unknown

• Whether the button has reached every member. The July reports described it as being added. By late August LinkedIn was quoting usage figures, which suggests a wide rollout, but it has not said the button is everywhere.
• How detection works, and how often it is wrong. A person who writes in a plain, tidy style has no published way to know whether their posts read as generated, other than the analytics note.
• Whether the 40% figure holds. It is LinkedIn's own, from a few weeks of data, with no method beside it.
• Whether LinkedIn has acted against any AI comment tool by name. We found no such case on the record as of October 2026.

FAQ

Does LinkedIn penalize AI-generated content?

LinkedIn limits the reach of posts that appear AI-generated and lack a clear perspective, and reduces the reach of posts readers flag as AI slop. It does not ban AI-assisted writing. Its own words, from May 2026, are that it is fine to use AI to help you write as long as the post represents your voice and your perspective. LinkedIn describes the author's flag notice as feedback rather than a penalty.

What is the "Seems like AI slop" button on LinkedIn?

An option LinkedIn added on 30 July 2026 to the three-dot menu on posts and comments. Tapping it hides the post from the reader who flagged it, reduces the post's reach in the way a "not interested" signal does, and gives LinkedIn a labeled example to tune its detection models. LinkedIn reported it was used more than a million times in its first two weeks.

What happens when a LinkedIn post is flagged as AI slop?

The post is hidden from that reader's feed with the message "Thanks for letting us know. Your feedback helps improve the feed." Its reach is reduced in the way a "not interested" flag reduces reach. The author is told privately through analytics that readers flagged it. LinkedIn has described no account-level consequence for flagged posts.

Can you still use AI to write LinkedIn posts?

Yes, by LinkedIn's own account. Its Global Editorial VP said in May 2026: "It's ok to use AI to help you write, but your posts and comments need to represent your voice and your perspectives." What LinkedIn limits is content that reads as generated and carries no point of view.

Did LinkedIn remove its AI writing feature?

Yes. In August 2026 LinkedIn removed the "enhance your post" option from the composer and replaced it with a proofreader that, in its Chief Product Officer's words, "proofreads your words, but does not change your voice." The AI assistant for profile headlines and About sections was kept.

How do I know if my LinkedIn posts are being flagged as AI?

LinkedIn is adding a note in post analytics when readers flag a post as possibly AI-generated. It says the purpose is to give authors feedback from real readers on what sounds authentic. LinkedIn has described no public count of flags on a post and no notice to other readers.

Does LinkedIn detect ChatGPT-written posts?

LinkedIn has not published how its detection works or how accurate it is, and its Chief Product Officer has said that "AI and slop are not the same thing." The signal it is building from is reader flags, which respond to how a post reads rather than to which tool wrote it. A post with nothing specific in it reads as generated whether or not it was.

Are AI-generated comments allowed on LinkedIn?

LinkedIn said in May 2026 that it detects and limits the reach of automated and AI-generated comments, and it names comment automation as something it takes enforcement action against. It reports blocking hundreds of thousands of automated comment attempts a day. Writing a comment with AI help in your own voice is covered by the same rule as posts; running a tool that comments for you is not.

That method has a flaw that is easy to miss and hard to fix. It mixes up what kind of post it is with what kind of person wrote it. If senior people with warm networks are the ones posting company news, company news will look like a winning shape whether or not it is one.

So we ran the test the other way. We took 12,791 posts published between 9 July and 6 October 2026 by 655 creators and, instead of comparing creators with each other, compared each creator with their own other posts. If a rule helps, it should lift the people who use it above their own average.

Seven rules held. Seven collapsed, including the ones that sound most like advice. And one reversed when we ran it on Depost AI users' own posts.

How the fair test works

For every post, engagement (reactions, comments and reposts) divided by the same creator's average over the three months. On that scale 1.00 is that creator's typical post. A 1.20 did 20% better than their normal, a 0.80 did 20% worse.

A rule's score is the median across the creators who used the shape at least twice and did something else at least twice, so one post that went viral cannot carry a format. Beside each score is the share of those creators who did better with the shape than without it, which is the number that tells you whether a rule works for most people or for a loud few. No cell below rests on fewer than 15 creators.

The grey dots are what you would get from the usual method on the same posts. The orange dots are the fair test. The rest of this post goes through them, then through what the post is for, how it opens, how it ends, the one format that reversed, and what changes by niche.

The seven rules that survived

None of them needs a new idea.

A single image beats the writer's own average by 39%. 1.39, and 72% of the 222 creators who posted both with and without a picture did better with it. It is the strongest result in the data and it held in every niche.

Asking for nothing beats every ask. A post that ends on its point earned 1.16 for 61% of 271 creators. A link at the end earned 0.80, and only 29% of the 198 creators who used one did better with it than without.

A personal story beats the average, and promotion falls furthest below it. 1.15 for 64% of the 276 creators who wrote one, against 0.77 for posts promoting something.

The plain bold claim is the only opener above water. 1.06 across 342 creators. The question and pain-then-promise, the two most taught openers, both earned 0.82.

Teaching is 47% of everything published and earns 0.92. Not the worst shape. The most common one, and below average for 56% of the people who write it.

Text with no picture earns 0.69. The format most people default to when they are in a hurry is the one that costs them most.

The webinar invite earns 0.60. In marketing, it beat the writer's own average for none of the 17 creators who posted one.

The seven that collapsed

This is the part most reports leave out, because the first cut of this one had different headlines and they were wrong.

The first version compared creators with each other, per follower, against the average for their niche. That is how most LinkedIn advice is produced, and it produced numbers that looked like findings: posts written to SDRs at 2.67 times a typical post, the marketing giveaway at 2.73, company and career news at 1.48. Under the fair test those became 0.88, 1.01 and 0.97.

Asking for a comment. 1.31 in the usual comparison, 0.96 once each creator is compared with themselves. The comment ask does not lift a post. The people who ask for comments already had readers who comment. LinkedIn, for its part, now names "comment to agree" prompts as engagement bait in its description of what the feed pushes down.

Writing to a named job. The most repeated advice on the platform, and the clearest collapse. Across 121 audiences with enough posts to rank, almost every one sits within a tenth of 1.00. Posts to SDRs earned 0.88 of the writer's own average, posts to growth marketers 0.81, posts to an AI audience 0.91. The 2.67 was a fact about who writes to SDRs, not about writing to them.

Company and career news. 1.48 in the first cut. Real number, wrong reading: senior people with warm networks post company news, and their other posts do just as well. Against themselves, 0.97.

The giveaway. 2.73 in the first cut, the highest number in it, and it belonged to 11 marketing creators. Against their own averages those 11 earned 1.01. The number was real. It was a fact about a handful of people.

The transformation opener. "I used to do X. Now I do Y." 1.12 in the usual comparison and 0.91 in the fair one, across 123 creators.

The length rule. The first cut said posts over 320 words lose 30%. Against the writer's own average they earn 0.93, within the noise, and no band of length moved most of the people who wrote at it. No length rule survived. Write to the point and stop, however long that takes.

Two rules, then, that looked like the strongest findings in the data were facts about who uses them. That is the whole argument for measuring against yourself.

What the post is for

Six things a post can be for. Only one of them beats the writer's own average.

The personal story is the only goal above 1.00, at 1.15 for 64% of the 276 creators who write them, and it is a third of what gets published. Teaching is 47% of everything and earns 0.92. Promotion is 9% of posts and earns 0.77, and only 30% of the 145 creators who promoted something beat their own average doing it.

The top right of that chart, the corner for things that are published a lot and also perform, has one circle in it.

The first line

The openers taught in every LinkedIn course are the ones at the bottom of this table.

Three things are worth reading out of it.

The bold claim is the only shape above 1.00, and it is also the one most creators already use. It is the safe default rather than a trick. What makes it work is that it is falsifiable: a reader can disagree with it from their own experience, and disagreement is what earns the click on "see more".

Opening with a statistic earns 0.84. That is the data-report instinct, and it loses even for creators who have the data. A number in the first line reads as a pitch for what follows. The same finding told as a claim does better.

The question and pain-then-promise finish last, at 0.82 each, with 115 and 103 creators using them. Those two are the most taught openers on the platform. The hook generator writes alternatives from a draft, and the glossary entry on hooks covers the shapes.

The last line

The ending matters more than the opening, and in the opposite direction from what most people do.

Ending on the point beats every ask. The link costs 20%, and on Depost AI users' own posts it cost 18%, the one finding that held in both sets. The comment ask does not help once a creator is compared with themselves.

Where the link goes instead is a trade, and it is worth being honest about it. Moving the URL to the first comment protects the post, but a link in the comments earns fewer clicks than one in the body. If the post's job is the click, put the link in and accept the cost. If the post's job is the reader, end on the point.

If you change one habit from this report: delete the link, delete the question, end on the point.

The one that reversed: carousels

For the library's top creators the carousel is the worst format. For Depost AI users measured against their own posts, it is the best.

For the 222 top creators who used both, the carousel earned 0.59 of their typical post. For the 27 Depost AI accounts with twenty or more measured posts, 4,628 posts scored the same way with clicks excluded so a swipe could not count, it beat the same person's other posts by a third, at 1.34. Video reversed too, from 0.82 to 1.13. The single image held in both sets.

The data cannot say why. Two readings fit and we are not going to pick one for you: the top creators are mostly large accounts whose text posts already travel, so a document has a higher bar to clear; the Depost AI accounts are smaller and mostly in B2B niches where a document earns the saves that carry it. Either way, the rule written for one group was wrong for the other.

This is also why the 2026 engagement-rate benchmarks put documents near the top while this test puts them at the bottom for top creators. That table measures interactions per impression on company pages. This one measures total engagement per post against the writer's own average on personal profiles. They answer different questions and both can be right, and the practical conclusion is the same one that post reaches: your own median is the baseline that matters.

That is the case for measuring your own posts instead of borrowing someone else's rules, and it is what the Performance Analyzer in Depost AI does. It reads your published posts against your own average and tells you which format and which shapes beat it for you, not for someone with a million followers.

What changes by niche

The general findings above held across niches. The shapes that win inside each one differ, and in two places they run opposite ways.

Marketing, sales and growth

The one niche with enough creators to test most shapes both ways.

Write the before-and-after (1.23, for 73% of 15 creators), the hot take (1.12, 61% of 36) and the market read (1.11, 68% of 34). Stop the launch announcement at 0.55, the webinar invite, and the case study, the surprise here at 0.67 across 31 creators.

The giveaway, at 2.73 the highest number in the first cut, is not on this chart because only 11 marketing creators used it both ways. For them it earned 1.01.

Business and startups

Thoughts on leading people is the reliable shape: 41 creators, 61% better with it, 1.05. The before-and-after and the market read sit at 1.10 on smaller groups. The how-to is the most used shape in the niche and sits below, at 0.93 for 55 creators. The framework, the house style of this niche, sits at 0.98 on 20 creators. The founder-lesson post did not have enough creators using it at least twice to judge, so it is not on the chart and we are not going to pretend it is.

The thinner niches

Software and product: tools and resources 1.01 on 53 creators, the how-to 0.92, frameworks 0.84, market commentary 0.83. Education and careers: nothing above 1.00 with enough creators to test. Finance: the how-to 0.98 on 15 creators. Health: the how-to 1.03 on 19, mindset posts 0.78.

In these four, the general findings are the better guide, because the niche-specific cells do not have the creators to stand on. A number from 12 creators is an anecdote with a decimal point.

How Depost AI puts these findings to work

Every finding above is a constraint, and a constraint is something a tool can be built around. Depost AI was built around these. Here is what each fact becomes inside it.

The fact: the only honest baseline is your own average. The Performance Analyzer reads your published LinkedIn posts and runs the comparison in this post on them: every post against your own typical post, so you see which shapes, openers, endings and formats beat it on your account, not on a top creator's. It is the fair test, run on you.

The fact: the personal story is the only kind of post above average, and the how-to is half of what gets published. The Idea Generator starts from the posts that beat your own average and from its library of high-performing posts, each one labelled by shape, opener and ending. It knows what you have already posted, so it does not hand you the same angle twice, and the ideas it pitches are built on your material: a decision, a number, a before-and-after, the thing only you can say.

The fact: open with a plain claim, end on the point, keep the link out of the body. The Post Generator writes in your voice, learned from your own posts, with a first line a reader could disagree with and an ending that stops at the point. Ask it to sharpen the hook, make a draft less salesy, or name your product without opening with it, and it rewrites the draft rather than starting over. Every scheduled post goes out with its first comment already written, which is where the link lives.

The fact: one real image lifts a post by 39%, and text alone earns 0.69. The Graphic Generator designs the image for each post in your brand's own visual identity, so nothing goes out as a wall of text.

The fact: a number that is not yours reads as a pitch. Depost AI writes around a number it does not have rather than inventing one, and the numbers it does use are the ones in your history.

And nothing publishes until you say yes. Approved posts go out through LinkedIn's official API, with no comment automation and no engagement tools, which are the things LinkedIn names as enforcement rather than ranking.

So run the fair test on your own posts. Connect LinkedIn, and Depost AI reads your history in about a minute and shows your best and weakest posts as multiples of your own typical post, the kinds that work for you and the kinds to stop. The analysis is free and needs no card.

One honest line: the analysis is what makes the rest true. On a brand-new profile with nothing imported, Depost AI is working from the general patterns above, the same ones any model could be told. The difference starts the moment your history is in.

What this cannot tell you

These creators already post well. They are a curated set, and the direction of each finding transfers better than the multiple does. Your own baseline is lower.

Engagement is not reach, and it is not revenue. A post with 400 comments and no customers is a bad post. Nothing measured here touches pipeline.

It is correlation, not cause. A vulnerable post may do well because vulnerability works, or because the people willing to write one are better writers. The data cannot separate those. The honest version of every finding here is "posts shaped like this did better", not "this shape will do better for you".

Three months is one season. The window covers a northern summer and the return to work.

The shape, opener, ending and audience labels on each post were assigned by a model, not by a person, so a small share of them will be wrong. And follower counts are current rather than as at the date of each post.

What to change this week

Three moves, in the order they pay.

1. Put one real picture on every post. A screenshot, a photo, a chart. The single image is the strongest result in the data, and text alone earns 0.69.
2. End on the point. Delete the link, delete the closing question, delete "comment X for Y". If a link has to go somewhere, the first comment, knowing it will earn fewer clicks there.
3. Swap one how-to for one story. If half of what you post is teaching, make one of next week's posts a decision you got wrong or a before-and-after with the numbers. The personal story is the only goal that beats the writer's own average, and it is the one most people under-publish.

Then measure your own. Twenty posts, each divided by your median, and look at which ones cleared it. That table says more about your readers than this one does. The post analyzer grades a single draft against a fixed rubric before it goes out; the Performance Analyzer in Depost AI runs the comparison above on your whole history.

FAQ

What type of LinkedIn post gets the most engagement in 2026?

Measured against each writer's own average across 12,791 posts, the personal story is the only post goal above 1.00, at 1.15 for 64% of the 276 creators who wrote one. A single image lifts a post by 39%, ending with no ask earns 1.16, and the plain bold claim is the only opener above average. Promotion earns 0.77 and the webinar invite 0.60.

Do carousels still work on LinkedIn in 2026?

It depends on whose posts you measure. For the top creators in this test the carousel was the worst format at 0.59 of their typical post. For Depost AI users measured against their own posts it was the best, at 1.34. Engagement-rate benchmarks for company pages also put documents near the top. The only reliable answer is to run the comparison on your own last twenty posts.

Does a link in a LinkedIn post hurt reach?

In this test a post that ends by asking for a click earned 0.80 of the writer's typical post, and only 29% of the 198 creators who used one did better with it. On Depost AI users' own posts the link cost 18%. LinkedIn has never confirmed a link penalty; what the data shows is that link posts earn fewer of the actions that count. Moving the link to the first comment protects the post but earns fewer clicks.

Should you end a LinkedIn post with a question?

Not if the goal is engagement. Ending with no ask at all earned 1.16 for 61% of 271 creators, while asking for a comment earned 0.96 once each creator was compared with themselves. Opening with a question also finished last among openers at 0.82.

How long should a LinkedIn post be?

No length rule survived the fair test. Posts over 320 words earned 0.93 of the writer's own average, which is within the noise, and no band of length moved most of the people who wrote at it. Write to the point and stop, however long that takes.

Does niching your LinkedIn audience work?

Not in this data. Across 121 audiences with enough posts to rank, almost every one sat within a tenth of 1.00 of the writer's own average. Posts to SDRs earned 0.88, posts to growth marketers 0.81. The high numbers for named jobs in the usual comparison were facts about who writes to those jobs, not about writing to them.

What is the best hook for a LinkedIn post in 2026?

A plain bold claim, at 1.06 across 342 creators, the only opening line that beat the writer's own average. A curiosity gap sat at 0.96, a transformation line at 0.91, a statistic at 0.84, and the question and pain-then-promise, the two most taught, at 0.82 each.

How was this measured?

12,791 LinkedIn posts published between 9 July and 6 October 2026 by 655 creators, drawn from a library of 141,170 posts. Each post's engagement (reactions, comments and reposts) was divided by the same creator's average over the three months, so 1.00 is that creator's typical post. A rule's score is the median across creators who used the shape at least twice and did something else at least twice, with the share of those creators who beat their own average beside it. No cell rests on fewer than 15 creators. Depost AI users were 27 accounts with twenty or more measured posts, 4,628 posts, scored the same way with clicks excluded.

The hard part is the word free. Some tools open and work. Some want an account first. Some are a seven-day trial with a bill at the end. This list covers ten sets of free LinkedIn tools and says which kind each one is. It starts with ours.

If you came for one job, skip ahead: writing posts, formatting, your profile, carousels, analytics, scheduling, or lead generation.

Three questions to ask about any free tool

Do you need an account? A tool you can open and use is free in the plain sense. A tool that asks for your email first is free too, but you will get the emails.

Is it a tool, a plan, or a trial? A free tool does one job for as long as you like. A free plan is a paid app with limits. A free trial is the paid app for a few days, and then it charges you. Lists of "free LinkedIn tools" often mix all three.

Where does your file go? Some tools do the work in your browser, so your photo or PDF never leaves your device. Others send it to a server. For a client's deck or your own resume, that matters.

The ten at a glance

How this list was put together: we opened each tool's own free tools page or pricing page on 9 October 2026 and wrote down what it lists. The counts are the tools on each page that day. We did not score the other tools' output, so entries 2 to 10 are in no strict order. Where a site does not state something, this list says so and does not guess.

1. Depost AI

Best for: anyone who wants one place for the small LinkedIn jobs, with no sign-up.

This is ours, so weigh it accordingly. Depost AI's free tools are 64 separate tools. None of them asks for an account or an email. You open the page, do the job, and copy or download the result.

Here is every one, grouped by the job, with what it does.

Writing tools (10)

• Post generator: writes a finished post from a rough thought, a link, or a few bullet points.
• Post booster: rewrites a post that did poorly and names every change it made.
• Hook generator: gives you several opening lines for a post you have already written.
• Content idea generator: suggests a week of post ideas from your role and your niche.
• Comment generator: drafts a reply to a post you paste in, for you to edit and post yourself.
• Hashtag generator: picks three to five relevant tags for a post.
• Poll generator: writes a poll question and answer options that fit LinkedIn's character limits.
• Article title generator: writes titles for an article or a newsletter edition.
• Carousel content generator: writes the copy for a carousel, slide by slide.
• Post templates: a library of post structures you can filter, each with a filled-in example.

Format and preview tools (12)

• Text formatter: adds bold, italic, and lists that survive pasting into LinkedIn.
• Post preview: shows where "see more" cuts your post and how it reads on a phone.
• Character counter: counts against the limits for posts, headlines, About sections, and connection notes.
• Invisible character remover: finds and removes the hidden characters and Markdown asterisks in text pasted from ChatGPT.
• Bullet points: turns your lines into a list with the bullet you choose.
• Arrows: every arrow that pastes into a post or headline, one click to copy.
• Symbols: stars, checks, currency, and legal marks, one click to copy.
• Emojis: emojis sorted for work, with a search box.
• Post image size: every LinkedIn image size in pixels, and a checker for your own image.
• Carousel tester: shows your carousel PDF inside a post at desktop and phone width, and checks it against LinkedIn's limits.
• URL cleaner: strips the tracking from a LinkedIn link and gives you the short version.
• Link preview checker: shows the card LinkedIn will make for a URL, and names any missing tags.

Profile tools (4)

• Headline generator: turns a job title into a headline that says what you do and who for.
• About generator: drafts an About section from your role, your work, and who you want to reach.
• Experience generator: writes six achievement bullets for one role, from the numbers you give it.
• Profile URL finder: shows where your profile link is and gives you the clean form for a resume.

Design tools (10)

• Carousel generator: turns an outline into a swipeable PDF in five layouts, with no watermark.
• Banner maker: makes the 1584 by 396 background photo with your line placed where you drag it.
• Profile picture resizer: frames your photo in LinkedIn's circle and exports it at the right size.
• Image resizer: resizes any image for a post, a banner, a company page cover, or a logo.
• QR code generator: makes a QR code for your profile, page, or post, with your logo and color.
• Quote card generator: turns one line into a feed-sized image with your name on it.
• Meme generator: writes the caption for a work meme and puts it on a template.
• Stats card maker: turns four of your numbers into a recap card you can post.
• Post screenshot generator: turns a post into a clean image, from its link or from text you type.
• Screenshot background maker: puts a screenshot on a background, ready to post.

Media tools (9)

• Video downloader: saves the video from a public post.
• Image downloader: saves the images from a public post.
• Carousel downloader: saves the PDF behind a document post, or each slide as an image.
• Post downloader: saves every part of a public post: text, images, video, and PDF.
• Post to PDF: saves a public post as a PDF with its author, date, text, and images.
• Profile picture downloader: saves a public profile photo at 400 by 400.
• AI metadata remover: removes the hidden data from a photo or video, including the Content Credentials behind LinkedIn's CR tag.
• PDF metadata remover: removes the author name, app details, and dates from a PDF.
• Video specs: every LinkedIn video limit, and a checker for your own video.

Analysis tools (6)

• Engagement rate calculator: works out your rate from a post's numbers and shows it next to the platform average.
• Post analyzer: grades a draft out of 100 and names the lines to rewrite.
• AI content detector: points at the lines in a draft that read as generated.
• Best time to post: shows the best days and hours from published research, in your own time zone.
• Growth plan generator: builds a posting plan from your role, your niche, and the time you have.
• Post date extractor: reads the exact date and time a post or comment went up, from its link.

Outreach tools (6)

• Connection request generator: writes a connection note inside LinkedIn's 300 characters.
• DM generator: writes a first message to a new connection, or a follow-up.
• InMail generator: writes a subject line and a message for someone outside your network.
• Lead magnet generator: writes a guide on the topic you give it and exports a branded PDF you can edit.
• Connection request limit calculator: shows how many invitations you have left this week and a safe daily pace.
• Outreach ROI calculator: works back from invitations to meetings to customers, against the hours you spend.

Tools for X (7)

• AI tweet generator: writes three post options from a rough idea, each inside 280 characters.
• X thread generator: writes a full thread from one prompt.
• X bio generator: writes five bios inside the 160 character limit.
• X character counter: counts the way X counts, where every link is 23 characters.
• X fonts generator: bold, italic, and other text styles that survive posting.
• X post mockup generator: makes a realistic X post image with the name, photo, and numbers you choose.
• Twitter banner maker: makes a 1500 by 500 header with your line on it.

Two things to know

The tools that write for you call an AI model, so each visitor gets a set number of runs a day. The tools that only handle a file, such as the image resizer, the PDF metadata remover, and the carousel tester, do the work in your browser, so the file is never uploaded.

What they do not do: remember you. Each tool does one job from what you type in that day. Learning your voice from your past posts and planning the week is the paid product.

2. LinkedIn itself

Best for: scheduling a finished post and reading its numbers.

Two of the most useful free tools are already in your account.

The scheduler. Click Start a post, write it, and click the clock icon. LinkedIn Help says a personal profile can schedule from 10 minutes to 3 months ahead. It has no calendar view and does not suggest a time.

Post analytics. Under each of your posts, LinkedIn shows impressions, members reached, and engagement. Its Help page on post analytics says impression and engagement counts are kept for 1,000 days, members reached for 400 days, and demographic breakdowns for 180 days. The creator analytics page adds up all your posts over a date range you pick, and can be exported to a spreadsheet.

LinkedIn also runs Post Inspector, which refreshes the picture and title LinkedIn has saved for a link.

What it does not do: write, design, or tell you why one post did better than another.

3. Taplio

Best for: a few AI generators from a well-known LinkedIn tool.

Taplio's free tools page lists 11: an engagement rate calculator, a headline generator, a summary generator, a carousel generator, a post booster, profile feedback, a text formatter, a video downloader, a viral post generator, a tool that turns your profile into posts, and a page of LinkedIn trends.

The page does not say whether the tools need an account. The Taplio app itself is a paid product with a free trial.

4. Scripe

Best for: trying one AI draft with no sign-up.

Scripe's free tools page has one tool, a LinkedIn post generator. The page says it is free with no sign-up: you give it an idea, pick an angle, and get a draft. The rest of Scripe is a paid app with a 7-day trial.

5. Supergrow

Best for: a small set of free tools with no login.

Supergrow lists 8 free tools: a carousel generator, a meme generator, a hook generator, a video downloader, a summary generator, a text formatter, a post preview, and a headline generator. The page says no sign-up is needed. Four more are marked as coming soon.

6. MagicPost

Best for: checking a draft before you post it.

MagicPost lists 18 free tools and says none of them asks for an account. Eleven are for writing and formatting, including a text formatter, a hook generator, a headline generator, a quote card generator, and a post previewer. Six check a draft or a number: a post grader, a hook analyzer, a readability checker, a reach calculator, an engagement rate calculator, and a calculator for what a sponsored post is worth. The last one estimates what your team resharing a post would add to its reach.

7. AuthoredUp

Best for: formatting and benchmarks from a LinkedIn editor.

AuthoredUp lists 13 free tools: a text formatter, a post preview, a best times to post tool, a text staircase tool, a headline writer, an About writer, an engagement rate calculator, an industry finder, a weekly content mix planner, a hiring post audit, a company page benchmark, an image size checker, and a character counter.

The page does not say whether they need an account. The AuthoredUp editor itself is a paid product.

8. ContentIn

Best for: a wide set of free tools with no sign-up.

ContentIn's free tools page lists 20, and says every tool on it runs without a sign-up. They include a post generator, a post humanizer, a post optimizer, a carousel generator, a video downloader, a post preview, a headline generator and analyzer, a hashtag research tool, a character counter, a post date extractor, a formatter, and an emoji list. One of them, weekly post ideas, arrives by email, so that one needs your address.

The ContentIn app is a paid product with a 7-day free trial.

9. Canva

Best for: designing an image or a carousel by hand.

Canva is a general design app, not a LinkedIn tool. Its pricing page lists a Free plan at no cost for one person, and says Canva Free is available to anyone. You need an account. The page does not list what the Free plan includes for LinkedIn, so we will not guess.

If you only need the right size, our post image size page has every LinkedIn size in pixels, and the image resizer crops a picture to it.

10. Buffer

Best for: scheduling to LinkedIn and other networks on a free plan.

Buffer is a general scheduler. Its pricing page lists a Free plan, free forever, that connects up to 3 channels with 10 scheduled posts per channel. You need an account. It publishes posts you have already written.

For more schedulers, see twelve LinkedIn scheduling tools.

Which free tool set has which tool

The same ten jobs, across the seven LinkedIn tool sets on this list. A dash means the set's free tools page did not list one on 9 October 2026.

The best free LinkedIn tool for each job

Free AI tools for LinkedIn posts

Start with the post generator: give it a rough thought, a link, or a few bullet points, and it writes a post. If you already have a draft, the hook generator gives you several openings for it, and the post booster rewrites a post that did poorly and says why it changed each line. The hashtag generator picks three to five tags for it. Before you publish, the AI content detector points at the lines that read as generated.

Taplio, Scripe, and ContentIn also list a free post generator. Supergrow and MagicPost list a free hook generator.

Free LinkedIn formatting tools

LinkedIn's composer has no bold or italic button. A text formatter swaps your letters for bold and italic characters that survive pasting. The post preview shows where the feed cuts your post on a phone, and the character counter counts against the limit for a post, a headline, or a connection note.

If you pasted the draft from ChatGPT, run it through the invisible character remover first. It also takes out the Markdown asterisks.

Taplio, Supergrow, MagicPost, AuthoredUp, and ContentIn each list a free text formatter as well.

Free LinkedIn optimization tools for your profile

Three parts of a profile do most of the work. The headline generator turns a job title into a line that says what you do and who for. The About generator drafts the About section. The banner maker makes the background photo at 1584 by 396 pixels, and the profile picture resizer frames your photo in LinkedIn's circle.

For the sizes and what to put on a banner, see the LinkedIn banner size guide.

Free LinkedIn carousel generators

A LinkedIn carousel is a PDF. The carousel content generator writes the slides, the carousel generator turns an outline into the PDF with no watermark, and the carousel tester shows the finished file inside a post at phone width. The guide on making a LinkedIn carousel with AI walks through all three steps.

For a single image, the quote card generator turns one line into a feed-sized picture, and the post screenshot generator turns a post into a clean image, which is what most people mean by a LinkedIn post mockup.

Taplio, Supergrow, and ContentIn also list a free carousel generator. For paid and AI carousel makers too, see the best LinkedIn carousel makers.

Free LinkedIn analytics tools

LinkedIn's own post analytics is the free place to get the numbers. What it does not give you is a rate. The engagement rate calculator takes the impressions, reactions, comments, and reposts from a post and shows the rate next to the platform average. What a good LinkedIn engagement rate is explains how to read it.

To judge a draft before it has any numbers, the post analyzer grades it out of 100 and names the lines to rewrite.

Free LinkedIn scheduling tools

LinkedIn's own scheduler is free and enough for a finished post. The best time to post tool shows what three published studies found, converted to your own time zone, so you have a time to put in it.

Free LinkedIn outreach tools

The free tools here help you write a message. They do not send it. The connection request generator writes a note inside LinkedIn's 300 characters, the DM generator writes a first message, and the connection request limit calculator shows how many invitations you have left this week.

Free LinkedIn lead generation tools

A lead magnet is the usual way to turn a post into leads: a guide people ask for in the comments or by message. The lead magnet generator writes the guide from what you want it to teach and gives you a branded PDF you can edit. The outreach ROI calculator works back from invitations to meetings to customers, so you can see what your outreach is worth before you spend more time on it.

Free LinkedIn downloaders

For a public post, the video downloader saves the video, the carousel downloader saves the PDF or its slides, and post to PDF saves the whole post with its author and date. Taplio, Supergrow, and ContentIn also list a free video downloader.

Free LinkedIn tools by who you are

• Job seekers. Fix the profile first: the headline generator, the About generator, the experience generator, and the profile picture resizer.
• Creators. Write and check each post: the post generator, the hook generator, the post preview, and the carousel generator.
• Sales and founders. Start conversations by hand: the connection request generator, the DM generator, and the lead magnet generator.
• Agencies and ghostwriters. Check a client's work before it goes out: the carousel tester, the PDF metadata remover, and the engagement rate calculator for the report.

What about free LinkedIn automation tools?

Many searches for free LinkedIn tools are really for automation: software that sends connection requests, messages, or likes for you. None of the ten sets above does that, on purpose.

LinkedIn's User Agreement says members agree not to use bots or other unauthorized automated methods to add contacts, send messages, or comment on, like, or share posts. A free automation tool still puts your account at risk. Our glossary explains what counts as LinkedIn automation and what gets an account restricted.

Where free tools stop

A free tool does one job once. It does not know what you posted last month, which of your posts did well, or how you write. You carry that from tool to tool yourself.

That is the difference between these tools and Depost AI, the product. Depost AI reads your past posts and their numbers, builds a brand memory from them, and then plans, writes, and designs your posts in your voice. You approve each one before it publishes. The free tools stay free either way.

If you are comparing paid tools, 15 LinkedIn AI tools covers them.

FAQ

What are the best free LinkedIn tools?

Depost AI's 64 free tools cover the most jobs with no account: writing, formatting, profile, carousels, images, downloads, and analytics. LinkedIn's own scheduler and post analytics are free with your account. MagicPost, ContentIn, AuthoredUp, Taplio, and Supergrow each list between 8 and 20 free tools.

Are there free LinkedIn tools with no sign-up?

Yes. Depost AI, MagicPost, ContentIn, Supergrow, and Scripe all say their free tools need no account. Taplio and AuthoredUp do not say either way on their free tools pages.

Is there a free LinkedIn post generator?

Yes. Depost AI's post generator writes a post from a rough thought, a link, or bullet points, with no account. Taplio, Scripe, and ContentIn list free post generators too.

Is there a free LinkedIn formatting tool?

Yes. A text formatter turns your words into bold or italic characters you can paste into a post, because LinkedIn's composer has no formatting buttons. Depost AI, Taplio, Supergrow, MagicPost, AuthoredUp, and ContentIn each list a free one.

Is there a free LinkedIn analytics tool?

LinkedIn's own post analytics is free and shows impressions, members reached, and engagement for each post. A free engagement rate calculator turns those numbers into a rate you can compare. Depost AI, Taplio, MagicPost, and AuthoredUp each have one.

Is there a free LinkedIn scheduling tool?

Yes, LinkedIn's own scheduler. It is free and schedules a post from 10 minutes to 3 months ahead on a personal profile. Buffer's Free plan also schedules to LinkedIn, up to 10 scheduled posts per channel.

Is there a free LinkedIn carousel generator without a watermark?

Yes. Depost AI's carousel generator exports a PDF with your own photo or logo on the slides and no watermark, and needs no account.

Are free LinkedIn automation tools safe?

LinkedIn's User Agreement does not allow bots or other unauthorized automated methods for adding contacts, sending messages, or reacting to posts. That applies whether the tool is free or paid, and the account at risk is yours.

What is the difference between a free tool and a free trial?

A free tool does one job for as long as you want. A free trial gives you a paid app for a few days and then charges you. Taplio, Scripe, and ContentIn each have free tools and a separate trial of the paid app.

A LinkedIn management tool is software for planning, writing, approving, publishing, and reporting on LinkedIn posts from one place. Most were built for one person and one profile. This list covers eleven that an agency can run clients on, and compares them on the things that only matter once you have clients. It starts with ours.

It is about content. Tools that send connection requests and messages automatically are a different kind of tool, and there is a short section on them near the end.

Four questions that sort the list fast

Are clients kept apart? With one client, everything lives in one account. With ten, you need each client's drafts, voice, and reviewers in a separate space, so nothing from one client shows up in another's. Tools call this a workspace.

How do you get into the client's LinkedIn? LinkedIn's User Agreement says members will keep their password secret and will not share their account with anyone else. So the client should connect their own account to the tool through LinkedIn's sign-in, and you work inside the tool. If a tool needs the client's password, that is a reason to stop.

How does the client approve a post? Someone at the client has to say yes before a post goes out under their name. Check whether the tool has an approval step, whether the client needs a paid seat to use it, and whether the approval is recorded.

What does the next client cost? The price on the pricing page covers one client or one person. What matters to an agency is what the bill does at five clients and at ten.

How to manage multiple LinkedIn accounts for clients

LinkedIn allows one account per person. Its User Agreement says you will only have one LinkedIn account, in your real name. So an agency never has "multiple accounts" of its own. It has access to each client's account, and the safe way to set that up is the same in every tool on this list:

1. Create a workspace for the client. Their drafts, their brand, and their reviewers go here and nowhere else.
2. Ask the client to connect their own LinkedIn account. They sign in through LinkedIn's own sign-in screen and approve the tool. You never see the password, and they can remove the access at any time.
3. Set who does what. Decide who on your team drafts, who edits, and who at the client approves.
4. Send posts for approval before they are scheduled. A post goes out only after the client has said yes.
5. Report on each client separately. Use that client's own numbers, not an average across your clients.

What you should not do is keep client passwords in a shared spreadsheet and log in as each client from your own browser. It breaks LinkedIn's terms, and one leaked spreadsheet exposes every client at once.

The eleven at a glance

Every fact about the other ten tools is from the tool's own website, read on 7 October 2026. Prices are the monthly-billed price unless the entry says otherwise. Where a site does not state something, this list says so and does not guess.

1. Depost AI

Best for: agencies and ghostwriters who run LinkedIn personal profiles for clients, and want the posts planned, written, and designed for each client, with the client approving every one.

This is ours, so weigh it accordingly. Most tools on this list help your team write faster. Depost AI does the first draft of the whole week for each client, and your team edits and approves.

A workspace for each client

Each client gets their own workspace: their LinkedIn account, their posts, their reviewers, and their knowledge base. Clients do not see each other. Your team sees the workspaces you add them to.

The client connects their own LinkedIn account, so nobody shares a password. When a client leaves, you remove the workspace and that account comes off the next bill.

Each client's own voice

Depost AI reads the client's profile and the posts they have already published, and builds a brand memory for that client: their positioning, their voice, and every post with its numbers. It reads that memory before it writes anything. Client five does not sound like client two, because each one is learned separately.

Four agents work for each client:

• Performance Analyzer reads the client's own analytics and finds the posts that beat their average.
• Idea Generator suggests next week's ideas from those posts.
• Post Generator writes the drafts in the client's voice.
• Graphic Generator designs an infographic for each draft in the client's colors and fonts.

Approval the client can do

Nothing publishes until someone approves it. On the Team plan you get comments, review rounds, roles, and an audit trail, so there is a record of who approved what. The client reviews in their own workspace.

Clients, teammates, and freelancers are invited free. Asking a client to approve their own posts does not add a seat to the bill.

Approved posts publish through LinkedIn's official API, at the times you set for that client.

What it costs as you add clients

Team is $99 a month for the first LinkedIn account and $59 a month for each one after that, up to 30. Workspaces are unlimited. Three clients cost $217 a month, and ten cost $630. Above 30 accounts, the Enterprise plan is priced with you. There is a 7-day free trial, and the details are on the pricing page.

Who it is not for

• Agencies that post to company pages. Depost AI publishes to personal profiles.
• Agencies that schedule the same post to several networks. Depost AI publishes to LinkedIn only.

The agencies page shows the setup.

2. Taplio

What it is: a LinkedIn tool with writing help, a library of popular posts, and a page for agencies.

What it covers

• A separate workspace for each client, with permissions per client.
• Client approval: its agencies page says clients can keep an approval step on everything you schedule.
• Each client connects their own LinkedIn account, so no passwords are shared.
• Analytics for each client that you can export.

What to know

• Public plans are $39, $69, and $199 a month. The team and agency price is not on the page. It offers a discount of "up to 30%" on request.
• White-label reports are not offered as such. Its site says agencies export the numbers or pull them through its API into their own reports.
• It also offers automatic connection requests and messages. See the section on outreach automation below before you turn those on for a client.

3. Scripe

What it is: a LinkedIn content tool for teams, with a page for agencies.

What it covers

• Every client in their own workspace, with their own knowledge base and brand kit.
• An approval flow you set once and adjust for each client. The client approves in their own workspace or sends a draft back with a note.
• Each person connects their own LinkedIn profile through LinkedIn's sign-in.
• Company pages, from the Advanced plan.
• A CSV export of each client's numbers.

What to know

• Its pricing page shows yearly billing: €55, €79, and €119 a month. Extra full seats are €45 a month each from the Advanced plan, and multiple workspaces need the Business plan.
• Agency pricing with volume discounts is by request.
• To review and approve, a client user joins their workspace with a Client role.

4. Supergrow

What it is: a LinkedIn writing tool with a plan for teams.

What it covers

• Feedback and approval workflows on the Teams plan.
• A voice profile for each team member.
• One content calendar across all members, and analytics for the whole team.
• An organization dashboard, which its site says is free for the admin.

What to know

• Teams is $199 a month and includes 5 accounts. For more than 5, you contact them.
• The plan is described for a company's own executives and team members. Its pricing page does not mention client management.
• It can post a first comment automatically after a post goes out.

5. MagicPost

What it is: a LinkedIn writing tool with a plan made for agencies and ghostwriters.

What it covers

• An Agency plan where one seat is one client's LinkedIn account, either a personal profile or a company page.
• Post approval by the client, and a client calendar you can share for approval.
• White-label reports.
• Admin-only seats for your own team at no charge.

What to know

• The Agency plan is $59 a month per seat, so ten clients cost $590 a month.
• Post generation, visuals, and scheduling are included for every seat.
• You run the writing workflow yourself for each client, one step at a time.

6. AuthoredUp

What it is: a LinkedIn editor for people who write the posts themselves.

What it covers

• An editor with text styling, a preview, and saved drafts and snippets.
• Company pages on every plan at no extra cost.
• An organization with Admin, Manager, and Member roles on the Business plan.
• Billing per connected LinkedIn profile. Its site says managers and ghostwriters who do not connect their own profile are free.

What to know

• Business is $14.95 a month per profile, with a minimum of 3 profiles. Ten clients cost $149.50 a month.
• There is no approval workflow. You can share a read-only link to a draft for review.
• It works through a browser extension, and you write every post. Its AI writing feature is in closed beta.

7. ContentIn

What it is: a LinkedIn writing tool for founders, with a plan for ghostwriters and agencies.

What it covers

• A Ghostwriter / Agency plan with multiple LinkedIn profiles and team seats.
• Approval and review flows on that plan.
• Company pages on the Pro plan.

What to know

• The Ghostwriter / Agency plan has no public price. You talk to them.
• The public plans, $39 and $48 a month, cover one personal profile.
• Its pricing page does not say how clients are kept apart.

8. Sendible

What it is: a general social media scheduler built for agencies.

What it covers

• A workspace for each client or brand, each with 6 social profiles.
• Unlimited users on every plan, including clients and freelancers.
• Client Connect, which lets a client connect their profiles without giving you a password.
• Assignment and approval workflows from the Plus plan.
• Reports for each network.

What to know

• Plans go up by the number of workspaces: 1 on Core, 3 on Plus, and 7 on Premium.
• White labeling, with your own branding on the dashboard and reports, is a paid add-on.
• It publishes to LinkedIn and other networks. Your team writes the posts.

9. Agorapulse

What it is: a social media suite with an inbox, reports, and shared calendars.

What it covers

• Shared calendars that let people outside your team review posts before they publish. The Advanced plan includes 5.
• One-step approval workflows, where a post is sent to a teammate or a client. Approval chains with several reviewers are on the Custom plan.
• Reports you can export with your branding.
• LinkedIn company pages and personal profiles, plus other networks.

What to know

• Pricing is per user: $99, $149, and $199 a month.
• Every person on your team who needs access adds to the bill.
• Your team writes the posts.

10. Buffer

What it is: a general social media scheduler priced per channel.

What it covers

• LinkedIn profiles and company pages, plus other networks.
• Unlimited team members on the Team plan.
• Content approval workflows and access levels on the Team plan, so you choose who can draft, who can approve, and which channels each person sees.

What to know

• Team is from $12 a month per channel, and its site says the price per channel goes down as you add more. Ten client profiles cost up to $120 a month.
• Its pricing page does not mention a workspace per client. You control access channel by channel.
• Your team writes the posts.

11. Hootsuite

What it is: a social media suite for larger teams.

What it covers

• LinkedIn and other networks from one dashboard.
• Review and approval of content with your team on the Advanced plan.
• Message assignment and team performance reports.

What to know

• Pricing is per user. Its site says paid plans start at $99, and its pricing page shows prices on annual billing.
• Its plans page lists review and approval under the Advanced plan.
• Your team writes the posts.

What ten clients cost

Four of the eleven publish a price you can multiply. For ten client profiles:

• Buffer, Team: 10 channels at up to $12, so up to $120 a month. You get scheduling and approvals. Your team writes everything.
• AuthoredUp, Business: 10 profiles at $14.95, so $149.50 a month. You get an editor and analytics. Your team writes everything.
• MagicPost, Agency: 10 seats at $59, so $590 a month. You get writing help, client approval, and white-label reports.
• Depost AI, Team: $99 plus 9 at $59, so $630 a month. The posts and graphics are drafted for each client, and reviewers are free.

Depost AI is not the cheapest of the four. The difference is who does the writing. With the first two, you pay for a place to publish from, and your team still writes every post. If writing for ten clients takes one person most of their week, that time costs more than any of these plans.

For the per-user tools, the bill depends on your team, not your clients. Three people on Agorapulse's Professional plan is $447 a month.

What about outreach automation tools?

If you search for LinkedIn tools for agencies, most lists you find are about a different kind of tool: software that sends connection requests and messages for a client automatically.

Read LinkedIn's rules before you use one on a client's account. Its User Agreement says members agree not to use bots or other unauthorized automated methods to add contacts, send messages, or comment on, like, or share posts. LinkedIn Help also has a page on prohibited software and extensions. Our glossary explains what counts as LinkedIn automation.

The account at risk is your client's, not yours. What gets a LinkedIn account restricted explains what LinkedIn acts on. None of the eleven tools above is on this list for automated outreach.

Tools left off this list

Kleo and Stanley are popular with solo creators. Neither site shows a team plan, client workspaces, or an approval step, so they are not here. They are covered in Kleo alternatives and Stanley alternatives.

If scheduling is the only job, twelve LinkedIn scheduling tools compares more general schedulers. For a side-by-side look at Depost AI and any LinkedIn tool above, see the comparison pages.

Free tools that help with client work

Two jobs before a client's carousel goes up do not need a paid tool. The carousel tester shows the PDF inside a post at phone width, so you can check it before you send it for approval. The PDF metadata remover takes your name and your working file name out of the file. Both are free and need no account. For the rest, see the best free LinkedIn tools.

FAQ

What is the best LinkedIn management tool for agencies?

Depost AI, for agencies that run personal profiles. Each client has a separate workspace, the posts are drafted in that client's voice with a graphic, and the client approves them free of charge. It costs $99 a month for the first account and $59 for each one after. For company pages or several networks, Sendible or Buffer fits better.

What is a LinkedIn management tool?

Software for planning, writing, approving, publishing, and reporting on LinkedIn posts from one place. General ones, such as Buffer and Hootsuite, cover several networks and schedule posts your team writes. LinkedIn-only ones, such as Depost AI and MagicPost, also help write the posts.

Can you manage multiple LinkedIn accounts?

Yes, if each account belongs to a different person and that person connects it to your tool. LinkedIn allows one account per person, so you cannot hold several of your own. An agency manages each client's account from a separate workspace, after the client connects it through LinkedIn's sign-in.

How do agencies manage multiple LinkedIn accounts?

With a tool where each client connects their own LinkedIn account through LinkedIn's sign-in, and the agency works inside a separate workspace for that client. This avoids shared passwords, which LinkedIn's User Agreement does not allow, and it keeps one client's drafts away from another's.

Can an agency log in to a client's LinkedIn account?

LinkedIn's User Agreement says members will keep their password secret and will not share their account with anyone else. Use a tool the client connects their account to instead. The client stays in control and can disconnect the tool at any time.

How do clients approve LinkedIn posts?

In most tools on this list, the client reviews a draft and approves it or sends it back with a comment, and the post only publishes after that. Check whether the client needs a paid seat. In Depost AI, reviewers are invited free, and each approval is recorded in an audit trail.

How much do LinkedIn tools for agencies cost?

For ten client profiles, the published prices run from about $120 a month on Buffer's Team plan to $630 a month on Depost AI's Team plan. The cheaper tools schedule posts your team writes. Agorapulse and Hootsuite charge per user, and Taplio, Scripe, and ContentIn price their agency plans on request.

Is there a white-label LinkedIn tool for agencies?

MagicPost lists white-label reports on its Agency plan, and Sendible sells white labeling as a paid add-on. Taplio says agencies export its numbers into their own reports.

Do these tools work with company pages?

Some do. MagicPost, Scripe, AuthoredUp, ContentIn, Agorapulse, and Buffer list company pages on their sites. Depost AI publishes to personal profiles only.

If LinkedIn is where your customers are, you need things a message thread does not give you: a view of which posts worked, a graphic with each post, a way to find the right conversations, and a place where a client or a teammate can sign off. Eleven tools, then, starting with ours. What each one covers beyond the draft, and what you would be giving up. If you are choosing from the whole category and not replacing one product, the 15 best LinkedIn AI tools is the wider list.

Three questions that sort the list fast

Stanley comes from Stan, the company behind Stan Store. It used to be a web app for LinkedIn posts only. That app now shows a notice that Stanley has moved, and sends you to a new Stanley that works across platforms through iMessage, Telegram, WhatsApp, or the web.

People on LinkedIn start looking for an alternative when one of these three answers no longer fits.

Is it built for LinkedIn, or for every platform? A tool for every platform treats a LinkedIn post, a reel, and a tweet as the same job. A tool built for LinkedIn knows your LinkedIn numbers, designs graphics for the feed, and helps you with comments and leads there. Stanley is now the first kind. Depost AI is the second.

What does it remember? Most tools learn how you write and stop there. A tool that also holds your positioning, everything you have published and how it performed, the posts you saved, and the documents you care about will write a third draft that is better than its first. A tool that forgets will not.

Who needs to see the post before it goes out? If it is only you, a reply in a thread is enough. If a client, a manager, or a legal reviewer has to approve it, you need review rounds, comments, and a record of who approved what.

The eleven at a glance

Every entry links to a comparison page that carries current pricing. Prices move often, and they are rarely the reason people make this change.

1. Depost AI

Best for: creators, B2B teams, and agencies whose customers are on LinkedIn, and who want the week planned, written, and designed for them, with approval on every post.

This is ours, so weigh it accordingly. The case against Stanley is not a longer feature list. It is that Depost AI does one platform in depth, and that it already knows your LinkedIn account when you start.

Connect LinkedIn and Depost AI builds the brand memory for you: your positioning, a voice learned from your published posts and refreshed every week, each post with its real numbers, and the posts you save. You do not type any of it in. You can add documents and notes for it to read, or leave that out. Every agent reads the memory before it writes, and a Sources panel on each draft shows what it used.

What a week looks like

Every tool on this list writes posts. Where they differ is what happens before and after the draft, and who does it.

It reads your LinkedIn numbers. Once you connect, your history comes in: impressions, engagement rate, follower growth, and every post you have published. The Analyst picks out the posts that beat your own average and explains why: the hook, the format, the topic, the time. Ask "what worked last month?" in chat and you get the answer with the posts next to it.

It plans from them. The ideas for next week begin with what worked on your account. The inspiration library and your saved posts come second. Popular posts from other people show what worked for them. Your own results show what works for your audience.

It writes and designs. Each draft is in your voice and comes with an infographic in your colors and fonts and a first comment ready to post. You can start from a voice note, a bookmark, a case study, or a call transcript.

It publishes after you approve. You approve, edit, or ask for changes. Approved posts are scheduled for a time suggested from when your audience actually engages, and they publish through LinkedIn's official API. If a post fails to publish, you get an email and can retry or reschedule it.

It helps you reply. You name the prospects, niches, and topics, and Depost AI builds feeds around them across LinkedIn, X, Reddit, Threads, and Facebook. It drafts replies, DMs, and connection notes from what each person actually posted. Lead lists come with stages, notes, and reminders. You are the one who presses send.

What that does for the business

• Your plan improves week by week. Each plan begins with the posts that beat your average the week before, so you repeat what your audience responds to.
• Replying takes less time. Drafts of the replies are waiting when you sit down, so you edit and send. You do not start from an empty box.
• Commenters become leads. A person who comments twice goes onto a lead list, with a stage, a note, and a follow-up reminder.
• Your profile gets checked too. After you connect, your profile is scored one section at a time, so visitors who arrive from a post find a complete profile.
• Reviewers cost nothing. On Team you can invite clients, assistants, and reviewers for free, with review rounds, threaded comments, roles per brand, and an audit trail.
• You can use it from Claude and ChatGPT. An MCP server comes with every plan, with its own key per brand. Your AI assistant can read your analytics, put drafts on your calendar, and list what is waiting for review.

Ready for a creator, a B2B team, or an agency

Stanley is made for one creator and one thread. Its site shows no approvals, no client access, and no team plan. Depost AI is set up for three kinds of customer.

• Creators. Solo covers one LinkedIn account and has every feature, so one person gets the same planning, graphics, and replies a team gets.
• B2B teams. Each founder, executive, or salesperson connects their own profile and gets their own voice and brand memory. Marketing or legal reviews each post first, with roles and an audit trail.
• Agencies. Every client is a separate brand with its own memory, calendar, and MCP key. Clients are invited free and approve in review rounds, and one Team plan holds up to 30 accounts. For a wider look, see eleven LinkedIn management tools for agencies.

What it costs as you grow

Solo is $59 a month. It covers one LinkedIn account and has every feature. Team is $99 a month for the first account and $59 for each added account, up to 30. People are free on Team, so ten client brands come to $630 a month no matter how many people work on them. Both plans start with a 7-day free trial, and yearly billing gives you two months free. The pricing page has the details.

Stanley is $79 a month. It has no pricing page, so you see the price at checkout, and its site shows no team plan.

Moving from Stanley

• Your history is on LinkedIn. Your posts live on LinkedIn, not in Stanley. About fifteen minutes after you connect, Depost AI drafts a brief from your profile and your last fifty or more posts, covering your positioning, topics, audience, and voice. Fix what is wrong and approve the rest. Analytics are complete from day one.
• You do not train a voice. It is learned from what you have already published. There is nothing to paste in or teach again.
• Voice notes still work. If you are used to sending Stanley a voice note, you can do the same here, and it becomes a draft with a graphic.
• Saved posts carry over. Posts you save on LinkedIn or in Depost AI are used by the planner, next to your own results.
• Scheduled posts stay behind. No two tools transfer them. Anything Stanley has scheduled for you needs to be created again, which takes an afternoon.

Who it is not for

• Anyone who posts to Instagram, X, and LinkedIn from one place. Depost AI publishes to LinkedIn only. Its feeds and saved posts also cover X, Reddit, Threads, and Facebook.
• Anyone who wants posts to go out without reading them. Every post, reply, and message waits for your approval, on purpose.

The full comparison with Stanley puts the features side by side. The free trial runs seven days on both plans. Read the brief it drafts in the first fifteen minutes, and you will know whether it sounds like you.

2. Kleo

What it is: a paid web app for writing a LinkedIn post and getting a graphic for it, for one profile.

What it covers

• Post writing and graphics.

What to know

• It does not cover feeds, replies, approvals, or MCP.
• It has one plan for one profile and no free trial. More profiles are listed as coming soon.
• The free Chrome extension people remember was shut down in June 2025.

Depost AI vs Kleo.

3. Taplio

What it is: a set of separate features: writing help, a library of posts, carousels, a lead feed, analytics, and scheduling. You open each one and run it yourself.

What it covers

• Carousels, a lead feed, AI comments, analytics, and MCP.

What to know

• It does not plan or draft the week for you. You work through it one feature at a time.
• Auto-connect and DM sequences go out under your name, including when you are not watching.
• There are no approvals, and each login is a paid seat, so reviewers and clients add to the bill.

Depost AI vs Taplio.

4. Scripe

What it is: a tool that makes posts from recordings, such as a podcast episode, a webinar, or a voice note.

What it covers

• Posts written from recordings and transcripts.
• Approvals, with unlimited users on the Advanced plan.
• Company pages and personal profiles.

What to know

• Most of the product assumes you record content. If you do not, there is less here for you.
• There are no graphics, no feeds, and no drafted replies.

Depost AI vs Scripe.

5. Supergrow

What it is: a writing tool that asks you questions to find ideas. It has templates and a feed of posts, and its Teams plan adds approvals and MCP.

What it covers

• Graphics, with approvals and MCP on the Teams plan. Feeds and replies are covered in part.

What to know

• Four accounts come with the Teams plan. After that you pay for each seat, so cost rises with headcount.
• It posts the first comment automatically, before you have reviewed it.
• It assists at each step. The steps are still yours to do.

Depost AI vs Supergrow.

6. MagicPost

What it is: a writing tool where you fill in forms to get a post. Clients approve from a link in the browser.

What it covers

• Client approval by link, with no login needed.
• Feeds, MCP, and more than one brand.

What to know

• Image generation is in beta.
• Replies are not drafted for you, so each comment is yours to write.
• You work in one account at a time, so agencies manage every client on its own.

Depost AI vs MagicPost.

7. Saywhat

What it is: a drafting tool sold together with strategy sessions, feedback on drafts, and a private community.

What it covers

• Strategy sessions, feedback on what you write, and a community.
• Feeds and drafted replies, in part.

What to know

• It has no graphics, approvals, MCP, or support for several brands.
• Usage is capped on each plan, so heavier use moves you up a plan.

Depost AI vs Saywhat.

8. Postwise

What it is: a scheduler built for X that can also publish to LinkedIn and Threads.

What it covers

• A single schedule across X, LinkedIn, and Threads.
• Basic post drafting.

What to know

• X comes first in the product, so LinkedIn gets less attention.
• It has drafting and scheduling only: no graphics, feeds, replies, or approvals.

Depost AI vs Postwise.

9. Oiti

What it is: a writing tool that reads your posts and your website to build a persona, and stores a knowledge base.

What it covers

• A persona built from your content, without a questionnaire.
• Graphics, and a knowledge base your drafts can draw on.

What to know

• It is for writing posts. It will not find posts for you to reply to, and there are no approvals or MCP.
• Oiti says it keeps your corrections. If you try it, compare a later draft with an early one to see if it still sounds like you.

Depost AI vs Oiti.

10. AuthoredUp

What it is: a formatting editor with analytics on the posts you have published. By design, it does not write posts.

What it covers

• Formatting, phone and desktop previews, and analytics on past posts. Pricing is per profile.

What to know

• There is no AI writing and there are no graphics.
• There are no feeds and no drafted replies, so you will use it next to other tools.

Depost AI vs AuthoredUp.

11. ContentIn

What it is: a writing tool with a big library of templates. It picks up your voice from how you edit its drafts.

What it covers

• Templates, graphics, and MCP. Feeds and replies are covered in part.

What to know

• Your voice is learned from your edits, not your published posts, so the early weeks take more editing.
• Plans are sold by usage, so a heavy month can move you up a plan. Team features are limited.

Depost AI vs ContentIn.

When the switch is worth it

When LinkedIn is the platform that brings you customers, and you want a tool that does that one platform in depth. When you want next week planned from your own LinkedIn results. When a client, a teammate, or a second brand arrives and someone else needs to approve the post. When you want each post to arrive with its graphic.

The one real cost is the scheduled posts, covered under moving from Stanley above. Everything else comes with you, because it lives on LinkedIn and not in the tool.

What the other lists get wrong

Three things to keep in mind when you read the other results for this search.

Many reviews describe the old Stanley. Most Stanley reviews were written about the LinkedIn-only web app. That app now shows a notice that Stanley has moved. Check the date on any review, and check Stanley's own site before you rely on a feature list.

The prices do not agree. Reviews published this year quote $97 or $149 a month, which were prices for the old LinkedIn app. Stanley has no pricing page. The checkout price was $79 a month when we read it in September 2026.

Vendor lists rank the vendor first. Almost every page that ranks for this search was written by a company selling a Stanley alternative, and each one puts itself at number one. This one does too, and says so in the first line. The ranking is not the useful part. Look for a page that shows what each tool covers, so you can check its claims against a table.

FAQ

What is the best Stanley alternative for LinkedIn?

Depost AI. It is built for LinkedIn only, and it is the one tool here that remembers your brand between sessions and covers all the work around the post: ideas from your own results, a graphic with each draft, replies drafted for you, and approvals for clients and teammates. Nothing publishes until you approve it. The other ten are listed above by what each one covers.

What happened to Stanley for LinkedIn?

It moved. The LinkedIn-only web app now shows a notice that Stanley has moved and points to a new Stanley that works across platforms through iMessage, Telegram, WhatsApp, or the web. The notice still has a log-in link for existing Stanley for LinkedIn accounts. We checked this on 5 October 2026.

How much does Stanley cost?

$79 a month, when we read it at checkout in September 2026. Stanley has no pricing page, and older reviews quote $97 or $149 for the LinkedIn app it replaced. Depost AI is $59 a month for one LinkedIn account, or $99 a month for a team's first account, both with a 7-day free trial.

Is there a free Stanley alternative?

Not a full one. Every tool in this category charges, because AI drafting costs money to run. What is free is the single jobs: our free LinkedIn tools cover post ideas, hooks, post templates, formatting, and post previews, with no account.

What does brand memory mean, and why does it matter?

Brand memory is what a tool knows about you from one session to the next. Depost AI builds it from the LinkedIn account you connect: your positioning, your voice, each post with its numbers, the posts you save, and any documents you add. All of it is read before a draft is written. Without it, you explain who you are every time, and the drafts do not get better.

Which Stanley alternative is safest for my account?

Ask any tool two things: what it sends without you, and how it publishes. A tool that publishes through LinkedIn's official API and waits for a person to approve each post, reply, and message is a lower risk than one that connects, comments, or messages on its own. What gets a LinkedIn account restricted explains where the line is, and how many comments a day is safe covers volume.

Stanley vs Kleo: which is better for LinkedIn?

Neither is built for a team. Stanley is an assistant you message, for creators who post on several platforms. Kleo is a web app for writing a LinkedIn post and its graphic, for one profile. Both are made for one person. Neither plans your week from your own LinkedIn results or gives a client or a manager a place to approve a post, which is what decides this list.

The hard part is having something to schedule. Most people who go looking for a scheduling tool do not have a timing problem. They have an empty calendar, a draft that still needs a graphic, or a client who has not approved next week's posts. So this list looks at what each tool does before the post goes out, and at how it publishes. Twelve tools, starting with ours.

Three questions that sort the list fast

Who writes the post? Some tools publish a post you wrote somewhere else. Some help you write it. A few plan the week, write the posts, and design the graphics, then wait for you to approve them. All of them are called schedulers, but they are different products.

How does the post reach LinkedIn? Some tools publish through LinkedIn's official API. Others publish through a browser extension that acts inside your logged-in LinkedIn session. The tools that use the API usually say so on their site. If a site does not say, ask before you connect your account.

Who approves it? If you work alone, nobody needs to. If you write for a founder, a client, or a team, you need a way for them to review and approve a post before it publishes. Many tools keep this for a higher plan.

Start with LinkedIn's own scheduler

Yes, you can schedule posts on LinkedIn itself, for free. Click Start a post, write the post, and click the clock icon. Choose a date and time, then click Schedule.

What LinkedIn's Help pages say about it:

• Personal profiles: you can schedule from 10 minutes to 3 months ahead. Events, jobs, and services posts cannot be scheduled.
• Company pages: you can schedule from 1 hour to 3 months ahead. Polls, posts with multiple photos, reshares, events, jobs, and services posts cannot be scheduled.
• To find your scheduled posts: click Start a post, then the clock icon, then View all scheduled posts. From there you can edit a post, change its time, or delete it.

If you write your own posts and only need them to go out at a set time, this is enough. It has no calendar view, does not suggest a time, and has no way for someone else to approve a post. That is where the tools below come in.

The twelve at a glance

Every fact about the other eleven tools is from the tool's own website, read on 28 September 2026. Where a site does not mention something, this list says so and does not guess.

1. Depost AI

Best for: founders, B2B teams, and agencies who want the calendar filled for them, and who approve every post before it goes out.

This is ours, so weigh it accordingly. Most scheduling tools start when the post is finished. Depost AI starts before there is a post.

Connect LinkedIn and it reads your profile and your published posts, and builds a brand memory: your positioning, your voice, every post with its numbers, and the posts you save. It reads that memory before it writes anything.

What happens before a post is scheduled

It suggests what to post. Your posts sync with their impressions and engagement. Depost AI finds the ones that did better than your average and suggests next week's ideas from them.

It writes and designs. Drafts are written in your voice. Each one comes with an infographic in your colors and fonts, and a first comment written and ready for you to post.

You approve. Nothing goes into the calendar until you, or the reviewer you choose, approves it. On Team, clients and teammates review with comments, review rounds, and roles, and they are invited free.

How scheduling works

• Posting times you set once. Choose the days and times you want to post. Put an approved post in the next open slot, or pick an exact date and time when it matters.
• Your best times, from your own data. Analytics shows the time slots when your audience engages most, so you can set your posting times from them.
• Every common post type. Text, images, video, PDF carousels, and polls.
• Official API. Posts are published through LinkedIn's official API.
• A calendar you can rearrange. See drafts, scheduled posts, and published posts in one place. Drag a post to another day to move it. Empty slots are shown, so you can see where next week is thin.
• An email if a post fails. If a post cannot be published, you get an email, and you can retry it or reschedule it.
• One calendar for every brand. On Team, the calendar covers every brand in the workspace, and you can filter by brand or label.

The scheduling page has the details.

If you schedule for clients, eleven LinkedIn management tools for agencies compares workspaces, client approval, and what each new client costs.

What it costs as you grow

Solo is $59 a month for one LinkedIn account and includes every feature. Team is $99 a month for the first account and $59 for each one after that, up to 30, and the people you invite are free. Both plans have a 7-day free trial. Details are on the pricing page.

Who it is not for

• Anyone who schedules the same post to several networks. Depost AI publishes to LinkedIn only.
• Anyone who posts from a company page. Depost AI publishes to personal profiles.

2. Buffer

What it is: a general social media scheduler.

What it covers

• LinkedIn profiles and company pages, plus 11 other networks.
• Text, image, GIF, PDF, video, link, and carousel posts.
• A scheduled first comment on paid plans.
• A free plan with up to 3 channels and 10 scheduled posts per channel.

What to know

• Approval workflows are on the Team plan only.
• Its site does not mention suggested posting times for your account.
• Paid plans start at $6 a month per channel when billed monthly.

3. Publer

What it is: a general social media scheduler.

What it covers

• LinkedIn profiles and company pages, plus 12 other networks.
• Text, links, photos, video, GIFs, PDF carousels, and polls.
• A scheduled first comment on paid plans.
• A free plan with 3 social accounts and 10 scheduled posts per account.

What to know

• Best times to post are on the Business plan only.
• Approval workflows need paid extra members.
• Paid plans start at $5 a month for one social account when billed monthly, before VAT.

4. SocialBee

What it is: a general scheduler that groups posts into content categories.

What it covers

• LinkedIn profiles and company pages, plus 9 other networks.
• Text, single and multiple images, video, and carousels from PDF or PowerPoint files.
• A scheduled first comment, and suggested posting times based on your best posts.

What to know

• There is no free plan. There is a 14-day trial.
• Post approval starts on the Accelerate plan, not the entry plan.
• Plans start at $29 a month when billed monthly, for up to 5 social profiles and one user.

5. RecurPost

What it is: a general scheduler that can repeat posts on a cycle.

What it covers

• LinkedIn profiles and company pages, plus other networks. Its site says it publishes through LinkedIn's official API.
• Text, image, multiple images, video, document, and link posts.
• A first comment, and posting times worked out from each account's own engagement.

What to know

• Its site says it cannot schedule LinkedIn polls or articles.
• There is no free plan. There is a 14-day trial.
• Approvals are on the Agency plan only.
• Plans start at $9 a month when billed monthly, for 2 social accounts.

6. Hootsuite

What it is: a social media suite built for larger teams that manage many networks.

What it covers

• LinkedIn scheduling next to your other networks, with a best time to publish feature.
• Analytics for LinkedIn profile and page posts.

What to know

• There is no free plan. There is a 14-day trial.
• Plans are sold per user. The lowest is $99 per user a month, and that price is for annual billing. Its plans page does not show a monthly-billed price.
• Approvals are on the Advanced plan.
• Its LinkedIn page does not list which post types it can schedule.

7. Taplio

What it is: a LinkedIn tool with writing help, a post library, carousels, a lead feed, and a scheduler.

What it covers

• LinkedIn profiles and company pages.
• Text, images, documents, videos, and carousels.
• Posting times suggested from when your followers engage.

What to know

• Its site does not say how posts reach LinkedIn.
• It has no approval workflow. Growth and Teams plans let you share drafts with writers.
• It also offers automated comments and outreach that run from your account.

Depost AI vs Taplio.

8. Supergrow

What it is: a LinkedIn writing tool with a calendar and a board for tracking drafts.

What it covers

• Scheduling with a calendar view on every plan. Its site says it connects through LinkedIn's official API.
• A first comment that is published with the post.

What to know

• Approval workflows are on the Teams plan, which includes five accounts.
• Its site does not mention company pages or list the post types it can schedule.

Depost AI vs Supergrow.

9. MagicPost

What it is: a LinkedIn writing tool where clients approve posts from their calendar.

What it covers

• Text, images, GIFs, video, PDFs, carousels, and polls. Its site says it uses LinkedIn's official API and no browser extension.
• Scheduled comments that go out right after the post, or after a delay you choose.

What to know

• One seat is one LinkedIn account, either a profile or a company page.
• Its suggested posting times are an average for your country, not for your own audience.
• Client approval is on the Agency and Team plans.

Depost AI vs MagicPost.

10. ContentIn

What it is: a LinkedIn writing tool with a template library and a weekly calendar.

What it covers

• Images, GIFs, video, and carousels. Its site says it publishes through LinkedIn's official API.
• A scheduled first comment.

What to know

• Company pages are on the Pro plan only.
• It does not suggest posting times. Its site says posting at a consistent time matters more.
• There is no free plan. There is a 7-day trial, and it asks for a card.

Depost AI vs ContentIn.

11. Scripe

What it is: a tool that turns recordings and voice notes into LinkedIn posts.

What it covers

• Scheduling and people tagging on every plan. Its site says it posts through LinkedIn's official API.
• Approval workflows on the Advanced plan.

What to know

• Company pages start on the Advanced plan.
• Its site has no page about scheduling, so post types and posting times are not described.
• It is built around recordings. If you do not record content, most of the product is not for you.

Depost AI vs Scripe.

12. AuthoredUp

What it is: a LinkedIn editor and analytics tool that runs as a browser extension. It does not write posts.

What it covers

• Scheduling to profiles, company pages, and groups, up to three months ahead.
• Images, GIFs, video, documents, polls, and events.
• A best time to post worked out from your own posts.

What to know

• Its site says scheduling runs through its browser extension.
• It has no approval workflow. You can share a read-only link to a draft.
• You write every post yourself.

Depost AI vs AuthoredUp.

Free LinkedIn scheduling tools

LinkedIn's own scheduler is the free option anyone can use, for posts up to three months ahead.

Two of the twelve tools have a free plan, and both limit you to 10 scheduled posts per account. The others offer a free trial instead.

Some jobs around scheduling are free too. Our best time to post tool shows three published studies on your own clock, the post preview shows where the feed cuts your post, and the carousel generator makes the PDF. None of them needs an account. The full list is in the best free LinkedIn tools.

What the other lists get wrong

Three things to check when you read other lists of scheduling tools.

The first price you see is often the yearly one. Buffer, Taplio, MagicPost, and Scripe all open their pricing pages on yearly billing. Paying month to month costs more. Every price in this list is the monthly-billed price, except where the entry says otherwise.

Official API claims are often second-hand. Some lists say which tools use LinkedIn's official API without a source. Only the tool itself can tell you. This list reports it only where the tool's own site says so.

Vendor lists rank the vendor first. Almost every list of scheduling tools was written by a company that sells one, and puts its own product at number one. This one does too, and says so. Check what each tool covers, not where it is ranked.

FAQ

What is the best LinkedIn scheduling tool?

Depost AI. It does the work before scheduling as well: it suggests ideas from your best posts, writes the drafts in your voice, designs the graphics, and waits for your approval. Approved posts publish through LinkedIn's official API at the times you set. If you only need to publish a finished post, LinkedIn's own scheduler is free.

Can you schedule posts on LinkedIn?

Yes. Click Start a post, write the post, then click the clock icon and choose a date and time. On a personal profile you can schedule from 10 minutes to 3 months ahead. On a company page, from 1 hour to 3 months ahead.

Is there a free LinkedIn scheduling tool?

Yes, LinkedIn's own scheduler. It is free and schedules up to three months ahead. It has no calendar view, does not suggest times, and has no approval step.

Where do I find my scheduled posts on LinkedIn?

Click Start a post, then the clock icon, then View all scheduled posts. Click the More icon on a post to edit it, change its time, or delete it. For a company page, open the Page admin view and go to Page posts.

Is it safe to schedule LinkedIn posts with a third-party tool?

Ask how the tool publishes. A tool that uses LinkedIn's official API connects through LinkedIn's own sign-in and can only do what LinkedIn allows. A tool that works through a browser extension acts inside your logged-in session. What gets a LinkedIn account restricted explains the risks.

Can you schedule carousels and polls on LinkedIn?

On a personal profile, LinkedIn's Help page lists only events, jobs, and services as posts that cannot be scheduled. On a company page, polls and posts with multiple photos cannot be scheduled either. Depost AI schedules PDF carousels and polls. How to make a LinkedIn carousel covers the file itself.

What is the best time to schedule a LinkedIn post?

The time your own audience is active, which differs by account. Best time to post on LinkedIn compares three published studies and shows how to test your own times over four weeks.

That is fine until the day you want that work done for you as well. Eleven tools, then, starting with ours. What each one covers beyond writing the post, and what you would be giving up. If you are shopping the whole category rather than replacing one product, the 15 best LinkedIn AI tools is the wider list.

Three questions that sort the list fast

Kleo began as a free Chrome extension for finding post ideas. LinkedIn asked for it to be removed in June 2025, and Kleo came back as a paid web app for writing posts and designing graphics, for one profile.

People start looking for an alternative when one of these three answers is no longer good enough.

Who does the work? A tool that helps you with each step is a different product from one that does the steps and hands you the result to approve. Kleo is the first kind. Depost AI is the second. You should know which one you are buying, because the daily experience is nothing alike.

What does it remember? Most tools learn your writing style and stop there. A tool that also holds your positioning, what you have already published and how it performed, the posts you saved, and the documents you care about will write a third draft that is better than its first. A tool that forgets will not.

What happens after the draft? Writing the post is one job. Knowing which posts worked, finding the conversations worth joining, replying to comments, and getting approval from a client are the rest of the week. Some tools cover them. Many stop at the draft.

The eleven at a glance

Each entry links to a comparison page with current pricing. Prices change often, and price is rarely what decides this choice.

1. Depost AI

Best for: creators, B2B teams, and agencies who want their LinkedIn week planned, written, and designed for them, and who approve everything before it goes out.

This is ours, so weigh it accordingly. The argument against Kleo is not a longer feature list. It is that the work is done for you, and that the tool already knows how you write and what has worked for you.

Connect LinkedIn and Depost AI builds the brand memory itself: your positioning, a voice learned from what you have published and refreshed weekly, every post with its real numbers, and the posts you save. Nothing has to be typed in by hand. Add documents and notes if you want them read, or do not. Every agent reads that memory before it writes anything, and a Sources panel shows you what each draft drew on.

What a week looks like

All eleven tools write posts. They differ in what happens before and after the draft, and in who does that work.

It reads your numbers. Connect LinkedIn and your history syncs: impressions, engagement rate, follower growth, and every post you have published. The Analyst finds the posts that did better than your own average and works out why: the hook, the format, the topic, the time. Ask "what worked last month?" in chat and the answer comes with the posts attached.

It plans from them. Next week's ideas start from what worked on your account. The inspiration library and the posts you saved are a second source, not the first. A library of popular posts shows you what worked for someone else. This starts from what worked for you.

It writes and designs. Drafts are written in your voice, each with its infographic designed in your colors and type and its first comment already written. A voice note becomes a post. So does a bookmark, a case study, or a call transcript.

It publishes when you approve. Approve, edit, or request changes. Approved posts are scheduled for a time suggested from when your audience actually engages, and go out through LinkedIn's official API. If a publish fails, you get an email and can retry or reschedule it.

It keeps you in the conversation. Targeted feeds around the prospects, niches, and topics you name, across LinkedIn, X, Reddit, Threads, and Facebook. Replies, DMs, and connection notes are drafted from what the person actually posted. Lead lists have stages, notes, and reminders. You press send, every time.

What that does for the business

• Each week builds on the last. The plan starts from the posts that did better than your average the week before, so you do more of what works for your audience.
• The hour after publishing gets shorter. The replies are drafted when you sit down, so that hour is spent sending and editing, not writing from nothing.
• Comments turn into leads. Someone who comments twice goes on a lead list with a stage, a note, and a reminder to follow up.
• The profile does its part. Connect the account and your profile is scored section by section, so the people your posts send there find one that is finished.
• Extra people are free. On Team, you invite clients, assistants, and reviewers at no cost, with review rounds, threaded comments, roles per brand, and an audit trail.
• It works inside Claude and ChatGPT. Every plan includes an MCP server, with a separate key for each brand, so your AI assistant can read your analytics, add drafts to your calendar, and show what is waiting for review.

Who it is built for: creators, B2B teams, and agencies

Kleo is for one person and one profile. It has no approvals, and a second profile is not available yet. Depost AI works for all three of these.

• Creators. One account on Solo, with nothing held back for a higher plan.
• B2B teams. Founders, executives, and salespeople each connect their own profile, and each gets a separate voice and brand memory. A marketing or legal reviewer signs off before a post goes out.
• Agencies. Each client brand is kept apart, with its own memory, calendar, and MCP key. The client reviews and approves at no extra cost, and a single Team plan covers up to 30 accounts. For a wider look, see eleven LinkedIn management tools for agencies.

What it costs as you grow

Solo is $59 a month for one LinkedIn account and includes every feature. Team is $99 a month for the first account and $59 for each one after that, up to 30. People are free on Team, so ten client brands cost $630 a month however many people work on them. Both plans have a 7-day free trial, and paying yearly gives you two months free. Details are on the pricing page.

Kleo has one plan for one profile, and no free trial. Its pricing page lists support for more profiles as coming soon, so today there is no way to add a second brand or a client account.

Moving from Kleo

• Your history comes with you. Your posts are stored on LinkedIn, not in Kleo. About fifteen minutes after you connect, Depost AI has drafted a brief with your positioning, topics, audience, and voice, based on your profile and your last fifty or more posts. Correct what is wrong and approve the rest. Your analytics are complete from the first day.
• There is no voice to train. The voice is learned from what you have published, so nothing has to be pasted in or taught again.
• Your saved posts still count. Save posts on LinkedIn or in Depost AI and the planner uses them, next to your own results.
• Scheduled posts do not move. They do not transfer between any two tools. Anything already scheduled in Kleo gets re-created, which is an afternoon, not a week.

Who it is not for

• Anyone who wants posts to go out without reading them first. Every post, reply, and message waits for your approval, on purpose.
• Anyone who schedules the same post to five networks. Depost AI publishes to LinkedIn only. Feeds and saved posts also cover X, Reddit, Threads, and Facebook.

The full comparison with Kleo has the feature table side by side. The free trial is seven days on either plan, and the brief it drafts from your account in the first fifteen minutes is the fastest way to know whether it sounds like you.

2. Taplio

What it is: a long menu of features: writing help, a post library, carousels, a lead feed, analytics, and scheduling, each one a feature you open and operate.

What it covers

• Carousels, a lead feed, AI comments, analytics, and MCP.

What to know

• You operate it feature by feature. Nothing is planned or drafted for you.
• Auto-connect and DM sequences run under your name, whether or not you were watching.
• No approvals, and it bills by the seat, so every reviewer and client with a login is a line on the invoice.

Depost AI vs Taplio.

3. Stanley

What it is: an assistant you message over iMessage, Telegram, WhatsApp, or the web. It used to be a LinkedIn-only web app, and it now works across several platforms.

What it covers

• Drafting, scheduling, and posting from a message thread.

What to know

• It is made for one creator on several platforms, not for LinkedIn in depth.
• Its site shows no graphics, approvals, or team plan.
• It has no pricing page. You see the price at checkout.

Depost AI vs Stanley.

4. Scripe

What it is: a tool that turns recordings into posts. You give it a podcast episode, a webinar, or a voice note.

What it covers

• Posts from recordings and transcripts.
• Approvals, and unlimited users on the Advanced plan.
• Company pages as well as personal profiles.

What to know

• It is built around recordings. If you do not record content, most of the product is not for you.
• It has no graphics, feeds, or drafted replies.

Depost AI vs Scripe.

5. Supergrow

What it is: a writing tool that interviews you to find ideas, with templates, a feed of posts, and approvals and MCP on its Teams plan.

What it covers

• Graphics, plus approvals and MCP on the Teams plan. Feeds and replies are partly covered.

What to know

• The Teams plan includes five accounts. For more than five, you contact them.
• The first comment is posted automatically. You do not review it first.
• It helps with each step, but you still do each step yourself.

Depost AI vs Supergrow.

6. MagicPost

What it is: a writing tool built on forms, where clients approve posts through a link in their browser.

What it covers

• Clients approve through a link and do not need a login.
• Feeds, MCP, and multiple brands.

What to know

• Image generation is still in beta.
• It does not draft replies, so you write every comment yourself.
• It works one account at a time, so an agency handles each client separately.

Depost AI vs MagicPost.

7. Saywhat

What it is: drafting software that comes with strategy sessions, feedback on your drafts, and a private community.

What it covers

• Strategy sessions, a community, and feedback on your writing.
• Feeds and drafted replies are partly covered.

What to know

• No graphics, approvals, MCP, or multi-brand support.
• Each plan limits how much you can use, so heavy use means a higher plan.

Depost AI vs Saywhat.

8. Postwise

What it is: a scheduler made for X that can also post to LinkedIn and Threads.

What it covers

• One schedule for X, LinkedIn, and Threads.
• Simple post drafting.

What to know

• It is built for X first, so it does less for LinkedIn.
• It drafts and schedules. There are no graphics, feeds, replies, or approvals.

Depost AI vs Postwise.

9. Oiti

What it is: a writing tool that builds a persona from your posts and your website, and keeps a knowledge base.

What it covers

• A persona made from your own content, with no questionnaire.
• A knowledge base that drafts can use, and graphics.

What to know

• It writes posts. It does not find posts to reply to, and it has no approvals or MCP.
• Oiti says it remembers your corrections. If you try it, check that later drafts still sound like you.

Depost AI vs Oiti.

10. AuthoredUp

What it is: an editor for formatting posts, with analytics on your past posts. It does not write posts, by design.

What it covers

• Formatting, previews for phone and desktop, and analytics on your past posts. You pay per profile.

What to know

• It does not write for you, and it has no graphics.
• It has no feeds or drafted replies, so you need other tools next to it.

Depost AI vs AuthoredUp.

11. ContentIn

What it is: a writing tool with a large template library. It learns your voice from the edits you make.

What it covers

• Templates, graphics, and MCP. Feeds and replies are partly covered.

What to know

• It learns from your edits, not from the posts you have already published, so expect more editing in the first weeks.
• Plans are based on usage, so a busy month can push you to a higher plan. Team features are limited.

Depost AI vs ContentIn.

When the switch is worth it

When you want the week planned from your own results, not from templates and other people's posts. When a client, a teammate, or a second brand arrives and one profile is no longer enough. When you are tired of doing everything around the post yourself.

The one real cost is the scheduled posts, covered under moving from Kleo above. Everything else comes with you, because it lives on LinkedIn rather than in the tool.

What the other lists get wrong

Three patterns are worth knowing when you read the rest of the results.

Generic scheduler lists are not LinkedIn lists. Several pages answer this search with Buffer, Hootsuite, and other social schedulers. Those are general social schedulers. None of them writes a LinkedIn post in your voice, reads your LinkedIn analytics, or drafts a reply.

Many of them have the date wrong. Several pages say LinkedIn had the Kleo extension removed in early 2026. The founder's own announcement is from June 2025. Check the date on anything you read about Kleo, because reviews from before the shutdown describe a different product.

Vendor lists rank the vendor first. Almost every page ranking for this search was written by somebody selling a Kleo alternative, and every one of them puts itself at number one. This one does too, and says so in the first line. The useful signal is not the ranking, it is whether the page shows what each tool actually covers, so you can check the claim against the table instead of taking it on trust.

FAQ

What is the best Kleo alternative?

Depost AI. It is the only one here that does the work around the post and remembers your brand between sessions, so the ideas come from what has already worked on your account, the graphic arrives with the post, and replies are drafted and waiting. Nothing publishes until you approve it. The other ten are listed above by what each one covers, so you can see what you would be giving up.

Is there a free Kleo alternative?

Not a full one. Everything in this category charges, because the drafting costs money to run, and Kleo itself no longer has a free plan. What is free is the individual jobs: our free LinkedIn tools cover post ideas, hooks, post templates, formatting, and post previews with no account.

What happened to the Kleo Chrome extension?

It shut down in June 2025. Kleo's founder, Jake Ward, announced it on 20 June 2025 and said LinkedIn had told them to. Kleo came back as a paid web app. The subscription includes a new extension, but it is for saving posts you like, not the free tool people remember.

What does brand memory mean, and why does it matter?

Brand memory is what a tool knows about you from one session to the next. Depost AI builds it from the LinkedIn account you connect: your positioning, your voice, every post with its numbers, the posts you save, and any documents you add. It reads all of that before it writes. Without it, you explain who you are every time you open the tool, and the drafts do not improve.

Which Kleo alternative is safest for my account?

Ask any tool what it sends without you, and how it publishes. Tools that publish through LinkedIn's official API and need a human to approve each post, connection, and message sit in a different risk category from tools running auto-connect and DM sequences on your behalf. What gets a LinkedIn account restricted covers where the line is, and how many comments a day is safe covers the volume side.

Kleo vs Taplio: which is better?

They are the same kind of tool: one you operate yourself. Kleo is drafting and graphics for one profile. Taplio is a longer menu with a lead feed and automated outreach. Neither plans the week from your own results or keeps a brand memory between sessions, which is the difference that decides this list.

We checked. We asked ChatGPT and Claude for the same eight LinkedIn posts, recorded exactly what their copy buttons put on the clipboard, and looked for every hidden character there is. Here is what OpenAI says, what we found, and what actually travels with a pasted draft.

Does ChatGPT watermark text?

Not yet, by OpenAI's own account. Its help page on provenance signals lists what carries a watermark today: images made with ChatGPT carry Content Credentials and a SynthID watermark, and generated audio carries SynthID. Text is not on the list.

The same page says OpenAI's goal is to extend these signals to all kinds of content, text included, in line with its commitments under the European Commission's Code of Practice on Transparency of AI-generated content. So the honest answer for September 2026 is: no text watermark today, one announced as a goal.

OpenAI does have a method. In an August 2024 update to its content provenance post, OpenAI said its teams had developed a text watermarking method that was highly accurate and held up against paraphrasing. It chose not to ship it, for two reasons it gave: the mark was easy to get around by translating the text or rewording it with another model, and it risked stigmatizing people who use AI as a writing aid, such as non-native English speakers.

Images are different. A picture made in ChatGPT carries Content Credentials in the file and a SynthID watermark in the pixels, and the Content Credentials are why LinkedIn shows a CR tag on it.

Where the hidden character rumor came from

In April 2025, Rumi reported that longer answers from OpenAI's o3 and o4-mini models contained narrow no-break spaces, characters that look exactly like a normal space, and suggested they were a watermark. OpenAI said they were not a watermark but a side effect of how the models were trained, and later output stopped showing them.

The story stuck, and it is why people still search for a "ChatGPT watermark remover" for text. It is also why we wanted to check today's output rather than repeat last year's.

Does ChatGPT add hidden characters? We checked 16 answers

On 23 September 2026 we sent the same eight prompts to ChatGPT, in a temporary chat on its default model, and to Claude, in an incognito chat on Opus 5.5. All eight asked for LinkedIn content: an announcement with a list, tips with bullet points, a short story post, a post with statistics, a welcome post, a carousel outline, a pros and cons post, and a rewrite of a funding announcement.

For each answer we recorded what the copy button put on the clipboard and the text shown on screen, then counted every hidden character: zero-width spaces and joiners, no-break and narrow no-break spaces, soft hyphens, byte order marks, direction marks, tag characters, and stray variation selectors.

Not one hidden character in 16 answers, on the clipboard or on screen. The only non-printing character anywhere was the selector inside a ❤️ emoji in one Claude answer, which is part of the emoji. Eight answers from each is a small sample from one day, so read it as a snapshot of September 2026, not a rule for every model.

What actually comes along when you copy from ChatGPT

The copy button is where the surprises are. Both chats write in Markdown, where **two asterisks** make a phrase bold and a dash starts a list item, and both copy buttons hand that Markdown over as the plain text version of what you copied. They also put a formatted version on the clipboard, and the app you paste into decides which one it takes.

LinkedIn does not read Markdown. Wherever the plain text version lands in a post, the asterisks and dashes show as they are. That gives a pasted draft away far more plainly than any watermark could.

The other differences were in the punctuation. ChatGPT used em dashes in seven answers out of eight and curly quotes in all eight. Claude used neither, and wrote its lists with • bullets rather than Markdown dashes. None of that is hidden, and none of it is a watermark. The swap option in the remover replaces the dashes and quotes if you want plain ones.

Does Claude watermark text?

Yes, for new models. In August 2026 Anthropic announced that text from Claude models launched on or after 2 August 2026 carries a watermark, with older models following over the coming months. It is done to meet the EU AI Act, whose marking rules for AI-generated content apply from that date, and Anthropic says the other major developers who signed the same code will add their own.

Anthropic is specific about what the watermark is not: nothing is added to the text, and there are no hidden characters, which matches what we found.

Two more details from the same announcement. Code carries less of the watermark, because code has fewer free word choices, though comments inside it can carry it. And files Claude produces, such as PNG, JPG, and SVG images, get C2PA Content Credentials in their metadata, the same kind of record that makes LinkedIn show a CR tag on an image.

How text watermarks work

A text watermark lives in the wording. Each time a model picks the next word, it usually has several that would do equally well. A watermarked model settles those small choices with a secret key instead of pure chance, which leaves a statistical pattern across a long enough passage. Anyone with the key can measure it; no reader can see it.

Claude's method is a version of SynthID-Text, which Google DeepMind published and which Google uses in Gemini. Because the signal is in which words were chosen, removing characters, fixing punctuation, or converting Markdown does not touch it. Anthropic says light editing probably will not remove it completely, and a complete rewrite in your own words will, at which point the text is arguably no longer AI-written.

It is also different from an AI detector. A detector guesses from style, the phrasing habits models tend to share, without any key. A watermark check needs the key, and for Claude that check is currently a detection API in private preview for regulators, researchers, and other eligible organizations.

Is there a ChatGPT watermark detector?

Not for text, because there is no ChatGPT text watermark to detect yet. What exists today checks for other things:

A site that promises to detect a "ChatGPT watermark" in text is really doing one of the last two. Ours are the invisible character remover for hidden characters and the AI content detector for style, which quotes the lines that read as generated instead of giving a percentage.

How to remove hidden characters from ChatGPT text

If you came here for a ChatGPT watermark remover for text: there is no watermark in the characters to remove, and no tool can remove a watermark that lives in word choice. What you can clean are the hidden characters a draft picks up on the way. Web pages use no-break spaces and soft hyphens to control line breaks, word processors add their own, and they come along with anything you copy.

1. Paste the draft into the invisible character remover. It checks the text in your browser, with nothing uploaded.
2. Read the count. Every hidden character is listed by name, and a marked copy of your text shows where each one sits. Joiners that hold an emoji together are kept and listed as kept.
3. Check the Markdown box if the draft came from a copy button. Bold becomes the bold letters LinkedIn keeps, headings become bold lines, and list dashes become bullets. There is a separate box for em dashes and curly quotes.
4. Copy the text and paste it into LinkedIn.

The same check runs inside the character counter and the post preview: paste a draft with hidden characters and they offer to remove them.

In Microsoft Word, Find and Replace can do part of it: ^s finds no-break spaces, and ^u followed by a character's decimal number finds the rest, such as ^u8203 for a zero-width space and ^u8239 for a narrow no-break space. Replace each with a normal space or with nothing. It works once you know which characters are there, which is what the checker tells you.

Should you hide that a post was drafted with AI?

Cleaning the characters and the Markdown is about the post reading properly. Asterisks in a LinkedIn post look like a mistake, whoever wrote it.

Hiding that a post was drafted with AI is a different question, and the tools will not settle it for you. Watermarks that live in word choice cannot be cleaned out, detectors judge the wording, and readers judge it too. What works on LinkedIn is a post that sounds like you: your examples, your numbers, your opinion. If a model helped, rewrite its draft until that is true, and it stops mattering where the first version came from.

FAQ

Does ChatGPT leave a watermark when you copy and paste?

Not in the text, as of September 2026. OpenAI lists watermarks for ChatGPT images and audio but not text. In our test of eight ChatGPT answers, the copied text had no hidden characters. It did carry Markdown asterisks and dashes, which show up as plain symbols in a LinkedIn post.

Can you tell if ChatGPT wrote something?

Not from a watermark in ChatGPT's text, because there is none yet. AI detectors guess from the wording, and people notice patterns like Markdown symbols, heavy em dash use, and generic phrasing. What reliably changes that is editing the draft into your own words.

Does ChatGPT watermark code?

No. OpenAI lists no watermark for text of any kind, code included. Claude's text watermark does reach code, but lightly: code leaves the model few free word choices, so most of the mark sits in comments.

Does Claude watermark text like Gemini?

Yes. Since August 2026, text from new Claude models carries a watermark based on SynthID-Text, the same family Google uses in Gemini. Both work through word choice, with no hidden characters, so no character cleaner removes them.

Do ChatGPT images have a watermark?

Yes. Images made with ChatGPT carry C2PA Content Credentials in the file and a SynthID watermark in the pixels. LinkedIn reads the Content Credentials and shows a CR tag, which the AI metadata remover can remove. The SynthID watermark stays in the picture.

How do I remove the asterisks from ChatGPT text?

Paste it into the invisible character remover and check the Markdown box. Bold phrases become Unicode bold letters, which LinkedIn keeps, and headings and list dashes are converted too. The LinkedIn text formatter does the same bold by hand.

Will OpenAI add a watermark to ChatGPT text?

It says so. OpenAI's help page names text as part of its goal to expand provenance signals, citing the EU's Code of Practice on AI-generated content. When it arrives, expect it to work like Claude's and Gemini's watermarks, through word choice.

The tag is not LinkedIn looking at your picture and deciding it looks generated. It is LinkedIn reading a note that the generator left inside the file, which means you can check for it before you post and remove it.

What the CR tag on LinkedIn means

CR stands for Content Credentials. LinkedIn's help page on Content Credentials explains that it adopted C2PA, an industry standard for recording where a piece of media came from, and that images and videos "cryptographically signed using C2PA Content Credentials" get the icon.

Clicking the tag opens a panel with what the credentials say. LinkedIn lists what it can show:

• An assertion about the media: whether AI was used to generate or edit any part of it.
• The app or device used: the AI tool, or the camera model for a photo.
• Created by: the person who generated it, when the credentials name one.
• Issued by: who created and signed the credential.
• Issued on: the date and time it was signed.

Two things follow from that page. The tag is not only for AI: LinkedIn's own examples include photos captured by cameras and stories from news organizations. And LinkedIn says plainly that it cannot yet identify and label all AI-generated content. It labels what arrives with credentials attached.

Why the CR tag appears on your image

The credentials are saved inside the image file, next to the picture, the same way a phone photo stores the camera it was taken with. You cannot see them by looking at the image, but LinkedIn reads them the moment you upload it, and that is when the "Content Credentials label added" notice appears.

Which tools write them in:

When we tested a portrait made in ChatGPT, the PNG it downloaded as carried credentials signed by "OpenAI Media Service API", marked as made with AI. That is exactly what LinkedIn would have shown behind the tag.

How to check if an image has Content Credentials

You can see what a file carries before LinkedIn does. Drop it into the AI metadata remover. It reads the file in your browser, without uploading it anywhere, and lists what is inside:

• Content Credentials, and who signed them
• Whether they mark the image as made or edited with AI
• EXIF data such as the camera, and any GPS location
• Prompts and settings some AI tools write into PNG files

For an image made with ChatGPT, OpenAI Verify is a second check: it looks for OpenAI's own Content Credentials and SynthID watermark.

If there are no credentials, LinkedIn has nothing to show a CR tag for, and you can post the original as it is. Nothing has to be downloaded to check.

How to remove the CR tag from a LinkedIn image

1. Drop the image into the AI metadata remover. JPG, PNG, WebP, MP4, and MOV all work, several at once.
2. Check what it found. The card under each file lists what was removed, and reads the cleaned file back to confirm nothing is left.
3. Download the clean copy and upload that one to LinkedIn. No credentials, no tag.

The tool cuts the metadata out of the file rather than saving the image again, so the pixels, the color profile, and a photo's rotation stay exactly as they were. For a PNG, re-saving would be lossless anyway. For a JPEG, it is not: in our test, saving a C2PA-signed photo again at quality 90 changed 13.6% of its pixels.

LinkedIn reads the file when you upload it. For a post that is already live, the dependable fix is to post the clean image again.

A carousel is a PDF, and a PDF carries details of its own: the author name, the app that made it, and the dates. The PDF metadata remover takes those out the same way, without changing the pages.

What removing the CR tag does not remove

Removing the credentials removes the note, not the facts behind it.

• It does not remove watermarks in the pixels. OpenAI says ChatGPT images carry a SynthID watermark alongside Content Credentials, and Google uses SynthID too. Those live in the picture itself, and no metadata tool touches them. OpenAI Verify may still find the watermark in a ChatGPT image after its metadata is gone, even though LinkedIn shows no tag.
• It does not remove visible marks. A logo or badge drawn onto the image has to be cropped.
• It does not make an AI image a photo. If it matters to your reader that an image was made with AI, a line in the post says so better than any tag.

Should you remove the CR tag?

Sometimes the tag helps you. A photographer whose camera signs their work, or a newsroom posting verified images, wants the credentials to stay: the tag is the proof.

For a post graphic, an infographic, or an illustration, the tag mostly invites the wrong question. Readers click it expecting a verdict on whether the content is trustworthy, and get a note about the tool the chart was drawn in. Removing it is reasonable there, and so is saying in the post that you made the graphic with AI.

Your profile photo is a separate case with the same logic: if you generated it, the profile picture resizer exports a fresh square with no metadata at all.

FAQ

What does "Content Credentials label added" mean on LinkedIn?

It means the file you uploaded carries Content Credentials, and LinkedIn will show the CR icon on it once you post. It is not a warning and nothing is wrong with your post. To post the image without the label, remove the credentials from the file and upload the clean copy instead.

What does CR mean on LinkedIn?

CR stands for Content Credentials. LinkedIn shows it on images and videos that carry a signed C2PA record, which says what made the file and whether AI was involved. Clicking it shows the app or device, who issued the credential, and any statement about AI use.

Does the CR tag mean LinkedIn detected AI?

No. It means the file arrived with Content Credentials attached, and LinkedIn is displaying them. LinkedIn's own help page says it cannot yet label all AI-generated content. An AI image without credentials gets no tag, and a camera photo with credentials does.

Why does my ChatGPT image show a CR tag on LinkedIn?

ChatGPT images carry C2PA Content Credentials signed by OpenAI, which record that the image was made with AI. LinkedIn reads them on upload and shows the tag. Removing the credentials from the file before you post removes the tag.

How do I remove the CR tag from a LinkedIn post that is already published?

LinkedIn reads the credentials from the file you uploaded, so the reliable way is to clean the image, then post it again. Check the new file first to confirm no credentials are left.

Does removing Content Credentials reduce image quality?

Not if the metadata is cut out of the file, which is how the AI metadata remover works: the pixels are identical afterward. Re-saving the image also removes it, and costs nothing for a PNG but changes pixels in a JPEG or WebP.

How do I remove CR from a ChatGPT image?

Drop the image into the AI metadata remover and download the clean copy. It removes the credentials OpenAI signed into the file, and leaves the picture exactly as ChatGPT made it.

Can I remove CR from a video?

Yes. LinkedIn shows the tag on videos with Content Credentials too. The same tool removes them from MP4 and MOV files without re-encoding the video.

Eleven tools, then, starting with ours. What each one covers beyond writing the post, and what you would be giving up. If you are choosing from every LinkedIn tool and not only replacing Taplio, the 15 best LinkedIn AI tools is the wider list.

Three questions that sort the list fast

Taplio has writing help, a post library, carousels, a lead feed, analytics, and scheduling. You open each feature and run it yourself.

People leave when one of these three answers stops working for them.

Who does the work? A tool where you run each feature is different from a tool that does the steps and gives you the result to approve. Taplio is the first kind. Depost AI is the second. Know which one you are buying, because using them day to day is very different.

What does it remember? Most tools start from nothing each time you open them. You paste in your background, get a draft, and the tool forgets it. A tool that keeps your positioning, your voice, what you have published and how it performed, and your documents will write a third draft that is better than its first. A tool that forgets will not.

What does it send without you? Auto-connect and DM sequences go out under your name, including when you are not watching. Check for this before you connect any tool.

The eleven at a glance

Pricing is on each comparison page linked from the entries below. Prices change often, and price is rarely the reason people switch.

1. Depost AI

Best for: creators, B2B teams, and agencies who want the week planned, drafted, and designed for them, with approval on every step.

This is ours, so weigh it accordingly. The reason to switch from Taplio is not the feature list. It is that the work is done for you, and that the tool already knows how you write.

Connect LinkedIn and Depost AI builds the brand memory on its own: your positioning, a voice learned from your published posts and updated every week, every post with its real numbers, and the posts you save. You do not have to type anything in. You can add documents and notes for it to read if you want. Every agent reads that memory before it writes, and a Sources panel shows what each draft was based on.

What a week looks like

All of these tools can write a post. They differ in what happens before and after the draft, and in who has to do that part.

It reads your numbers. When you connect LinkedIn, your history is imported: impressions, engagement rate, follower growth, and every post you have published. The Analyst finds the posts that beat your own average and works out why: the hook, the format, the topic, the time. Ask "what worked last month?" in chat, and the answer includes the posts.

It plans from them. Next week's ideas start from those posts. The inspiration library and the posts you saved come after that. Taplio's post library shows what worked for other people. Depost AI starts from what worked for you.

It writes and designs. Each draft is written in your voice, with an infographic in your colors and fonts and a first comment already written. A voice note, a bookmark, a case study, or a call transcript can each become a post. You do not need a separate design tool.

It publishes when you say yes. You approve, edit, or request changes. Approved posts are scheduled for a time suggested from when your audience actually engages, and they go out through LinkedIn's official API. If a publish fails, you get an email and can retry or reschedule it.

It helps you reply. You choose the prospects, niches, and topics, and Depost AI builds feeds for them across LinkedIn, X, Reddit, Threads, and Facebook. It drafts replies, DMs, and connection notes based on what the person posted. Lead lists have stages, notes, and reminders. You send every message yourself.

What that does for the business

• The plan gets better each week. Every week starts from the posts that beat your average the week before, so you write more of what your audience responds to.
• You spend less time replying after you post. The replies are already drafted when you sit down. You edit them and send them.
• Comments become leads. A person who has commented twice is saved as a lead, with a stage, a note, and a reminder to follow up.
• Your profile is checked. When you connect, each section of your profile gets a score, so people who click through from a post see a finished profile.
• A second person costs nothing. On Team, the client who approves, the assistant who drafts, and the reviewer who signs off are all invited free, with review rounds, threaded comments, roles per brand, and an audit trail.
• It works in the AI assistant you already use. Every plan has an MCP server, with one key for each brand, so Claude, ChatGPT, or Cursor can read your analytics, add drafts to your calendar, and tell you what is waiting for review.

One tool for creators, B2B teams, and agencies

Taplio has no approvals and charges for every login, so it fits one person better than a team. Depost AI is built for all three.

• Creators. Solo is one account with the full product.
• B2B teams. Every founder, executive, or salesperson has their own profile, voice, and brand memory in the same workspace. Marketing or legal approves before anything is published.
• Agencies. Clients are separate brands, each with its own memory, calendar, and MCP key. Clients approve for free, and the Team plan goes up to 30 accounts. For a wider look, see eleven LinkedIn management tools for agencies.

What it costs as you grow

Solo is $59 a month for one LinkedIn account, with every feature included. Team is $99 a month for the first account and $59 for each extra account, up to 30. You can have as many workspaces as you need, and people are free. Ten client brands cost $630 a month, whether three people work on them or thirty. Both plans have a 7-day free trial, and paying yearly gives you two months free. The full details are on the pricing page.

Taplio charges for each seat, so every reviewer and every client with a login adds to the bill. Compare that total, not the starting price.

Moving from Taplio

• Your history comes with you. Your posts are stored on LinkedIn, not in Taplio. Connect your account, and about fifteen minutes later you have a brief with your positioning, topics, audience, and voice, written from your profile and your last fifty or more posts. Correct what is wrong and approve the rest. Your analytics are complete from the first day.
• Scheduled posts do not move. No two tools transfer them. Anything already scheduled in Taplio has to be created again, which takes an afternoon, not a week.
• Carousels become graphics. Taplio gives you templates to fill in. Depost AI designs the infographic for you, in the shape the post needs, and attaches it to the draft.
• Auto-connect and DM sequences are not replaced. This is on purpose. You get lead lists with stages and reminders, and researched notes that you send yourself.
• MCP is still there. Both tools have it. In Depost AI, each brand has its own key, and MCP is on every plan.

Who it is not for

• Anyone who wants the tool to post, connect, or message while they are away. Everything in Depost AI waits for your approval, on purpose, and it has no auto-connect or DM sequences. If that is the Taplio feature you want to replace, no tool on this list replaces it safely.
• Anyone who schedules the same posts to five networks. Depost AI publishes to LinkedIn only. Its feeds and saved posts also cover X, Reddit, Threads, and Facebook.

The full comparison with Taplio has the feature table side by side. The free trial is seven days on either plan. The brief it writes from your account in the first fifteen minutes will show you whether it sounds like you.

2. Kleo

What it is: a tool that writes a post and makes a graphic to go with it.

What it covers

• Drafts and graphics in one place.

What to know

• It does not sell analytics or team features.
• It has no feeds, replies, or approvals, so you do everything after the draft yourself.
• It handles one brand at a time, and it has no MCP.

Depost AI vs Kleo.

3. Stanley

What it is: an assistant you text. The LinkedIn-only app has moved to a new Stanley that runs in iMessage, Telegram, WhatsApp, or on the web, for more than one platform.

What it covers

• Drafting, scheduling, and posting, all from a message thread.

What to know

• Built for a single creator across platforms. LinkedIn is one of several.
• No graphics, approvals, or team plan on its site.
• No public pricing page. The price appears at checkout, which some teams cannot buy against.

Depost AI vs Stanley.

4. Scripe

What it is: a tool that makes posts from recordings. You upload a podcast episode, a webinar, or a voice note, and it writes posts from it.

What it covers

• Posts from recordings and transcripts.
• Approvals, and unlimited users on the Advanced plan.
• Company pages as well as personal profiles.

What to know

• If you do not record anything, the main feature is of no use to you.
• No graphics, no feeds, no drafted replies.

Depost AI vs Scripe.

5. Supergrow

What it is: a writing tool that interviews you to get ideas. It also has templates and a feed of posts, with approvals and MCP on the Teams plan.

What it covers

• Graphics, approvals, and MCP on the Teams plan, with partial feeds and replies.

What to know

• The Teams plan covers five accounts. For more than five, you contact them.
• Its first comment is posted automatically, with no review step.
• It helps you with each step, but you still do the steps.

Depost AI vs Supergrow.

6. MagicPost

What it is: a writing tool based on forms. Clients approve posts through a link in the browser.

What it covers

• Client approval through a link, with no login for the client.
• Feeds, MCP, and multiple brands.

What to know

• Generated images are still in beta.
• It does not draft replies, so you write your own comments.
• It works on one account at a time, so an agency handles each client separately.

Depost AI vs MagicPost.

7. Saywhat

What it is: drafting software sold with strategy sessions, feedback on your drafts, and a private community.

What it covers

• Sessions, a community, and feedback on your writing.
• Partial feeds and drafted replies.

What to know

• No graphics, approvals, MCP, or multi-brand support.
• Each plan has usage limits, so using it more means paying for a higher plan.

Depost AI vs Saywhat.

8. Postwise

What it is: a scheduler for X that also posts to LinkedIn and Threads.

What it covers

• One schedule for X, LinkedIn, and Threads.
• Quick post drafts.

What to know

• It is made for X first, so it has fewer LinkedIn features.
• It only drafts and schedules. There are no graphics, feeds, replies, or approvals.

Depost AI vs Postwise.

9. Oiti

What it is: a writing tool that builds a persona from your posts and your website, and keeps a knowledge base for it.

What it covers

• A persona built from your own material, with no questionnaire.
• A knowledge base the drafts can use, and graphics.

What to know

• The persona writes posts. It does not find posts to reply to, and there are no approvals or MCP.
• Oiti says it remembers your feedback. If you try it, check whether the third draft still sounds like you.

Depost AI vs Oiti.

10. AuthoredUp

What it is: an editor for formatting posts, with analytics on your past posts. It does not write posts, and that is on purpose.

What it covers

• Formatting, device previews, and analytics on your past posts, billed per profile.

What to know

• It does not write anything for you. If you struggle to start a post, it will not help with that.
• It has no feeds and no drafted replies, so you will need other tools as well.

Depost AI vs AuthoredUp.

11. ContentIn

What it is: a ghostwriting tool built around a library of templates. It picks up your voice from your edits.

What it covers

• Templates, graphics, and MCP, with partial feeds and replies.

What to know

• It learns from your edits and not from your published posts, so you will edit more in the first weeks.
• Plans are based on usage, so a busy week can move you to a higher plan. It has few team features.

Depost AI vs ContentIn.

When the switch is worth it

When a second person joins and your bill is about to double. When you want the work done for you and not a list of features to run. When you are tired of telling the tool who you are every morning.

The one real cost is your scheduled posts, covered under moving from Taplio above. Everything else comes with you, because it is stored on LinkedIn and not in the tool.

What the other lists get wrong

Two things to know when you read the other results for this search.

General review sites do not list LinkedIn tools. The big review sites answer this search with Zoho Social, Buffer, Hootsuite, and Sprout Social. Those are general social schedulers. None of them writes a LinkedIn post in your voice, reads your LinkedIn analytics, or drafts a reply, and that is what you used Taplio for. A high rating for a different kind of product does not tell you much.

Vendor lists rank the vendor first. Every page that ranks for this search was written by a company selling a Taplio alternative, and each one puts itself at number one. This one does too, and says so in the first line. Do not go by the ranking. Look at whether the page shows what each tool covers, so you can check the claim against the table.

FAQ

What is the best Taplio alternative?

Depost AI. It is the only one here that does the work for you and remembers your brand between sessions. The ideas come from what has already worked on your account, each post comes with its graphic, and replies are drafted for you. It is built for creators, B2B teams, and agencies. Nothing publishes, connects, or messages until you approve it, and you pay per LinkedIn account, not per seat. The other ten are listed by what each one covers, so you can see what you would be giving up.

Is there a free Taplio alternative?

Not a full one. Every tool in this category charges, because AI drafting costs money to run. What is free is the single jobs: our free LinkedIn tools cover hooks, headlines, post previews, carousels, and an engagement rate check with no account. Most vendors offer a trial, not a free plan.

What does brand memory mean, and why does it matter?

It is what the tool knows about you between sessions. In Depost AI it is one record for each brand, built from the LinkedIn account you connect: your positioning, a voice learned from what you publish, every post with its real numbers, the posts you save, and any documents you add. Every agent reads it before it writes. Without it, you paste the same background in every time, and the third draft is no better than the first.

Which Taplio alternative is safest for my account?

Ask any tool what it sends without you. A tool that publishes through LinkedIn's official API and needs a person to approve each post, connection, and message is a much lower risk than a tool that runs auto-connect and DM sequences for you. What gets a LinkedIn account restricted explains where the line is, and how many comments a day is safe covers volume.

Can I move my scheduled posts from Taplio to another tool?

No, not automatically. Scheduled posts do not transfer, so plan to create them again in the new tool. Your published posts are stored on LinkedIn and not in Taplio, so your analytics are rebuilt when the new tool syncs your account.

Is Supergrow better than Taplio?

They are the same kind of tool: you run each step yourself. Supergrow covers most of what people use Taplio for each day, and adds approvals and MCP on its Teams plan. Taplio adds the lead feed and automated outreach. Neither does the work for you or keeps a brand memory between sessions, and that is the difference this list is ranked on.

Getting back to it is the hard part, and using it for anything is harder still. It is easy to end up with a list you have not opened in months, full of posts you cannot remember saving.

Where the page is, why posts go missing from it, and what to do with one once you have kept it.

How to find your saved posts on LinkedIn

On a computer

LinkedIn's help page puts it plainly: "Click Saved Items on the left pane of your LinkedIn homepage," then "Click Saved posts and articles."

The page you land on is titled Saved Posts. It sits in a section called My Items, alongside posted jobs, the job tracker, and My Learning.

In the phone app

Tap your profile picture, then tap Saved posts. Same list, same order.

The direct link

Nothing on the feed points at the page clearly, so the fastest route is the URL:

linkedin.com/my-items/saved-posts

Put that in your browser bookmarks and you never have to hunt for the menu again.

Why are my saved LinkedIn posts not showing?

Four reasons, in the order you should check them.

The Articles filter is on. The page has two buttons at the top, All and Articles. If Articles is selected you are only seeing newsletters and articles, and everything else is hidden.

The post is further down than you think. The list is ordered by when you saved each item, and it loads in batches behind a Show more results button. A post you saved in March is not missing. It is a long way down a list that only loads more when you ask it to.

The author deleted it. A saved item points at the live post rather than storing a copy. If the author removed the post or closed their account, your entry goes with it, and you get no notice that it happened. To keep a copy of a public post as a file, the free LinkedIn post to PDF tool saves its text and images.

You saved it on another account. Saved items belong to the profile that saved them, so a post you kept while signed in elsewhere is not in this list.

Does LinkedIn notify anyone when you save their post?

No. Saving is private. Nothing appears on your profile, the author is not told, and no one else can see your list. If you are saving a competitor's post to study how it was built, nobody is told.

What the saved posts page can and cannot do

That second column is the whole problem. Saving is easy, and everything after saving is manual.

Why saving LinkedIn posts is worth the effort

A file of other people's posts is sometimes called a swipe file. The reason to keep one is not admiration. It is that writing gets much faster when you are not starting from nothing. Five things a saved post gives you later.

An opening you can learn from. A post gets two lines before "see more" cuts it off, so the opening does nearly all the work. A post that got you to click is a working example of an opening that held attention on a subject near yours. The post preview shows where that cut lands on your own draft.

A shape to reuse. Almost every good post is one of a small number of shapes: a list of things that went wrong, a before and after with one number in it, a teardown of something public, a short story with one lesson, a contrarian claim with three reasons. The shape is not owned by anyone. Saving one you liked means you have a structure ready the next time you have something to say and no idea how to lay it out.

A number with a source. Numbers are the hardest part of a post to come by. When someone cites a figure and names where it came from, that citation is worth keeping, along with the source so you can check it yourself before you repeat it.

The objection your buyers keep raising. The comments under a post in your industry are full of doubts written in your buyers' own words. Each one is a question somebody wants answered, which makes them a better source of post ideas than any list of topics.

Proof that a topic works. If three posts about the same narrow subject did well in your niche this month, that subject has an audience. That is useful before you spend an hour writing about something nobody asks about.

Each of those is only useful if you can find it again, which brings us to the part LinkedIn does not help with.

What to tag when you save a post

A save with no note is a bet that you will remember why you saved it. You will not. Two weeks later you are looking at a post about hiring and wondering whether you kept it for the opening line, the layout, or the argument.

Five tags cover almost everything worth keeping.

Pick your own words if you prefer, but keep the set small and use it consistently. A file with forty tags is the same as a file with none.

LinkedIn has no tags, so you need somewhere to put them. The three options that work in practice:

• A note on your phone. Free, immediate, and enough if you save a couple of posts a week. You are copying the link and one word.
• A document or a spreadsheet. Better for reviewing in bulk, worse in the moment, because saving takes a browser tab and a paste.
• A tool that does it at the point of saving. The only version that survives a busy week, because the tag goes on while you are still looking at the post.

How to turn a saved post into a post of your own

Saving is the easy half. This is the half that produces a post. You are borrowing the shape of a post that worked. You are not borrowing its words, its numbers, or its opinion.

1. Name the shape

Write the structure down in one sentence, without the subject. "One number, then the three things they stopped doing." "A mistake, then what it cost, then the fix." If you cannot describe the shape in a sentence, you have not understood why the post worked, and you will end up copying sentences instead.

2. Swap in your subject

Now fill that shape with something only you can write about: a project you ran, a number from your own business, a pattern you see across your clients. The shape stays, everything inside it changes.

3. Add your own evidence

This is where most repurposed posts fall over. If you do not have a number, say so plainly and use an example instead. Borrowed structure with invented evidence is how a post gets picked apart in the comments.

4. Write the hook yourself

Read the original's first two lines, work out what job they do, then close the tab and write your own. If the original opened by naming a cost, name your cost. Do not paraphrase the sentence, because the people most likely to spot a paraphrase are the ones who read the original.

5. Post it, then tag what worked

If the shape performed, tag it again in your file. After a few months you know which shapes work for your audience, which is worth more than any list of tips.

A worked example

Say you saved a post from a recruiter: one number at the top, then the three things they stopped doing to get it. Say you are not a recruiter: you run a small agency.

The shape is "one number, then the subtractions." Your version: the month you cut your proposal template from nine pages to two, the three sections you deleted, and what happened to your close rate. Same structure, none of the same content, and a post only you could write.

Repurposing, reposting, and copying

If you want the other side of this, repurposing your own content covers getting a second and third post out of things you already published.

A weekly routine that keeps the file useful

The upkeep is about ten minutes a week.

• While you read. Save anything that made you stop, with one tag. Two or three a day is plenty. If you cannot pick a tag in five seconds, do not save it.
• Friday, ten minutes. Open the file, delete anything you no longer understand the point of, and pick one post to build on next week.
• Once a month. Look at which tags you actually use. If you have not opened #proof in six weeks, drop it.

The file should get smaller as often as it gets bigger. A saved list you never prune becomes the same unreadable pile as LinkedIn's own.

How Depost AI does this for you

We built bookmarks into Depost AI because the gap between saving and writing is where the habit breaks.

Save the whole post, not a link to it. Save any LinkedIn post to Depost AI while you read it. The text, the author, the likes, comments and reposts as they stood, the media, and the link all come along, so the bookmark still works if the original is deleted.

Tag it as you save. The tag goes on in the moment, not in a separate app later. Star the ones that matter and they stay at the top.

Search everything you kept. Search the text of every saved post, and filter by tag, by post format, or by platform. The post format and the content pillar are detected on save, so the file sorts itself.

Repurpose in one step. Every bookmark has a Repurpose action. It takes the post you saved and your angle on it and drafts your version in your voice, using what Depost AI knows about how you write, which is what brand memory is for. You edit the draft and approve it. Nothing is posted for you.

The same tab holds the viral posts library: posts we collected and broke down by hook, format, and score, next to the ones you saved yourself.

FAQ

Where do I find saved posts on LinkedIn?

On a computer, click Saved items in the left pane of your homepage, then Saved posts and articles, or go straight to linkedin.com/my-items/saved-posts. In the app, tap your profile picture, then Saved posts.

Can I search my saved posts on LinkedIn?

No. The saved posts page has no search box, and LinkedIn's main search bar does not look inside your saved items. The only controls are the All and Articles filters.

Can I organize saved posts into folders on LinkedIn?

No. There are no folders, tags, or notes, and no way to change the order. The list runs newest save first.

Why did my saved LinkedIn posts disappear?

Usually they did not: the list is ordered by when you saved, loads in batches, and older saves sit well below the newer ones. If an entry is genuinely gone, the author deleted the post or closed the account, because a save points at the live post rather than storing a copy.

Is there a limit to how many posts you can save on LinkedIn?

LinkedIn does not publish one and its help pages do not mention a ceiling, which is the same pattern as the daily comment limit. The practical limit is attention: an unsearchable list stops being opened long before it fills up.

Does LinkedIn tell the author when you save their post?

No. Saving is private, the author gets no notification, and nothing about it shows on your profile.

Can I export my saved posts from LinkedIn?

There is no export on the saved posts page. If you want a list you can keep, sort, or hand to someone else, build it outside LinkedIn as you save.

Is it legal to repurpose someone else's LinkedIn post?

Reusing a structure is normal practice and nobody owns a post shape. Reusing the sentences is copying, whether or not you reword them. Write your own hook, use your own examples, and credit the person if the idea is clearly theirs.

What is the difference between saving a post and saving an article?

Nothing on LinkedIn's side. Both land in the same list, and the Articles filter is what separates newsletters and articles from the rest.

The differences show up after the first month. Do the drafts still sound like you? Is the graphic still your job? Does the bill double when a client needs a login? Does the tool send anything from your account without you?

That is what this list ranks on. Ten tools get a full entry: a best-for label, what each does well, where it falls short, and the monthly price. Five more get the same verdict in a paragraph. Depost AI is ours. It is ranked first, and the entry says why. The other fourteen are judged on the same five questions.

What counts as a LinkedIn AI tool

A LinkedIn AI tool is software that uses a language model to do part of the work of showing up on LinkedIn: finding an angle, drafting the post, designing the visual, deciding when it goes out, writing the replies, or reading the results. Some of these tools are a single writing assistant. Others are a team of agents that does every step. The price does not tell you which kind you are looking at.

The useful question is who does the work. Some tools write a draft and leave the rest to you. Some give you a set of steps that you run yourself. One does every step and waits for your approval. The ranking below depends on that more than on the number of features.

How the tools were judged

Five questions, the same for every tool:

1. Does it write in your voice, or in its own? A tool that learns from your published history produces drafts you can post. A tool that starts from a blank prompt produces drafts you rewrite.
2. Does it read your real analytics? Impressions and follower history from the account you connect, or a dashboard that shows what the tool itself posted.
3. What does it do besides write? Graphics, scheduling, engagement, and lead tracking are separate tools for most people. The fewer tools you need, the more likely the work gets done.
4. How does the price scale? Per seat, per profile, per credit, or per brand. The number on the pricing page is for one person. Almost nobody stays one person.
5. What does it send from your account without you? Automated connection requests, DM sequences, and auto-comments are the features that carry account risk. A tool that drafts and lets you send is a different category from a tool that acts as you.

The fifteen at a glance

Prices are the monthly-billed figure for one person, read from each vendor's live pricing page on September 8, 2026, and Oiti's on September 12. Pricing pages change often, so check the vendor's page before you buy.

1. Depost AI

Best for: creators, B2B teams, and agencies who want every step done for them, with nothing published until a person approves it.

Depost AI is the only tool on this list that does every step for you. The others help with one step at a time. Four agents work from the analytics of the LinkedIn account you connect: the Performance Analyzer reads which posts beat your own average, the Idea Generator turns that into next week's ideas, the Post Generator drafts in a voice learned from your published history, and the Graphic Generator designs the visual in your colors. The drafts go into a calendar for review, and publish through LinkedIn's official API once you or your reviewer approves them.

The second half is engagement. Targeted feeds show posts from the people and topics you care about across LinkedIn, X, Reddit, Threads, and Facebook. Replies are drafted in your voice, and lead lists keep stages, notes, and reminders for every prospect. You read, edit, and send.

What it does well

• Ideas are ranked from your real impressions and full follower history, not from a generic best-practice list.
• The post and its graphic come together, so you do not have to make the image later.
• Approvals with rounds, threaded comments, roles per brand, and an audit trail on Team.
• Pricing scales by LinkedIn account, and clients, VAs, and teammates are invited free.
• Works from Claude, ChatGPT, Cursor, or any MCP client, with a separate key for each brand, on every plan.
• Sends no automated connection requests, DMs, or comments. Everything it drafts, you send.

Who it is not for

• Anyone who wants a tool that posts, connects, or messages while they are away. Every step stops at your approval, on purpose, and nothing on this list does automated outreach safely.
• Anyone running one queue across five networks. Publishing is LinkedIn. The engagement feeds reach four other networks, but the scheduler does one properly.

Price: Solo is $59 a month for one LinkedIn account. Team is $99 a month for the first account and a flat $59 for each additional one, up to 30, with unlimited workspaces and no charge for people. Yearly billing is two months free. There is a 7-day free trial on both. See pricing.

2. Taplio

What it is: a feature menu operated seat by seat: an AI coach, a post library, carousels, a lead feed, analytics, scheduling, and an MCP server in one product.

What it covers

• Carousels, a lead feed, analytics, scheduling, and MCP.
• A library of other people's posts to work from.

What to know

• The AI writing most people buy it for starts on the $69 tier, not the $39 one.
• Pricing is per seat, and an accounts add-on sits on top of that for agencies.
• Automated outreach, meaning auto-connect and DM sequences, runs on your account. That is a decision to make deliberately, not a default to leave on.
• No team approval workflow.

Price: from $39 a month, with AI writing from $69 a month, per seat. See the full Taplio comparison.

3. Scripe

What it is: a recording-first drafting tool. A podcast episode, a webinar, or a voice note goes in, and posts come out with a workflow around them.

What it covers

• Posts from recordings and transcripts, with post history synced.
• Unlimited users from the Advanced plan, and an approval workflow on it.
• Scheduling to personal profiles.

What to know

• No graphic design for the post.
• No targeted feeds, lead tracking, or drafted replies. It is a creation tool.
• Billed per profile, in euros, with each extra profile at €45 a month.

Price: €69 a month for one profile, plus €45 for each additional profile. Advanced, with unlimited users, is €99. See the full Scripe comparison.

4. Stanley

What it is: an assistant you message over iMessage, Telegram, WhatsApp, or the web. The LinkedIn-only app has moved to this cross-platform product.

What it covers

• Drafting, scheduling, and posting from a message thread.

What to know

• It is built for one creator across several platforms, so LinkedIn is one channel among others.
• Its site shows no graphics, approvals, MCP, or multi-brand support.
• There is no public pricing page. The price appears at checkout, so check what you are buying.

Price: $79 a month at checkout, per person. See the full Stanley comparison.

5. Kleo

What it is: a drafting assistant that designs a graphic alongside the post.

What it covers

• Drafts and graphics in one place.

What to know

• No targeted feeds, no drafted replies, no approvals, no MCP.
• $99 a month buys drafting and graphics only; scheduling and engagement are elsewhere.
• Analytics are a dashboard, not an input to what gets written next.

Price: $99 a month. See the full Kleo comparison.

6. Supergrow

What it is: an assisted writing workflow: AI interviews, a curated feed, templates, and approvals and MCP on the Teams plan.

What it covers

• Interviews that turn what you know into posts.
• Carousels and infographics designed with the post.
• Approvals and MCP on the team plans.

What to know

• The Teams plan includes five accounts. For more than five, you contact them.
• Its automated first comment posts under your name without a review step.
• Analytics show a dashboard rather than driving what gets written next.

Price: from $39 a month. Teams is $199 a month for five accounts. See the full Supergrow comparison.

7. Saywhat

What it is: drafting sold alongside strategy sessions, feedback on drafts, and a private community.

What it covers

• Coaching sessions and a community next to the software.
• A library of posts to research, and analytics history.
• An engagement feed with AI comments.

What to know

• No scheduler at all. Every post is published by hand.
• No graphics, no approvals, no MCP.
• Writing allowances are sold as credits per tier, so heavy use means the $99.99 or $149.99 tier.

Price: from $59.99 a month. See the full Saywhat comparison.

8. MagicPost

What it is: a form-driven workflow with client approvals over a browser link and a lead pipeline that pushes into the CRM.

What it covers

• Link approvals with no client login.
• Feeds with a lead pipeline, CRM integrations with HubSpot, Pipedrive, and lemlist, MCP, and per-client workspaces.
• Nothing auto-sends.

What to know

• Generated visuals are still in beta.
• Forms rather than a conversation, so you drive each step.
• One seat is one account, and drafted replies are not part of the product.

Price: from $49 a month, with AI writing from $69. See the full MagicPost comparison.

9. Oiti

What it is: a persona built from your LinkedIn profile and website, with a knowledge base of videos, articles, and notes behind it.

What it covers

• A persona from your posts and site, and a knowledge base the drafts draw on.
• An infographic made with the post.
• Official API publishing to personal profiles and company pages, with a 7-day trial.

What to know

• It stops at the published post. No feeds, no drafted replies, no approvals, and no MCP shown.
• No carousels yet, and analytics are a twelve-month view rather than an input to what gets written next.
• The persona and correction-memory claims are Oiti's own, and it is a newer entrant. The trial is the place to check them.
• Pro caps at three clones and three personal accounts.

Price: $49 a month for one AI clone, $79 for three. See the full Oiti comparison.

10. Resonate

What it is: drafting from the tools you already work in. It connects to Notion, Slack, GitHub, and other work tools, and gives each draft a score.

What it covers

• Integrations with the tools you work in.
• A critique and a score on each draft.
• Carousels and AI images, twelve months of analytics history, MCP and an API.

What to know

• Priced per person, at $99 a month on a launch price with $199 as the list price, and $69 for each extra person.
• Outreach runs inside the tool, paced with daily limits. Drafted replies are on the done-for-you plan only.
• Draft review arrives on the Growth plan, and agency workspaces on the Agency plan.

Price: $99 a month at the launch price, $199 list, plus $69 per extra person. See the full Resonate comparison.

The last five, quickly

They rank below the top ten because they cover fewer of the seven jobs, serve a narrower buyer, or are built around a network other than LinkedIn.

11. AuthoredUp

What it is: a formatting editor with post-history analytics, and no AI drafting anywhere in the workflow.

AuthoredUp does not write for you, and says so. It gives you an editor, device previews, a hook library, and analytics on your post history. Every word is yours to write, there are no graphics, feeds, or drafted replies, and team features stop at draft collaboration with multi-profile on the Business tier.

Price: $19.95 a month per profile. See the full AuthoredUp comparison.

12. ContentIn

What it is: an AI ghostwriter with a large template library that learns your voice from your edits.

It has a template library, images and carousels with the draft, AI comments and MCP on Pro, and a Telegram bot. It learns from your edits and not from your published posts, so the first weeks take more editing, and team features are on a custom plan.

Price: Growth is $39 a month. See the full ContentIn comparison.

13. Postwise

What it is: an X scheduler with LinkedIn and Threads attached.

It offers one-shot drafts and one queue for three networks. The LinkedIn-specific parts, from formatting to analytics to engagement, are lighter than the X side, and there are no graphics, feeds, approvals, or MCP.

Price: from $37 a month, tiered by accounts and AI post credits. See the full Postwise comparison.

14. Heyoo

What it is: employee advocacy program software, measured in participation: campaigns, nudges, and a leaderboard.

Comms creates a campaign, employees get suggested posts and nudges over Slack and Teams, and a leaderboard tracks participation. It has HR integrations for large rollouts. Employees share posts themselves, and approvals arrive on Growth. It offers suggested posts and an editor rather than drafts in each person's own voice, with no graphics shown, a colleague feed only, and API access on Professional rather than MCP.

Price: $349 a month for up to 10 users, with the approval workflow on Growth at $469. See the full Heyoo comparison.

15. Oktopost

What it is: an enterprise social suite sold by quote: multi-network publishing, social listening, CRM attribution, and SSO.

Oktopost covers multi-network publishing, social listening, CRM attribution, SSO, an AI Assist, and MCP. It is a suite you operate, sold by quote on annual contracts, with approvals on Advanced and an optional advocacy auto-pilot worth reading the fine print on.

Price: from $499 a month, billed annually, by quote, with approvals from $799. See the full Oktopost comparison.

What LinkedIn AI tools cost

The pricing page number is the price for one person before any seat, profile, or credit is added. On that number, the tools built for LinkedIn cost between $19 and $99 a month, and the enterprise suites are sold by quote.

The number that matters more is the second one. What does a reviewer, a client, or a second profile cost? That depends on which of four pricing models the tool uses, and the pricing model tells you more about your bill in six months than the starting price does.

Work out the cost at the size you expect to be, not the size you are now. A ghostwriter with six clients pays for six seats on a per-seat tool, or six profiles on a per-profile tool. Compare those two totals. The pricing page has a slider for the per-brand version.

Best LinkedIn AI tools by use case

The ranking above is for the general case. Most people have a narrower question. Here is the answer to each one.

• Best for writing LinkedIn posts. Depost AI drafts in a voice learned from the published history of the account you connect, with the sources behind each draft shown. The free post generator drafts from a form with no account, and the content creation guide covers what to feed it.
• Best for scheduling. Depost AI, where the time slot is suggested from your own engagement data and publishing goes through LinkedIn's official API. The scheduling page has the detail, and the best time to post guide explains how that slot is found.
• Best for analytics. Depost AI syncs real impressions and the full follower history of the account you connect and turns it into next week's ideas. The engagement rate benchmarks say what a good number looks like, and the engagement rate calculator does the one-post version for free.
• Best for carousels and graphics. Depost AI designs infographics in your own colors and type, in the same chat as the post. The free carousel generator is the no-account version, and the carousel guide covers the format. To fit an image you already have, the image resizer exports it at the size LinkedIn gives, and the post image size page lists every size.
• Best for engagement and leads. Depost AI, with targeted feeds across five networks, drafted replies, and lead lists you send from. The engagement page and the LinkedIn AI comment generator have the detail, and the smart comments post covers the habit the feeds are built for.
• Best for profile optimization. Depost AI scores the profile section by section when you connect it. The free headline generator, about generator, and banner generator do the job without an account. The profile URL finder gives you the clean link to share, and the QR code generator turns it into a code for a slide or a card.
• Best for agencies. Depost AI on Team, priced per brand with clients and VAs invited free, and approvals with rounds and an audit trail. See Depost AI for agencies. Our list of eleven LinkedIn management tools for agencies compares workspaces, client approval, and price per client.
• Best for ghostwriters. Depost AI on Team, one brand per client with the client invited free to approve. See Depost AI for ghostwriters, and the glossary on what an AI LinkedIn ghostwriter does and does not do.
• Best for LinkedIn marketing teams. Depost AI, with approvals, roles per brand, and an audit trail on Team. See Depost AI for marketing teams.
• Best for recruiters. Depost AI for a recruiter's own posts and the replies to the candidates who comment on them, and the free InMail generator for the one-off message. See Depost AI for recruiters.
• Best for employee advocacy. Depost AI, where each executive gets their own voice, analytics, and approval flow, not a shared template. See employee advocacy.
• Best free LinkedIn AI tools. The Depost AI tools hub has 64 generators, calculators, and downloaders that need no account. Our list of the best free LinkedIn tools compares them with the other free sets.

How to choose

The table gets you down to three or four tools. These four cases get you to one.

• You are one person and you want the work done for you. Depost AI Solo: one LinkedIn account, the four agents, the feeds, and the free tools for the one-off jobs.
• You run more than one LinkedIn account, or a client will need a login. Count the people who will use the tool in six months, then price every option at that number. Per-seat pricing and fixed team plans come out very differently at ten people from per-brand pricing with free invites. Depost AI Team is the second kind.
• Your buyer is procurement. Depost AI Enterprise, from 30 accounts, with roles, approvals, and an audit trail.
• You will write every word yourself. Connect the account anyway. The analytics, the feeds, and the drafted replies are useful even if you never use the drafts.

One more check, whatever you pick. Before you connect any tool, find out how it publishes and what it sends without you. Publishing through LinkedIn's official API and drafting replies for you to send is one category. Automated connection requests, DM sequences, and auto-comments running under your name is another. Both kinds are on this list. Only turn on the second kind if you have decided you want it. The glossary entries on LinkedIn automation and the LinkedIn API explain the difference.

What about ChatGPT or Claude?

Both can write a LinkedIn post from a prompt, and for some people that is enough. What they do not do is the work around the post. They do not know which of your posts beat your own average, they hold no calendar and publish nothing, and what they remember about you is general. It is not your brand's tone, formats, and topics to avoid. In the figure above they belong in the first column.

You can also use them together with a tool from this list. Depost AI has an MCP server on every plan, so Claude, ChatGPT, or Cursor can read your brand context and analytics, add drafts to your calendar, and find the gaps in it, with a separate key for each brand. Publishing is a separate permission you turn on, and approval stays in Depost AI. The MCP page and the glossary entry on the Model Context Protocol have the detail.

One thing to know if you make post images in ChatGPT: they carry Content Credentials, and LinkedIn shows a CR tag on images that carry them. The AI metadata remover checks for them and removes them in your browser.

Text can carry something similar. Drafts copied out of ChatGPT or Claude sometimes bring hidden characters with them, such as zero-width or narrow no-break spaces, and the invisible character remover shows where each one is and copies the text back without them. Whether ChatGPT watermarks its text is a separate question, and we checked.

What about LinkedIn's own AI?

LinkedIn has its own writing help. Check what it covers before you pay for anything. As of September 2026, LinkedIn Help describes an AI-powered writing assistant for the profile, which suggests text for the Headline, About, and Experience sections and is available to a select group of Premium subscribers, and a Message Proofreader for Premium members that reviews, shortens, or clarifies the first message in a new conversation once you have typed at least twenty words. The AI post-writing tool LinkedIn introduced earlier is, in LinkedIn Help's words, "not available at this time", with new tooling promised.

So the built-in help covers the profile and the first message, not the post, the calendar, or the analytics. If the Headline and About are the problem, start there, or with the free headline generator and about generator. The tools on this list are for the work after that.

Automation tools are a different list

Search for LinkedIn AI tools and half the results are lists of automation tools, Expandi, Dripify, Waalaxy, HeyReach, and the like, which send connection requests, run message sequences, and comment under your name on a schedule. None are on this list, and not because they lack AI. They act as you. LinkedIn's User Agreement prohibits automated access of that kind, and if LinkedIn restricts an account, it is yours and not the vendor's. The glossary entry on account restrictions covers what happens and how long it lasts.

Three tools on this list include some of it, Taplio's automated outreach, Resonate's paced outreach, and Supergrow's automated first comment, and each entry says so. The drafting side of outreach is a different job: a note that mentions something specific, and a follow-up you remember to send. The connection request generator, the connection request limit calculator, and the outreach workflow cover it, and nothing is sent automatically.

What was left out, and why

Buffer, Hootsuite, and Sprout Social schedule to LinkedIn and have each added an AI writing assistant, but they are general social schedulers, and none is built around a LinkedIn profile's history. Canva and Grammarly are general design and writing tools that LinkedIn people happen to use. ChatGPT and Claude have their section above. We asked one question: is the product built for LinkedIn, and does AI do part of the LinkedIn work? Fifteen tools passed.

FAQ

What is the best AI tool for LinkedIn in 2026?

Depost AI. It is the only tool on this list that does every step, from reading your real analytics to an approved, scheduled post with its graphic. It remembers your brand between sessions, it is built for creators, B2B teams, and agencies, and you pay per brand, not per seat. Every other tool on the list covers part of that and leaves the rest to you.

Are LinkedIn AI tools safe to use?

It depends on what the tool does on your account, not on whether it uses AI. Tools that publish through LinkedIn's official API and draft replies for you to send are one category. Tools that run automated connection requests, DM sequences, or auto-comments under your name are another, and you should read LinkedIn's terms before you turn that on. Several tools on this list offer it as a feature. Depost AI does not.

How much do LinkedIn AI tools cost?

Between $19 and $99 a month for one person, with the enterprise suites, Heyoo and Oktopost, at $349 and up. The number that matters more is the second one: what a second person, a second profile, or a client login costs. Per-seat tools roughly double, per-profile tools add a flat amount for the account and nothing for the person.

Do LinkedIn AI tools write in my voice?

Most claim to. The difference is what they learn from, and when. Depost AI learns from the published history of the account you connect before the first draft, refreshes the voice weekly, and keeps what you tell it in chat. Tools that learn from your edits, an interview, or a prompt know less about you at the start. Test any of them with a topic you have already posted about and compare the draft to what you actually wrote.

Which LinkedIn AI tool remembers my feedback?

Depost AI keeps brand memory across sessions: tone, formats, topics to avoid, and the preferences you state in chat, with a separate memory for every brand you run. Most of the other tools on this list start each session from the prompt, so the test is simple: give the same instruction on two different days and see whether the second draft already knows it.

Can one tool cover writing, scheduling, analytics, and engagement?

Depost AI does, with one brand memory behind all four: ideas from your analytics, drafts and graphics, scheduling through the official API, and feeds with drafted replies. Most tools on this list cover one or two of the four and leave the rest to other tools. The coverage table above says which.

Which LinkedIn AI tool is best for agencies?

Depost AI on Team, because you pay for the number of LinkedIn accounts, not the number of people, so a client, a VA, and a reviewer per brand cost nothing. Each client gets their own brand memory, approvals run in rounds with an audit trail, and nothing publishes until the client approves it. See Depost AI for agencies.

Is there a free LinkedIn AI tool?

Most tools here offer a free trial rather than a free plan. For one-off jobs, Depost AI publishes free tools that need no account: a post generator, a hook generator, a headline generator, a carousel generator, and an engagement rate calculator, among others on the tools hub.

Does LinkedIn have its own AI writing tool?

For the profile, yes: a Premium writing assistant that suggests Headline, About, and Experience text for a select group of subscribers, plus a Message Proofreader for the first message in a new conversation. For posts, not at the moment; LinkedIn Help says its post-writing tool is not available at this time. None of it reads your analytics or schedules anything. The tools on this list do.

How were these tools ranked?

On five questions: whether the tool writes in your voice, whether it reads your real analytics, what it does besides write, how the price scales past one person, and what it sends from your account without you. Feature claims and prices were checked against each vendor's live site in September 2026, with monthly billing selected, and are checked again every month. Depost AI is our product. The head-to-head pages linked from each entry have the full feature tables.

Every one of those numbers has the same source, which is somebody's experience plus somebody else's article. LinkedIn has never published a daily comment limit, and the way its help pages are written suggests it never intends to.

That does not mean there is no limit. It means the question is worse than people think, and the honest answer is more useful than a number.

What LinkedIn actually says

Three things, and they are all worth reading in the original.

The first is the trigger. LinkedIn describes acting on "patterns that indicate your account is engaging in automated or otherwise inauthentic activity," and gives an example: "sharing high volumes of messages, comments, posts, or other content in a short period of time." Comments are named. Volume is named. A threshold is not, and neither is the length of that short period.

The second is the consequence, and it is not the one people expect. LinkedIn says it may "limit the visibility of your content or restrict your account." Visibility comes first. Before anything stops you commenting, the comments you already left can simply stop reaching people, which is invisible from where you are sitting and looks exactly like a quiet week.

The third is escalation. A temporary restriction re-enables itself at the time given on the notification, and LinkedIn warns that repeated temporary restrictions may result in permanent restriction. Hitting a wall once is an inconvenience. Hitting it every month is a trajectory.

Why the number is not published

This is the same pattern as the connection request limit, where the famous "100 a week" is also not LinkedIn's figure. LinkedIn's help pages describe what triggers an invitation restriction and how long it lasts without naming a threshold anywhere.

The reason is straightforward. A published limit is a target. If LinkedIn said 100 comments a day, the tools that automate commenting would ship a setting called "99" that afternoon, and the number would have made the problem more efficient rather than smaller.

What is enforced instead is a pattern. Speed, repetition, similarity between one comment and the next, the hours it all happens in, and whether the account does anything else. That is why two people leaving forty comments on the same morning can get different outcomes, and why the tables of numbers you find online never quite reproduce.

So how many comments per day is safe?

Higher than the internet thinks, and the honest version of the answer has two parts: what the platform tolerates, and what is worth your morning. They are different numbers, and most articles collapse them into one.

On the platform side, we run the Depost AI account at 50 to 100 comments a day, split across two or three sessions with a few hours between them. That has been the pattern for months, on an established account that also publishes, and we have never hit a restriction doing it. Fifty comments dropped into a single half hour is a different proposition entirely, and that is the part most warnings get backwards: they quote a total when the thing being measured is a rate.

Read that as a ladder rather than a target. Almost nobody asking about the limit is anywhere near the top of it: the usual case is somebody doing eight comments a day and wondering whether it is safe to do more. It is, by a wide margin, provided the extra ones arrive spread out and say something.

Two or three sessions, hours apart

This is the part that does the work, so here it is in detail.

A session is twenty to forty comments in half an hour to forty-five minutes, and then you close the tab. Two of those covers a normal day. Three is a heavy one. The gap between them matters more than what happens inside them, because a gap is what turns a burst into a habit.

A working shape most people can hold:

• Morning session. Thirty minutes on the feed while it is still moving, on posts published in the last hour or two.
• Midday session. Shorter. Start it by answering the replies your morning comments collected, which is the cheapest conversation on the platform and the one most people skip.
• Late afternoon, if the day allows. Different accounts from the morning, so the same five names are not the whole of your activity.

Two rules that survive contact with a busy week. Spread the comments across the hours you are actually online, because activity that continues at a steady rate through the night is the clearest signal there is that a person is not doing it. And vary where they land: commenting on the same handful of accounts every day looks like an engagement pod, and LinkedIn names pods in its own enforcement language.

What actually gets you limited

If the ceiling is a pattern rather than a total, these are the parts of the pattern you control.

Bursts. Forty comments in twenty minutes is not a reading pattern, because nobody reads forty posts in twenty minutes. The same forty across a working day is unremarkable.

Repetition. Comments that are structurally identical are the easiest inauthentic pattern to detect and the easiest to produce by accident. "Great point, Sarah" fifteen times is a template whether or not you used one.

Automation that acts as you. This is the sharp edge. Software that signs in with your password, or borrows your logged-in browser session, and comments while you are asleep produces exactly the pattern the enforcement is built to catch, and everything it does is recorded as something you did. What gets an account restricted covers the mechanics.

Nothing else on the account. An account that only comments, never posts, and has a thin profile reads differently from one doing all three. The judgment is about the account, not the action.

The comments still have to be worth reading

Everything above is about the platform's tolerance. Your own is the tighter constraint.

Past a certain point in a session the comments get shorter, more generic, and more agreeable, and a generic comment is worth approximately nothing: nobody clicks the name attached to "couldn't agree more." The reason to care about volume at all is that a comment only pays if somebody reads it and wants to know who wrote it, and that stops being true long before LinkedIn intervenes.

The published limit here is generous: 1,250 characters, the same for a reply as for a top-level comment. Almost no good comment needs half of it. What a good comment looks like has the specifics, and the character counter has the rest of the limits if you want to check a draft against them.

If the tiredness is the problem, drafting help keeps late comments from going generic without changing who posts them. The free AI comment generator turns a post and your take into three drafts, and the LinkedIn AI comment generator in Depost AI does the same on the post you are reading, in your voice, and leaves the posting to you.

What to do if you are already restricted

If commenting has stopped working, the notification tells you when it lifts, and waiting is the whole remedy. There is no appeal worth filing for a temporary limit and no support conversation that shortens it.

Do two things while you wait. Disconnect anything that comments on your behalf, because LinkedIn's stated remedy for an automation restriction is to disable the software and let the account re-enable itself. And when it lifts, come back at half the rate you were running and rebuild from there, because a second restriction is the one that starts an escalation.

Other LinkedIn limits worth knowing

The same split runs through all of them. A short list of numbers LinkedIn publishes and expects you to work around, and a much longer list of thresholds it enforces without ever naming, for the same reason it does not name the comment one.

Two of those change how you work even if you never go near the ceiling.

An InMail that gets a reply is free. LinkedIn returns the credit for any InMail accepted, declined, or responded to within 90 days of being sent, so only the ignored ones cost you anything. Your real monthly allowance is set by how relevant your messages are, not by which plan you bought.

Followers are uncapped, connections are not. The 30,000 limit applies to two-way connections only. At the cap, Follow quietly becomes the default button on your profile. The platform is telling you which of your two audiences can still grow.

FAQ

Is there a daily comment limit on LinkedIn?

Not a published one. LinkedIn enforces against high volumes of comments in a short period but has never stated a number, and the figures circulating online are practitioner estimates rather than policy.

How many comments per day is safe on LinkedIn?

More than most people think. We run 50 to 100 a day on our own account, split across two or three sessions with hours between them, and have never hit a restriction doing it. The same total in one sitting is a different pattern, and that is what gets accounts limited.

How long should a commenting session be?

Twenty to forty comments in thirty to forty-five minutes, then stop. Two sessions covers a normal day and three covers a heavy one. The gap between sessions is doing more work than anything inside them.

What happens if you comment too much on LinkedIn?

LinkedIn says it may limit the visibility of your content or restrict the account. The visibility case comes first and is invisible from your side: comments still post, but reach fewer people. A restriction re-enables itself automatically at the time given in the notification.

Does using AI to write comments risk a restriction?

Drafting with AI and posting the comment yourself is you commenting. Software that signs in as you and comments unattended is the pattern LinkedIn enforces against, and it is prohibited by the User Agreement regardless of how the text was written.

What other limits does LinkedIn have?

Thirty thousand connections, a monthly cap on profile searches for free accounts, InMail metered in credits that come back when someone replies, and published character counts for posts, comments, and headlines. The volume limits on invitations and messages are enforced without published numbers, exactly like the comment one.

How long is a LinkedIn comment allowed to be?

1,250 characters, including spaces, and the same limit applies to replies. It is one of the few LinkedIn limits published as a number.

Eight months ago we stopped patching the first version. We started again from an empty folder. This is the story of why, and what came out the other end.

The list that kept arriving

We built the first Depost AI for ourselves. We were not copywriters. We wanted an audience. A tool that turned a rough idea into a decent post felt like the whole problem solved.

Thousands of people used it. Their feedback was consistent, and at first it was uncomfortable.

Nobody said the writing was bad. They said the writing was the easy part.

What ate the hour was everything around it. Deciding what to write about. Making the graphic. Picking the time. Being there for the replies. Knowing, a week later, whether any of it had worked.

The requests that arrived most often were not about writing at all. They came from agencies, from marketing teams, from founders whose ghostwriter needed a login. They were about approvals, workspaces, teams, and a chat. The same four words, month after month.

Version 1.0 was a set of tools. You opened a generator, filled in a form, and got a draft. It could not carry any of that list. We could have bolted the pieces on. Instead we started over.

What we built is not a writing assistant with extras. It is a content team.

It starts with a conversation

The first thing you notice is that there is no form. There is a chat.

"Plan my week of content." "Write a post in my voice." "Analyze my recent posts." "Interview me," which asks you questions about something you know and turns the answers into a post. You type it, or you say it. A voice note on the walk is a draft by the time you are back at a desk.

It works the way ChatGPT works, with one difference: the thing on the other end already knows you. Your brand memory. Your published history. Your calendar. The third draft does not repeat the first. Ask for a content plan and you get drafts sitting in the calendar, waiting for your approval. Not a list of suggestions to copy somewhere.

Behind the chat are four agents, and each brand you run gets its own set. The Performance Analyzer reads your numbers. The Idea Generator finds the angles. The Post Generator writes in your voice. The Graphic Generator designs the visual. You brief them once. The right one picks up each message.

When you want to write by hand, a real editor sits underneath. LinkedIn formatting, hook variants, rewrite-with-AI. Manual writing is first-class, not a fallback.

The difference between a colleague and a vending machine is that one of them remembers.

You never start from nothing

The blank page was the other thing people described. So the team watches the feed for you.

Inspiration is a live feed of posts working in your niche right now. Each comes with a short breakdown of why it worked and who it reached. Save the ones worth returning to. Then hand any of it to the chat. A trending post, a bookmark, a document from your knowledge base. Ask for the post that comes out of it, in your voice.

The point is not to copy anyone. The point is to never start from nothing.

The graphic is no longer your problem either. The Graphic Generator designs infographics in your colors, your type, and your themes, learned from what you have already published. It happens in the same chat where the draft lives. The post and its graphic arrive together, in whatever aspect ratio the post needs. "I will make a graphic later" is where a lot of good posts used to die. That step is gone.

A calendar that fills itself, then waits

Consistency is what LinkedIn rewards. Consistency is what busy people lose first. So the calendar does the remembering.

Approved posts sit at the time they go out. The time is suggested from your own engagement data, not a generic chart. Publishing goes through LinkedIn's official API, with the first comment already written. If a post fails to publish, it lands in your inbox with a one-click retry. Nothing is silently dropped. On Team, recurring workflows draft and queue on a schedule, so Monday's pipeline fills itself and waits for review.

Nothing enters that queue that you, or the reviewer you assign, has not approved. That was true in 1.0. It is truer now.

It starts from your numbers

Growth on LinkedIn is not one great post. It is a loop that gets slightly better every week. The loop only works if someone is reading the numbers. So Depost AI 2 starts from the numbers.

Analytics syncs the real impressions and the full follower history of the account you connect. Every post is mapped against your own average. The Performance Analyzer reads that before anyone writes a word. It works out which posts beat your baseline and why, and hands the lesson to the Idea Generator. Next week's ideas are ranked from what actually worked.

You approve. The post publishes. The loop runs again with one more data point.

"What worked last month?" is now a question you type and get answered with receipts.

The conversations that become clients

Posting is half of LinkedIn. The other half happens in the comments and the inbox. That is where most of the pipeline comes from.

So engagement got its own place. Targeted feeds watch the people, niches, and topics you care about, across LinkedIn, X, Reddit, Threads, and Facebook. The right conversations find you without the scroll. Replies arrive drafted in your voice. Lead lists keep stages, notes, and reminders on every prospect, so you know who went quiet and who is warming up. On Team, connection notes and DMs are researched from the prospect's own activity, and lead warming workflows keep drafts flowing for the lists that matter.

You read, you edit, you send. Every time.

Built for the people who kept asking

Approvals, workspaces, teams. These were the requests. So they are built in, not bolted on.

Every draft waits in review for you or the reviewer you assign. Decisions are approve or needs-changes. Feedback lives in threaded comments on the post itself. Roles are set per brand. There is an audit trail of who approved what and when.

A workspace holds the brands you run and the people who work on them. An admin overview puts every brand on one screen. Each brand gets its own agents, so the voice learned for one client never leaks into another.

That decided the pricing. A product built for teams cannot charge per seat. Charging per seat punishes you for involving the people who should be involved. Team is $99 a month for your first LinkedIn account and $59 for each one after that, with unlimited workspaces and no charge for people. Solo is $59 for one account, for one person. Bring in the reviewer, the client, the VA, and the intern. It costs nothing, and everyone acts under their own name.

Your team, inside the AI you already use

The last thing we heard was that people did not want another tab. They already work in Claude or ChatGPT all day.

So the whole team is reachable from Claude, ChatGPT, Cursor, or any MCP client, over MCP. You create a key scoped to one brand or one workspace, with exactly the permissions you grant it. Revoke it without touching the rest. A research thread becomes a LinkedIn draft in your voice, queued for review. Your real impressions pull into the conversation you are already having. The calendar is a sentence, not a tab switch. It ships on every plan, Solo included.

The rule that did not change

Everything above got bigger. One thing did not move. I want to be explicit about it, because it was the reason a lot of people chose 1.0.

Nothing publishes without a human yes.

Publishing goes through LinkedIn's official API. Depost AI sends no automated connection requests, no automated DMs, and no automated comments. Replies and outreach are drafted in your voice and sent by you, after you have read them. The team got much better at its job. It did not get permission to act on its own, and it will not.

Everything in Depost AI 2

The whole team, in one list. Each one has its own page if you want the detail.

• The agents. Performance Analyzer, Idea Generator, Post Generator, Graphic Generator. One set per brand, briefed on your numbers, your voice, and your goals.
• Chat and editor. Brief the team in plain language, by text or voice. A real LinkedIn editor underneath, with hook variants and rewrite-with-AI.
• Infographic generation. Graphics in your colors and type, learned from what you publish, generated in the same chat as the draft.
• Analytics. Real impressions, your full follower history, post-level breakdowns, and posting streaks, synced from the account you connect.
• Scheduling. Calendar, best-time slots from your own data, the first comment written, recurring workflows on Team. Official API only.
• Engagement and leads. Targeted feeds on five platforms, replies drafted in your voice, lead lists with stages and reminders.
• Teams and approvals. Review rounds, threaded comments, roles per brand, an audit trail, and clients and VAs invited free.
• Workspaces and brands. A room per client, a team per brand, and an admin overview of every brand on one screen.
• MCP. The whole team inside Claude, ChatGPT, Cursor, or any MCP client. Scoped keys, on every plan.

Thank you

To everyone who used 1.0 and told us what was missing, plainly and more than once: this release is mostly your list.

We are still building for people who have something to say and no interest in becoming content marketers. If that is you, start a free trial. Brief your new team. Tell us what we still get wrong.

Atta, co-founder of Depost AI

FAQ

What is Depost AI 2?

An AI content team for LinkedIn, rebuilt from scratch over eight months around a chat. You brief it the way you would text a colleague. It plans, writes, and designs from your real analytics and your own voice, and a human approves everything before it publishes. Teams get approvals, roles, an audit trail, and a workspace per brand. It works from Claude, ChatGPT, or Cursor over MCP.

What changed from Depost AI 1.0?

Posting is now a conversation rather than a set of tools. Inspiration watches what is working in your niche. Infographics are generated in your own colors. The calendar fills from approved drafts. Analytics benchmark every post against your own average and feed next week's ideas. Engagement has targeted feeds and lead lists. Approvals, workspaces, teams, and per-brand pricing are built in.

Who is Depost AI 2 for?

People who need to be visible on LinkedIn and do not have an hour a day for it. Founders, consultants, and creators building their own presence. Agencies, marketing teams, and ghostwriters running it for others. Solo covers one person and one account. Team covers every brand you run.

Does it cost more than 1.0?

No. Solo is $59 a month for one LinkedIn account, for one person. Team is $99 a month for the first account and $59 for each additional one, with unlimited workspaces and free invites for clients, VAs, and teammates. Yearly billing is two months free.

Is there an autopilot mode?

No. Every post, reply, comment, and connection note waits for a human yes. Depost AI sends no automated DMs, connection requests, or comments. Publishing goes through LinkedIn's official API.

That changed in March 2026. LinkedIn shipped a rebuilt feed ranking system and, unusually for a platform, published both a plain-language announcement and an engineering write-up describing how it works. Most of what you can now say about LinkedIn ranking is on the record.

This post separates three things that usually get mixed together: what LinkedIn has confirmed, what third parties have measured, and what is still guesswork.

What LinkedIn changed in March 2026

The old feed was, in LinkedIn's own description, a patchwork of separate systems. It has been replaced by two stages.

Retrieval finds the candidates. A single system built on large language models replaced five legacy retrieval sources. It encodes both you and each post as text and compares them, which means it matches on what a post is about rather than on the words it happens to contain. LinkedIn reports this runs in under 50 milliseconds across millions of posts, with new posts becoming available within minutes.

Ranking orders what survives. A model LinkedIn calls a Generative Recommender reads more than a thousand of your historical interactions and predicts what you will do with each candidate. It is a transformer, and it is trained on sequences of your behavior rather than on hand-built numeric features.

The practical consequence of the retrieval change is the one to remember. Keyword stuffing and hashtag games were always weak tactics; against a system that reads for meaning they are close to pointless. LinkedIn's announcement says the feed "better understands what a post is actually about and how your professional interests evolve."

Both documents were published on 12 March 2026: the plain-language announcement from LinkedIn and the engineering write-up by Hristo Danchev.

What the ranking system reads

The engineering post lists the signals it uses. Grouped, they look like this.

Three things stand out.

Skips are a signal. Scrolling past a post is recorded alongside reads, likes, comments and shares. This is the mechanism behind advice that otherwise sounds like superstition. A weak first line does not merely fail to earn a click on "see more"; it produces an active negative signal from everyone who scrolls by. Reach that stops early is often a skip problem, not a penalty.

Your profile is an input. Industry, experience, skills and geography are used in ranking, not just in search. An account whose profile does not say what it is about gives the retrieval stage less to match against.

Recency is one signal among many, listed beside affinity and interest. It is no longer the axis the feed is organized on.

Relevance can now outrank recency

Through 2025 and into 2026, people started seeing posts in their feed that were days or weeks old. That was not a bug.

This is LinkedIn's own design. The engineering post lists recency as one signal beside affinity and interest rather than the axis the feed is sorted on, and the announcement says the feed now weighs how your professional interests evolve. LinkedIn has since described this as balancing the two rather than dropping recency, so the feed still leans recent; it is no longer bound to it.

For anyone publishing, two things follow. A good post has a longer tail than it used to, so the first hour matters less as a hard ceiling than it once did. And a post aimed at everybody is now competing against posts precisely matched to each reader, which makes a narrow, consistent subject more valuable than it used to be, not less.

That does not make timing irrelevant. It decides who is awake for the first test. That is a smaller effect than most guides claim, but it is real. The three current posting-time studies disagree with each other by up to twelve hours, which is itself the most useful fact about them.

Every confirmed change, dated

Most articles on this subject describe a single moment. The useful version is a list with dates on it, because a claim from 2024 is not evidence about the feed you are looking at now.

The most recent one is easy to miss because it changes the page rather than the feed. Since August 2026, comments under a post are no longer shown to everyone in the same order. LinkedIn ranks them by relevance to each reader, using professional interests, connections, and engagement activity, and it made the change after reporting an 18% year-on-year rise in time spent in post comments.

That has a practical edge. The old advice to get an early comment from a well-known account assumed everyone would see it at the top. Now your comment appears high for the people it is relevant to and may not appear high at all for anyone else. Comment where you have standing on the subject, not where the follower count is largest.

What LinkedIn says it pushes down

The announcement is specific about this, and the distinction it draws matters.

Three are ranking decisions: repetitive low-substance posts, engagement bait such as "comment to agree" prompts, and video that does not match the text around it.

Three are enforcement: comment automation, engagement pods, and unauthorized third-party tools. That is a different category. A downranked post is a post that did not do well. An account using an unauthorized tool is an account LinkedIn may act against, which is why anything that publishes for you should be going through LinkedIn's official API and nothing else.

The engagement bait line matters, because a lot of advice still tells you to do exactly that. Asking a question at the end of a post is fine and always was. "Comment YES if you agree" is the thing being named.

How each format is treated

LinkedIn does not rank formats directly. It ranks predicted response, and formats differ in how much response they tend to earn, which is why the gap between the best and worst format is close to two to one.

The mechanism is attention. Dwell time is one of the things the system reads, so anything that keeps a reader on the post is working in your favor before the writing does anything at all.

• Documents and multi-image posts lead, because each swipe is another moment spent on the post. This is most of why carousels have outperformed everything else for three years running.
• Video sits in the middle. It holds attention when it earns the first three seconds and loses badly when it does not, and LinkedIn now names video that does not match its own text as something it pushes down.
• Single images and polls are middling. A poll buys cheap votes rather than the comments that carry more weight.
• Text-only posts run below average, which surprises people who write well. It is a ceiling on the format, not a verdict on the writing, and it is why a strong text post often does better rebuilt as a carousel.
• Posts with an external link come last, every year. Not a penalty so much as an exit: a post whose purpose is to send people away earns fewer of the actions that count.

The current numbers for each format are in what a good LinkedIn engagement rate looks like in 2026, which is where we keep the benchmark table up to date.

What third parties have measured

LinkedIn does not publish the weight of each signal, so this is where independent data helps.

Hootsuite's analysis of more than three million posts found saves the strongest engagement signal available, driving roughly five times the reach of a like and about twice that of a comment. That order, save then comment then like, tracks how much each action costs the reader.

Format matters too, and by a wide margin. Socialinsider's 2026 benchmarks put multi-image posts and documents, the format everyone calls carousels, well clear of text-only posts. The full table is in what a good LinkedIn engagement rate looks like in 2026.

Confirmed, measured, or guessed

Sorting the claims by how much you can rely on them is more useful than another list of tips.

A note on the fourth item in that last column, because it is everywhere. Many 2026 articles cite a LinkedIn research paper describing a 150-billion-parameter model called 360Brew handling more than thirty ranking tasks. That paper has been withdrawn from arXiv. The notice states it was removed by administrators because the submitter did not have the right to agree to the license. The engineering blog post is the source that still stands, and it is the one this article uses.

On the rest of the guesswork. There is no published list of banned words. External links are not penalized as a rule, though a post whose job is to send people elsewhere earns fewer of the actions that count as engagement, which looks identical in your analytics. And the "golden hour" is a useful habit rather than a documented switch: LinkedIn says early engagement influences distribution and has never published a fixed window.

Why your reach dropped

Almost nobody arrives at this subject out of curiosity. They arrive because the numbers fell and they want to know who to blame. Here is the honest order to check.

Your first line stopped working. Skips are a signal, so a weak opening does not just fail to earn the click on "see more", it feeds the system a negative response from everyone who scrolls by. This is the most common cause and the easiest to fix.

You changed what you write about. The system decides who to test a post on, and it learns that from your recent history. Change subject and you are shown to a different, colder group who have no reason to respond. Reach recovers when you go back to one subject and stay there for a few weeks.

You changed format. Moving from carousels to text-only is worth a real drop on its own, with the writing held constant.

You posted a link. Link posts sit at the bottom of every benchmark, every year.

Nothing changed and this is normal. Reach is noisy. One quiet post is not a signal, and a month of posts is. Compare your median rather than your last post against your best one.

It is not a shadowban. There is no such published mechanism, no list of banned words, and no confirmation from LinkedIn that any such thing exists. What does exist is enforcement, and LinkedIn names it plainly: comment automation, engagement pods, and unauthorized third-party tools. If you are not doing those, you have not been secretly penalized. You have been outranked.

What to do about it

The mechanism changed. The work did not change much.

Write for one audience about one thing. Retrieval matches on meaning, and consistency is what teaches the system who to show you to. An account that changes subject weekly resets that each time.

Earn the first line. Skips are a signal, so the opening sentence is doing more than persuasion. If your rate is flat everywhere, start there; the hook generator will write five alternatives from a draft.

Use formats that hold attention. Dwell time is read as interest, which is most of why carousels lead the benchmarks. The carousel maker builds one from plain text.

Give people a reason to save. The strongest measured signal is the one nobody optimizes for. Reference material, checklists, and frameworks get saved; opinions get liked.

Be there afterwards. Replies are fresh engagement while the post is still being tested, and a thread where the author is answering pulls other readers in.

Do not buy reach. Pods and comment automation are named in LinkedIn's own enforcement language. The risk is no longer theoretical. Commenting by hand is unaffected, and how many comments per day is safe covers where that line actually sits.

None of that is new advice. The ranking system got substantially more sophisticated and the conclusion stayed the same, because a distribution mechanism amplifies the response your writing earns. It cannot manufacture one. For the full picture of how reach is decided, the glossary entry on the algorithm is the short version, and ten things that actually move a post is the practical one.

FAQ

How does the LinkedIn algorithm work in 2026?

In two stages. An LLM-based retrieval system finds candidate posts by matching what a post is about against your profile and interests, in under 50 milliseconds. Then a model LinkedIn calls a Generative Recommender ranks those candidates using more than a thousand of your past interactions. LinkedIn published both the announcement and the engineering detail in March 2026.

Did the LinkedIn algorithm change in 2026?

Yes, substantially. LinkedIn replaced five legacy retrieval systems with one built on large language models and moved ranking to a Generative Recommender. The visible effects are a feed that understands topics rather than keywords, and older posts appearing when they are still relevant.

Does the LinkedIn algorithm penalize external links?

Not as a stated rule, and LinkedIn has never published one. What is true is that a post whose purpose is to send people away earns fewer reactions, comments and reposts, so it performs worse on the signals that drive distribution. The usual fix is to make the post stand on its own and put the link where it does not compete with the argument.

What does LinkedIn downrank?

By its own account: repetitive low-substance posts, engagement bait such as "comment to agree" prompts, and video that does not match the accompanying text. Separately, it enforces against comment automation, engagement pods, and unauthorized third-party tools, which is account risk rather than a ranking penalty.

Do saves matter more than likes on LinkedIn?

According to Hootsuite's analysis of more than three million posts, yes, by a wide margin: a save drives roughly five times the reach of a like and about twice that of a comment. The ordering follows how much each action costs the reader.

Does the first hour still matter?

It matters, but less as a hard ceiling than it used to. LinkedIn says early engagement influences how widely a post is distributed, and has never published a fixed window. Now that relevance can outrank recency, a good post can keep surfacing for days, so a slow start is no longer final.

Why did my LinkedIn reach suddenly drop?

Check in this order: a weaker first line, since scrolling past is itself a negative signal; a change of subject, which sends your post to a colder test group; a change of format, since text-only runs well below documents; and a link post, which sits at the bottom of every benchmark. If none of those changed, it is probably normal variance. Compare your median across a month rather than one post against your best.

Does LinkedIn shadowban accounts?

There is no published shadowban mechanism and LinkedIn has never confirmed one. What LinkedIn does confirm is enforcement against comment automation, engagement pods, and unauthorized third-party tools, which is a stated action rather than a secret one. Low reach without any of those is almost always a content or consistency problem.

Are LinkedIn comments shown in the same order to everyone?

No, not since August 2026. Comments are ranked by relevance to each reader, using signals like professional interests, connections, and engagement activity. A comment that appears near the top for one reader may sit far down for another.

Is there a list of banned words on LinkedIn?

No such list has ever been published, and the ranking system reads for meaning rather than scanning for terms. What LinkedIn does name is engagement bait, which is a pattern rather than a vocabulary.

That is why published benchmarks for 2026 range from under 1% to above 5% depending on where you read them, and why so many people conclude their account is failing when it is doing fine. Here are the three formulas in circulation, the current numbers by format, and what actually moves yours.

Three formulas, three different answers

Engagement rate is the share of people who did something with a post beyond seeing it. The disagreement is entirely about what counts and what you divide by.

LinkedIn's own dashboard runs highest. Its help pages define the engagement rate in Page content analytics as the ratio of interactions to impressions, where interactions include clicks, reactions, comments, and shares. Clicks are the important word: a "see more" expand or a profile click counts, and nobody outside your account can see those. That is why no benchmark uses this version, and why the number LinkedIn shows you is usually the flattering one.

By impressions is the benchmark version:

(reactions + comments + reposts) ÷ impressions × 100

It answers "of the people who saw this, how many acted?", and it is the formula behind almost every published figure, including the ones below.

By followers is the same numerator divided by your follower count. It answers a different question, "how much of my audience did this post move?", and it produces a much smaller number, because most of your followers never see any given post.

None of the three is wrong. The mistake is comparing one against a benchmark built from another. If a tool tells you your rate is 0.4% and a chart says the average is 5.20%, check the denominators before you conclude anything. The engagement rate calculator gives you both of the comparable versions, benchmarked, so you can see which one you were looking at.

The 2026 benchmarks by format

Socialinsider's current edition analyzed 1.3 million posts across 16,645 business pages. These are its Q2 2026 figures, by impressions.

Three things are worth reading out of that table.

The spread between the best and worst format is nearly two to one. A document post and a link post can carry identical writing and land a long way apart, which makes format the cheapest lever on this page. Carousels hold attention across slides, and that dwell time is read as interest.

Links sit at the bottom, and they always have. This is not a penalty so much as an exit: a post whose job is to send people elsewhere gets fewer of the actions that count as engagement. If the link matters, the usual fix is to make the post work on its own and put the URL in the first comment or the post body once the argument has landed.

Text-only posts at 3.95% are below average, which surprises people who write well. It is not a verdict on writing. It is a verdict on the format's ceiling, and it is why a good text post that would have done 4% often does better rebuilt as a carousel.

Why the benchmark sets disagree

Search for this number and you will find 5.20%, 3.85%, 2.1%, and figures under 1%, all published this year. They are not contradicting each other. They are measuring different things.

Different denominators. Company page benchmarks are usually computed by impressions, because page owners can see them. Personal profile benchmarks are usually computed by followers, because a researcher studying creator accounts can only see what is public. Divide by followers instead of impressions and the same post's rate falls roughly tenfold.

Different account types. Socialinsider's 5.20% is a business page average. Personal profiles behave differently, and generally better: personal profiles earn about 8× the engagement of company pages, while company page organic reach has fallen around 60% since 2024.

Different account sizes. A niche consultant with two thousand followers who all know her will run far above any platform average on a normal week. A page with fifty thousand followers will run far below it, and that is arithmetic, not failure. Reach grows faster than the number of people who care.

So before you use any benchmark, ask three questions of it: by impressions or by followers, pages or profiles, and what year. A figure from three years ago is not a benchmark, it is history.

What counts, and what counts more

The formula treats a reaction, a comment, and a repost as one action each. The LinkedIn algorithm does not, and since LinkedIn rebuilt its ranking system in March 2026 there is published detail on how it weighs them.

A comment is worth more than a reaction because it costs the reader more, and a comment you reply to is worth more still, since it signals a conversation rather than a broadcast. Time spent on the post is also read as interest, which is why formats that hold attention are favored in distribution, and why the two image-heavy formats sit at the top of the table above.

So a post with fifty reactions and two comments and a post with twenty reactions and thirty comments can show similar engagement rates and be treated very differently by the system. When you look at your own numbers, look at the mix, not just the total. The comment-heavy post is the one to learn from.

What actually moves the number

The hook. The feed shows about three lines before it collapses the rest behind "see more". The click on that link is the first engagement a post earns, and posts that do not earn it do not earn anything else. If your rate is low across the board, the first line is the first place to look. The hook generator writes five alternatives from a draft, each taking a different angle.

The format. Two to one, per the table above. If you have a post with more than three points in it, it is probably a carousel. The carousel maker builds one from plain text.

The first hour. Distribution is a staged test. A post goes to a slice of your network first, and if that group responds, it is shown to more people. Being present after publishing to reply to comments while the post is still being tested is the single cheapest thing you can do for the rate, and most people are in a meeting when it matters. Scheduling for a time you can be present beats posting at the "perfect" time you cannot, which is the practical conclusion of finding your own best time to post.

Consistency of subject. The system is classifying you as well as the post. An account that keeps writing about one thing teaches it exactly who to run the first test on, and those readers are the ones most likely to respond. An account that changes subject every week resets that audience each time.

Asking for the action you want. A question at the end of a post earns comments. "Agree?" earns reactions. A post that ends with a statement earns whatever it earns. None of this is manipulation; it is telling the reader what kind of response you would find useful.

What a good rate looks like for you

The platform average is a floor to clear, not a target to hit. The number to beat is your own.

Take your last twenty posts, work out the rate by impressions for each, and find your median. Use the median rather than the mean, because one post that travels unusually far will drag an average upward and make every normal post look like a failure against it.

That baseline says more about your audience than any benchmark, because it is measured on exactly the people you care about. From there, the question changes from "am I above average?" to "which of my posts beat my average, and what did they have in common?"

That second question is the one worth answering, and it usually has a clear answer within a month of looking. The hooks that worked, the formats that held people, the subjects your readers actually turn up for. Write more of those.

The benchmark tells you where the platform is. Your own history tells you where to go.

FAQ

What is a good engagement rate on LinkedIn?

Above the 2026 platform average of 5.20% by impressions is good. Document posts, the carousel format, average 6.60% and multi-image posts 6.90%, so a strong visual post should clear a higher bar than a text one. Above 8% on a text post is exceptional. Compare against your own median before you compare against the platform.

How do I calculate LinkedIn engagement rate?

Add reactions, comments, and reposts, divide by impressions, and multiply by 100. That is the version the benchmarks use. Dividing by followers instead gives a much smaller number that answers a different question, and LinkedIn's own dashboard adds clicks to the numerator, which makes its figure higher than either.

Is a 2% engagement rate on LinkedIn bad?

By impressions, it is below the platform average and worth looking into, starting with the hook and the format. By followers, 2% would be excellent, because most followers never see any given post. Check which one you have before you act on it.

Why is my company page engagement so low?

Because company pages earn a fraction of what personal profiles do, about an eighth on average, and their organic reach has dropped roughly 60% since 2024. It is the platform, not the page. If reach matters, the people at the company need to post.

Which LinkedIn format gets the most engagement?

Multi-image posts at 6.90% and document posts, the carousel format, at 6.60% lead the 2026 benchmarks, both well clear of the 5.20% average. Posts with an external link are last at 3.30%.

Why does my rate fall as my post reaches more people?

Because the denominator grows faster than the interactions do. A post that escapes your immediate network reaches colder readers who engage less, so a falling rate on rising reach is usually a post doing well, not badly. Read the rate next to impressions rather than on its own.

Does engagement rate matter more than followers?

For anything you want to happen, yes. A thousand followers who respond will get you a conversation; fifty thousand who scroll past will not. Growth follows engagement, rarely the other way round.

Three serious studies are current. Each analyzed millions of posts, each is dated this year or last, and they disagree with each other by as much as twelve hours. That disagreement is the most useful thing on this page, because it tells you the honest answer is not a chart at all.

So this post has both halves. First, what Sprout Social, Buffer, and Hootsuite actually found, day by day, with their sample sizes and dates. Then the four-week method for finding the window that fits your audience rather than theirs.

Where the studies agree

Start from the consensus, because it is free and it holds.

All three rate Wednesday in their top tier. Midweek beats the edges of the week in every dataset, and weekends are the weakest ground in all three, which fits a professional network read mostly on work rhythms. That converts to one line: post Tuesday to Thursday unless your own data says otherwise.

What the consensus does not give you is an hour. Here is why.

The three studies, side by side

Each study is serious, current, and measuring a different crowd.

Drawn on one track, the disagreement is obvious.

They are not wrong. Each is measuring its own customers, and those customers live in different industries, regions, and time zones. Sprout's sample leans toward the brands and agencies that buy Sprout. Buffer's is the posts its users schedule. Hootsuite's spans 118 countries, which pulls the clock in every direction at once. An average of millions of unlike audiences describes none of them exactly.

Buffer's own note is the clearest evidence that these numbers move: peak engagement in its 2025 analysis sat inside working hours and fell off after 5pm, and in the 2026 data those peaks shifted later into the evening. A chart with no date on it is not a finding.

The best time to post tool on this site draws all three on one track, named and dated, and converts every window to your own clock.

Best time to post on LinkedIn, by day

Here is every day with what each study found. Sprout reports peak windows, Buffer reports its top three slots, and Hootsuite reports a single best hour, so the columns are not the same shape. All of them are in the audience's local time.

Monday

The weakest weekday in two of the three datasets. Sprout gives it a single hour at lunchtime, and Buffer explicitly puts Monday among the lowest-engagement days of the week. People are clearing an inbox, not reading a feed. If you post once a week, this is not the day.

Tuesday

The first strong day. Sprout gives Tuesday the widest window of any day, 11am straight through to 5pm, and Buffer's top slots cluster in the late afternoon. Hootsuite is the outlier at 6am to 8am, which is what an audience spread across 118 countries looks like when you average it. Tuesday is the safest day to start a test on.

Wednesday

The one day every study rates highly. Sprout has 11am to 4pm, Buffer's top three are 4pm, 3pm, and 5pm, and Hootsuite has 9am. If you take exactly one thing from the research, take Wednesday.

Thursday

Almost as strong as Wednesday, and the day where Buffer's later shift shows most clearly: its top three run 5pm, 7pm, and 9pm, well into the evening. Sprout still has the afternoon. Thursday is the natural second slot in a two-posts-a-week rhythm.

Friday

Attention thins after lunch, and both Sprout and Buffer put their windows before the day ends. Hootsuite's 8pm is an outlier worth ignoring unless your audience is genuinely nocturnal. Friday works for lighter posts, the ones that trade on personality rather than a claim you want scrutinized.

Saturday and Sunday

Weakest in every dataset, and the one point where all three agree without qualification. The hours the studies do report at weekends are thin slices of a small number, not an opportunity. The exception is an audience of founders and freelancers who work through the weekend, which your own numbers will show if it is true for you.

The worst times to post on LinkedIn

The studies converge much faster on what does not work.

Three rules come out of that:

• Skip weekends unless your audience has told you otherwise through your own analytics.
• Be careful with early mornings if your readers are in one region. Hootsuite's early hours come from a global sample, where somebody is always at their desk.
• Do not post twice in one day. Two posts split your own audience's attention across both, and the second usually gets the worse half.

Convert to your audience's clock, not yours

This is the step most people skip, and it is why so many tests come back inconclusive.

Every study reports in the audience's local time. If you are in Karachi or Lisbon and your buyers are in New York, "11am" means 11am where they are, which is the evening or the afternoon for you. Test in your own morning because it is convenient and you have tested a window your readers were asleep for.

Decide where your audience mostly is and run the test on that clock. If they are spread across the US and Europe there is no single clock to optimize, and a window like late morning Eastern is best read as "working hours where most of your readers are". The best time tool does the conversion for any pair of zones, with daylight saving worked out for today.

Scheduling solves the inconvenience. A post can publish at 11am Eastern while you are at dinner, as long as you are back for the replies within the hour, because being present after publishing is distribution work, not politeness.

How to find your own best time

Everything above is a starting point. Your own posting history is a study of exactly the audience you care about, with a sample nobody else has. Here is how to read it in four weeks.

It is tempting to copy an account you admire instead. The post date extractor reads the exact time a post went up from its link, so you can see their habit, but it is their audience's clock, not yours.

Step 1: Start from what the studies agree on

Take midweek as given and stop researching it. Post Tuesday to Thursday, in a window that is plausible for where your readers actually are. You are not testing the day. The three studies have already settled that, and the day is the part they agree on.

Step 2: Pick two windows and alternate them for four weeks

Choose two candidate windows. If you sell to US companies, that might be late morning Eastern and mid-afternoon Eastern. If your readers are founders in Europe, early morning and lunchtime.

Then alternate. Post in window A on Tuesday, window B on Wednesday, A on Thursday, and swap the order the following week.

Six posts per window is enough to see a pattern and not enough to be sure, and that is fine. You are looking for a difference big enough to notice, not a p-value.

Two rules keep the test honest:

• Hold everything else steady. Same kind of post, same length, same amount of replying afterwards. If window A gets your best post of the month, the test is measuring the post.
• Do not post in both windows on the same day. Two posts a day split your audience and muddy both readings.

Step 3: Read the first hour, not the total

LinkedIn tests a new post with a small slice of your network first and widens distribution based on how that group responds. How the algorithm works in 2026 covers the rebuilt ranking system behind that. This is why the first hour matters more than anything you do afterwards, and it is also why total engagement is the wrong number to compare.

A post that gets a slow start and then a late lift from one large repost will show a good total while telling you nothing about timing. What timing controls is how quickly the first readers arrive.

So for each post, note the engagement in the first sixty to ninety minutes: reactions, comments, and whether anyone you do not know turned up. Compare those early numbers across the two windows. If one window consistently gets your first ten reactions faster, that is your answer, and you will usually see it by week three.

Worth being precise about what is known here. LinkedIn has said early engagement influences how widely a post is distributed. It has never published a fixed sixty or ninety minute window, and Buffer says the same in its analysis. The first hour is a useful habit, not a documented switch.

For a cleaner read on each post, the engagement rate calculator benchmarks it against the platform average, so a good Tuesday and a bad Thursday are compared on the same scale. What counts as a good rate in 2026 has the current benchmarks by format.

Step 4: Convert the winner to a schedule

Once one window is ahead, write it down as an hour in your audience's zone, not yours, and put it in a queue. This is the step where good tests die. People find the window, then keep publishing whenever they happen to open the app, and three months later the finding is gone.

Scheduling is also what makes the first hour survivable. Pick the window you can actually be present for, or accept that you will publish at the best hour and reply an hour late, which trades some of what you just measured.

Step 5: Lock it in, then retest each quarter

Stop testing and stick with it. Post in the winning window consistently for the next two or three months. Consistency of time helps the same way consistency of subject does: the readers who respond to you learn when to expect you, and the system learns who to run the first test on.

Then retest, because the numbers move. Buffer's peaks shifted later between its 2025 and 2026 analyses, and your own audience changes as it grows, shifts industries, or spreads across more time zones. One quarter is a sensible interval: long enough to have compounded, short enough to catch a drift.

Keep the retest small. Your locked window against one new candidate, three weeks, first-hour numbers. If the incumbent wins, nothing changes. If the challenger wins clearly, switch.

How much timing actually matters

Less than the post. Timing decides how quickly your first readers arrive; the content decides what they do, and what they do decides the reach. A strong post at a mediocre hour outperforms a weak post at the perfect one, every time.

Treat timing as a small, free gain. Worth taking, because it costs nothing once you know it, and not worth agonizing over, because the rest is what you wrote. If your numbers are flat across every window you try, the problem is not the clock, and the tips that actually move a post are a better use of the next month.

FAQ

What is the best time to post on LinkedIn?

Midweek, and beyond that it depends on your audience. Sprout Social, Buffer, and Hootsuite all rate Wednesday highly and weekends poorly, and they disagree on the hour by as much as twelve hours because each measures a different customer base. Sprout points at 11am to 5pm, Buffer at 3pm to 8pm, and Hootsuite at early morning. Your own analytics beat all three.

What is the best day to post on LinkedIn?

Wednesday. It is the only day that appears in the top tier of all three current studies, with Tuesday and Thursday close behind. Monday and Friday are mixed, and weekends are the weakest ground in every dataset.

What is the worst time to post on LinkedIn?

Weekends, in all three studies. Buffer also finds Monday and Tuesday the weakest days of its week and the hours before noon weaker than the afternoon. The one thing every dataset agrees on is that Saturday and Sunday are not worth a good post.

How long does it take to find my own best time?

About four weeks of alternating two windows, reading the first-hour engagement rather than the total. Most people see a clear winner by week three. Retest once a quarter, because the platform's own peaks move.

Should I post in my time zone or my audience's?

Your audience's. The studies report in the audience's local time, and a test run in your own morning has tested a window your readers may have been asleep for. Decide where your readers mostly are and schedule on that clock.

Does the day matter more than the hour?

For most accounts, yes. The studies agree on the day and disagree on the hour, which tells you the day is the reliable signal. Get midweek right first, then refine the hour with your own data.

Is it worth posting on weekends?

Rarely, for a professional audience. Every current study finds weekends the weakest ground. The exception is an audience of founders and freelancers who work through them, which your own numbers will show if it is true for you.

Does the first hour after posting really matter?

Early engagement does influence how widely a post gets distributed, and LinkedIn has said so. What has never been published is a fixed window, so treat "the first hour" as a useful habit rather than a rule: be there to reply while the post is new, and do not post at an hour you cannot be present for.

How often should I post on LinkedIn?

Two to four times a week suits most people, and consistency beats volume. Twice a week for six months does more than five times a week for three weeks followed by nothing.

This is the whole spec, what actually happens to the image on the page, and what to put on it once the dimensions are right.

LinkedIn banner size: personal profile and company page

LinkedIn calls the personal one a background photo in its own settings. Everyone else calls it a banner, a cover photo, or a header. It is the same image.

Both figures come from LinkedIn Help: the profile numbers from its photo upload requirements, the page numbers from its image specifications for Pages and Career Pages. Checked September 2026.

Two details from those pages that catch people out. LinkedIn does not accept GIFs for a profile photo or background photo, so an animated banner is not an option. And the two limits are different: 8MB on a personal profile, 3MB on a page, which matters if you are exporting a photograph rather than a flat design.

Upload at those dimensions or at an exact multiple of them. The free banner maker on this site exports the personal size at 2x, 3168 by 792, so it stays sharp on retina screens and LinkedIn scales it down cleanly. What you should not do is upload a wider image and let LinkedIn crop it, because you do not control where the crop lands.

The two shapes are not interchangeable. A 4:1 personal banner dropped onto a company page loses a slice from the top and the bottom, which is usually where the text was.

Every other LinkedIn image size

While you are in there, the rest of the specs LinkedIn publishes:

The link post size is the one people forget. When you share a URL, LinkedIn pulls the page's own preview image, and if that image is not close to 1.91:1 it gets cropped in the feed. The link preview checker shows what LinkedIn will actually pull before you post.

Where your profile photo lands

On a personal profile, your photo sits over the lower-left of the banner. On desktop it overlaps the bottom-left corner; on mobile it sits a little further in and covers more of the height. Anything placed there is gone.

That rules out the bottom-left roughly a fifth of the width and the lower half of the height. The dependable space is the right two-thirds, in the vertical middle.

The banner maker draws that overlap on the preview, and you can put your actual photo inside it so you are judging the real overlap rather than a guess. It is a preview layer only; it never ends up in the download.

If the photo itself needs a fresh crop, the profile picture resizer frames it in the same circle LinkedIn uses and exports a square that uploads the first time. Lost the original file? The profile picture downloader gets the 400 × 400 copy back from one of your posts. To fit an image you already have to 1584 × 396, use the image resizer, and for post images the sizes are on the post image size page.

What survives the crop

The image is not shown at one size. It is scaled to whatever width the profile is being viewed at, and on narrow screens the top and bottom edges are trimmed to keep it from getting too tall.

Three rules follow from that:

• Keep the text in the vertical middle. The middle band survives every crop currently in circulation. The top and bottom edges do not.
• Keep it off the left edge. Between the photo and the mobile crop, the left fifth of the banner is the least reliable real estate on the profile.
• Make the type large. Fine detail and small text do not survive LinkedIn's compression. If the line is not readable at a glance on a phone, it is not readable.

Flat colors and simple shapes compress well. Photographs with texture, gradients with many stops, and screenshots of anything do not. This is also why the file size limits rarely bite: a flat design at 3168 by 792 lands well under a megabyte, while a full-bleed photograph can run to several.

What to put on it

One line. The same job as your headline: what you do, and who for.

The banner is the largest text you get on LinkedIn, and most people spend it on a stock skyline, a sunset, or the default blue that has not been changed since the account was created. Someone who has just landed on your profile is deciding whether you are worth following, connecting with, or replying to. A line that states what you do gives them the context every other section then confirms. A skyline tells them you have not thought about it.

The banner and the headline are read within a second of each other, and they work when they rhyme: the banner makes the claim in large type, the headline restates it in searchable words. If you are settling both, write the headline first, with the headline generator if it helps, and put its plainest form on the banner. The profile checklist covers the rest of the sections in the same pass.

Five lines that work

The plain claim. "I help B2B founders turn LinkedIn into a pipeline." Says what you do and who for. The safest default.

The claim with proof. "Grew from zero to 7,000 followers posting once a day. Now I help teams do the same." One number, then the offer. Use a real number or none.

The topic. "Writing about pricing, positioning, and the SaaS metrics nobody explains." For people whose profile is a publication rather than a service. It sets the expectation for the posts.

The offer. "Free LinkedIn audit for the first 20 people who message me this month." For a campaign. Change it when the campaign ends, because a stale offer reads worse than no banner.

The credential. "Former Head of Growth at [company]. Now consulting." For people whose past employer is the thing that earns the click. Short, because the About section will do the rest.

What all five have in common: a person could read them on a phone in the two seconds a profile visit lasts, and each one tells the visitor what to expect from the posts below.

The mistakes that cost the most

Text in the bottom-left. The most common one, because that is where a designer's eye puts a caption. Your photo covers it.

Using the old company page size. 1128 by 191 is still the number in most guides and most template packs. Upload that to a page today and it is stretched to fill a 1512 by 256 slot, which softens every edge in it.

A collage. Four logos, a headshot, a phone number, and a QR code. None of it is readable at profile size, and it signals that the profile is an advertisement rather than a person.

Contact details. An email or a phone number on the banner cannot be clicked, and it dates the image the day either of them changes. The contact section exists for this.

A group photo. Faces at banner size are small and the crop moves them. If a face belongs on the banner, it is yours, and it is already there in the profile photo.

Leaving the default. Not a crime, but a missed slot. The banner is free positioning space at the top of the page, and the blue gradient tells people nothing.

Making it

Type your line into the LinkedIn banner maker, pick one of the eight designs, and download it at the exact size. Nothing is uploaded; the image is drawn in your browser. Add your profile photo to the preview to check the overlap, and use the corner handles to make the line as large as the space allows.

If you post on X as well, the Twitter banner maker is the same tool at X's 1500 by 500, with the avatar corner marked for that platform instead.

If the banner should show your product rather than a line, put a screenshot of it on the banner with the screenshot background maker. It sizes the image to 1584 by 396 and keeps it in the right-hand part, clear of your photo.

The banner is a one-time job, and the profile is what people check after a post earned their attention. Its job is to confirm what the post suggested. If you are judging the posts as well as the profile, what a good engagement rate looks like in 2026 is the number to hold them against.

FAQ

What is the LinkedIn banner size in 2026?

1584 by 396 pixels for a personal profile, a 4:1 ratio, as a PNG or JPG up to 8MB. Company pages use 1512 by 256, as a PNG or JPEG up to 3MB. Both figures are from LinkedIn Help. Upload at those sizes or an exact multiple, and keep important text in the right two-thirds and the vertical middle.

Is the LinkedIn company page banner still 1128 by 191?

No. LinkedIn's current image specifications for Pages list the cover image as 1512 by 256 pixels, with the same figure as both the minimum and the recommended size. 1128 by 191 is the older spec that most third-party guides are still repeating.

Is the LinkedIn banner the same as the background photo?

Yes. LinkedIn's own settings call it the background photo. Banner, cover photo, and header are the same image; only the name changes.

Why does my banner look cut off on mobile?

The image is scaled to the screen width and trimmed at the top and bottom on narrow screens. Text placed near the edges is the first thing to go. Keep the line in the vertical middle and it survives the crop.

Can I use a GIF as my LinkedIn banner?

No. LinkedIn's photo requirements accept PNG and JPG for the profile photo and background photo, and state that GIFs are not supported. An animated banner is not possible on a personal profile.

Can I use a photo as my LinkedIn banner?

You can, if it compresses well and the text stays readable over it. Textured or busy photos lose detail under LinkedIn's compression, and the crop moves whatever the photo's subject is. A flat color with a clear line outworks most photos at profile size.

Should the banner and the headline say the same thing?

Nearly. The banner makes the claim in large type; the headline restates it in the words people search for. When the two disagree, the profile reads as unfinished.

For a long time my own routine looked like this:

• Post something
• Scroll the feed
• Drop a few random comments
• Send a couple of DMs when I remembered

It felt active. It did not feel reliable.

Some weeks I would get a few promising conversations. Most weeks, nothing I could count on.

After talking to a lot of founders, sales reps, and solo creators using Depost AI, it became clear this was not just my problem. We were not missing motivation. We were missing a workflow.

Why the old outreach playbook stopped working

The classic social selling playbook is: show up every day, post good content, comment on other people's posts, send personalized DMs to prospects.

On paper that is fine. In practice it falls apart the moment you try to run it for more than a fortnight, because it relies entirely on your memory. You lose track of who was actually interested. You forget to follow up with the people who were warmest. You comment on whatever the feed puts in front of you, just to feel active. And your "personalized" messages still read as templates, because you wrote nine of them in a row.

The problem is not effort. It is that the whole thing depends on remembering things, at the exact moment the main feed is doing its best to distract you.

At some point I had to accept something:

I didn't need more content. I needed a LinkedIn outreach workflow I could repeat every day.

From feed to pipeline

The turning point was one question: what if I stopped treating LinkedIn as a feed and started treating it as a pipeline?

When you think "feed", you scroll. When you think "pipeline", you work through steps.

So the goal became narrow and specific: a five-step loop I could run in 30 to 45 minutes a day. No hacks, no 200-DM blasts. Just something repeatable.

We then built that loop into Depost AI, so it is not just a thing I describe in a blog post.

The five-step loop

1. Start from a targeted feed, not the home feed

The first rule: never start an outreach session on the generic LinkedIn home feed.

Work from a targeted feed instead, a view that only shows posts from your ideal prospects, decision-makers, warm engagers, and people already in your pipeline.

That one change turns LinkedIn from an infinite scroll into a short list of people who matter. It also means everything you do next is aimed at someone specific.

Building that list takes intent, not tooling. You add people as you come across them, from a post, a comment thread, or a profile, and the feed grows with the list.

If step one of your day is opening the main feed, you are already in reactive mode.

2. Leave five to ten real comments

From that feed, leave five to ten real comments a day.

Not "Great post!" or "Love this 🔥" or anything you could have written without reading. Aim for one of three things: a short insight, a genuine question, or an example that adds something the post did not have.

The point is to become a familiar and useful name in your prospect's world before you ever send them a message. By the time you do, you are not a stranger.

If a comment does not move the conversation forward, rewrite it.

3. Watch for warm signals

Over a few days or weeks, some people start to stand out:

• You have engaged with them several times
• They reply to your comments
• They like more than one of your posts
• They keep turning up in your notifications

These are not just people who saw you. They are people showing intent.

Most outreach fails because it skips this layer entirely and jumps straight to a cold DM. The workflow inverts it: you move when the signals are already there.

4. Send a personalized note and first message

Once someone feels familiar, outreach stops being awkward.

A good connection note references something real, their post, their role, a project, or an opinion they have stated. It is short. And it contains no pitch at all.

Then, once they accept, your first DM thanks them, ties back to something in their world rather than yours, and ends with one question that is easy to answer.

Simple first DM template: "Hey [Name], appreciate you connecting. I saw your post about [topic] and it stuck with me, especially the part about [specific bit]. Curious: how are you currently handling [related challenge]?"

No six-paragraph monologue. Just a message that makes sense given what came before it.

If you want a feel for either before committing to the loop, the free connection request generator drafts a note inside the 300 characters LinkedIn allows, and the DM generator does the same for the first message.

5. Follow up on a system you can see

This last step is the difference between predictable leads and occasional lucky ones.

Every DM you send should carry a status (New, Engaging, Warm, Contacted, Replied, Need Follow-up, Won) and a follow-up date if one applies.

Then your daily task is boring, which is the point:

1. Open your list
2. Check "Not replied" and "Follow-up due today"
3. Send one honest nudge

No mental clutter, and no "I'll remember to reply later", which is a sentence that has never once been true.

Running the loop without spreadsheets

Every step above works with a notebook and a calendar reminder. What breaks it is not difficulty, it is bookkeeping: remembering who you commented on, which note went out on Tuesday, and who never replied.

Depost AI holds that part. A targeted feed shows only posts from the people on your lists, so step one takes minutes instead of scrolling. Comments, connection notes, and first messages arrive drafted in your tone for you to edit. Every lead carries a status and a follow-up date, so "I'll remember to reply later" stops being load-bearing.

Nothing is automated in the sense that matters: no connection request, note, or message is ever sent for you. You press send on all of it, which is the point, and also why it does not carry the risk that unattended automation does. What gets an account restricted covers where that line sits.

If you would rather watch than read, this walks through the whole loop:

FAQ

What is a LinkedIn outreach workflow?

A repeatable set of daily steps that moves people from stranger to warm contact to conversation. The value is that you stop deciding what to do each morning and just run the loop.

How much time does this workflow take per day?

Thirty to forty-five minutes. It is built around consistency rather than volume, and it stops working if you try to compress a week into one Sunday.

Can this workflow work without Depost AI?

Yes. The logic is the thing, and you can run it with LinkedIn lists and a spreadsheet. Depost AI handles the targeting, drafting, tracking, and reminders so it survives a busy week, but nothing here depends on the tool.

Who is this best suited for?

B2B founders, agency owners, consultants and coaches, sales and SDR teams, and creators who want both inbound and outbound coming from the same activity.

If LinkedIn is a real channel for your business and you have been "active" for months without a system, this is the gap it fills.

Which is strange, because most people are sitting on a back catalogue of things that already worked. A post from four months ago that got real comments is a better bet than whatever you invent under pressure this morning, and almost nobody in your audience remembers it.

This guide is about mining that back catalogue: what is worth reusing, how to reshape it so it does not read as a rerun, and where the whole thing goes wrong.

What repurposing actually means

Repurposing means keeping the idea and changing everything else: the format, the angle, the opening, the length.

A repost asks people to read something twice. A repurpose gives them a different way in, which is why it does not feel like one.

Why it works

Most of your audience missed it the first time. LinkedIn shows any given post to a fraction of your network, so the people who saw your best work are a minority of the followers you had then, and a much smaller minority of the ones you have now.

Repetition is how content pillars work. Nobody remembers your point the first time. Saying the same thing four ways over three months is not being repetitive, it is being known for something.

One idea can carry a week. A single argument can be a text post, a carousel, a poll, and a longer piece, and each reaches slightly different people.

Consistency is easier when you are not starting from zero. The reason people stop posting is the blank page, not the writing.

Reposting and repurposing are not the same thing

Reposting is for your two or three genuine hits. Repurposing is for everything else.

How to spot a post worth rebuilding

Not every post deserves a second life. Look for the ones that:

• Started conversations. Comments and DMs matter more than likes here. Likes mean people agreed. Comments mean they had something to say.
• Taught something. Frameworks, walkthroughs, and specific how-tos age well. Reactions to a news cycle do not.
• Made someone feel caught out. Posts that name a thing people quietly do tend to keep working.
• Sit on your actual position. If you want to be known for one thing, repurpose the posts about that thing.
• Produced follow-up questions. A question in the comments is a brief for the next post, already validated.

The posts that flopped are usually not worth rescuing. Occasionally a good idea died because of a bad first line, and that one is worth a second attempt. The free hook generator exists for exactly that retry: new openings for a post you have already written.

Five ways to rebuild a post

Turn a text post into a carousel

Break the argument into five to eight slides, one point each, and let the design carry the pacing. Carousels hold attention longer because swiping is a decision the reader keeps making.

• Original post: "Creators give up too fast. Patience wins."
• Carousel: "Why LinkedIn creators quit too soon, and how to keep going"

Expand it into an article

Take the post that got the most comments and write the long version: the caveats, the counter-arguments, the parts that did not fit in a feed post.

This is also the version that keeps earning attention months later through search, which a feed post never does.

Turn the comments into the next post

Your comments section is a list of things your audience wants explained, written in their own words. It is the best content research available to you and it costs nothing.

• Comment: "What tools do you use to stay consistent?"
• New post: "Three things that keep me posting every week"

Change the angle, not the substance

The same claim lands differently depending on who you are talking to and how blunt you are willing to be.

• Original: "Rest is part of growth."
• Repurposed: "You're not lazy. You're recharging."

Same idea, different reader.

Make it a poll

Turn a claim into a question and let the audience argue about it:

What's your biggest LinkedIn challenge?

• A) Staying consistent
• B) Finding fresh ideas
• C) Writing a strong opening

Then write the follow-up post about what the answers revealed. The follow-up usually does better than the poll.

Doing it without the rebuild

The tedious part of repurposing is not the idea, it is the rebuild: making the same point four ways and designing something to go with each one.

Depost AI does the step people get wrong, which is the choosing. It measures your posts against your own average, so you rebuild the ones that earned it rather than the ones you happen to remember, then proposes angles on whichever you pick and drafts each version in your voice. If one wants a carousel or an infographic, it designs that too. Everything waits for your approval.

A worked example

Say this post did well:

"LinkedIn isn't about going viral. It's about staying visible."

Four things you can do with it:

1. A carousel. Slide one is the claim. Slides two to five are the evidence: what a consistent account looks like after six months versus one that got lucky once.

2. A poll.

   Would you rather have 100,000 views once, or 1,000 views every day?

3. A short video. Thirty seconds of you making the same argument. Different medium, different half of your audience.

4. A longer article. "Visibility over virality," with the numbers from your own account and what you would do differently.

One idea, four posts, and none of them reads as a repeat.

What goes wrong

Reposting too soon. Three to four weeks minimum for a true repost. Repurposing has no waiting period because the post is genuinely different.

Changing the format but not the writing. Pasting a text post onto slides is not a carousel. If the words are identical, people notice immediately.

Reusing posts that did not work. Repurposing amplifies what you feed it. Check the numbers before you commit a week to an idea that already failed once.

How to tell if it worked

Compare the repurposed version against the original, not against your best-ever post:

• Impressions: did the new format reach people the first one did not?
• Engagement rate: likes, comments, and shares per impression, since raw counts just track reach. The 2026 benchmarks say what a good one looks like by format
• Comments specifically: the clearest signal that the new angle hit something
• Follows after the post: the metric closest to "this changed how someone sees me"

If the second version underperforms the first, the format was probably wrong for that idea. Try a different one before you retire it.

You are allowed to repeat yourself

You do not owe your audience a brand new thought every morning. You owe them a clear one, and you are allowed to make the same point more than once.

Start with what already worked, change the shape rather than the substance, and keep going. That is most of what consistency actually requires.

FAQ

What's the difference between repurposing and reposting?

Reposting is the same post again with small edits. Repurposing keeps the idea and rebuilds it as something else: a carousel, a poll, a video, a longer article. The first asks people to read something twice. The second gives them a new way in.

How often should I repurpose my LinkedIn content?

You can repurpose a good post immediately, because the new version is genuinely a different post. Straight reposts need three to four weeks, and are worth doing only for the handful of posts that clearly outperformed everything else.

Will my audience get bored if I reuse old ideas?

Far less than you fear. Most of them never saw the original, and the ones who did will not remember the wording. Being known for a position requires repeating it.

How do I know which posts are worth repurposing?

Look for the ones that started conversations rather than the ones that collected likes, and check the numbers instead of trusting your memory of which posts felt good.

Can I repurpose content for platforms other than LinkedIn?

Yes, as long as you reshape it. The same idea works as an X thread, a newsletter section, or a short video, but each has its own rhythm and readers can tell when something was written for somewhere else.

The advice usually offered to busy people is to try harder: post daily, block time, build the habit. That is not a strategy, it is a wish. What follows is the version that survives a bad month.

Why it still matters

The case is narrow and worth being honest about.

People look you up. Your profile and your recent posts are what they find, and that shapes the conversation before it starts. If you are hiring, selling, raising, or job hunting, that is worth real money.

The commercial case is not subtle either. Personal profiles earn many times the engagement of company pages, and company page organic reach has been falling since 2024, so a company that puts its effort into the brand page is pushing against the platform. What a good LinkedIn engagement rate looks like has the figures and where each one comes from.

Being visible in your field also means opportunities arrive rather than being chased. Not a flood, but the occasional message that turns into something, which is more than most channels deliver.

None of this requires a large audience. It requires that the right few hundred people know what you work on. And it only works if you are still there in six months.

Where the time actually goes

Four jobs, and only one of them is the writing:

• Deciding what to write about, which is the part that eats the most energy and produces nothing visible
• Writing it, thirty to sixty minutes if you care about the result
• Making something to go with it, if the idea needs a visual
• Replying, in the hour after publishing, which is the hour you are usually in a meeting

Two or three posts a week means several hours. Most people do not have several spare hours, so they do it for three weeks, then a deadline lands, and that is that.

The fix is not discipline. It is removing steps.

The week that survives a bad month

Two posts and fifteen minutes a day of replies. That is the whole strategy, and it works because it fits in the gaps of a bad week rather than needing a good one.

Monday, thirty minutes. Pick the week's two subjects and write the first post. Only the first, because the second one is better after you have seen how the first landed.

The hour after it publishes, twenty minutes. Reply to everyone who comments, while the post is still moving.

Midweek, twenty minutes. Write the second post, using whatever the first one's comments taught you.

Every day, fifteen minutes. Comment on other people's posts. This is the first thing people cut and the thing that compounds hardest, because a comment worth reading gets you read by an audience you did not have to build.

Thirty minutes, twenty, twenty, and a quarter of an hour on weekdays comes to about two and a half hours. More than half of that is the commenting, which tells you where the value actually is. How many comments you can leave in a day covers the safe shape of that half, and what a good comment looks like covers the rest.

What to cut

Everything here is optional. Treating it as mandatory is why people stop.

• Posting daily. Twice a week for six months beats five times a week for three weeks and then silence.
• A content calendar. A note with two subjects on it is a calendar. Anything more elaborate becomes a second job.
• A graphic for every post. Text posts work. Make something only when the idea genuinely needs a picture.
• Hashtag research. A few relevant ones, picked in ten seconds, is the whole of it.
• Chasing every format at once. Pick the one you are fastest at and stay there until it stops working.
• Replying to everything in your feed. Fifteen minutes on posts where you have something specific to add beats an hour of "great point".

The four steps worth removing

They are the ones that produce nothing visible: deciding what to write about, getting to a first draft, making the graphic, and finding the posts worth replying to.

That is what Depost AI takes off the week. The ideas are ranked from your own numbers, the drafts arrive in your voice, the graphics exist, and the posts worth replying to sit in one place with a starting point on each. Every step stops at your approval, and nothing publishes without it.

The free tools cover the small jobs on their own with no login: a headline, a hook, a post preview, an engagement rate check.

Who this is for

Freelancers who plan a week on Sunday, approve everything in half an hour, and spend the rest of the week on client work.

Founders who need to be visible for hiring and fundraising but cannot make content a daily job, and whose company updates are worth more than generic advice anyway.

Coaches and consultants whose pipeline runs on inbound, where being consistently visible to a few thousand people in one niche is the whole marketing strategy.

The common thread is not company size. It is people whose bottleneck is time, not knowing what to say.

What decides it

LinkedIn does not reward the best writers. It rewards the people who are still there in month six.

If time is what stops you, adding discipline will not fix it. Removing steps will.

FAQ

How often should I post on LinkedIn to grow my reach?

Two to four times a week works for most people. Consistency matters more than volume: twice a week for six months beats five times a week for three weeks and then nothing.

What kind of posts work best on LinkedIn?

Specific things from your own work. What broke, what it cost, what you would do differently. Polished advice performs badly because there is already too much of it.

Can AI-generated posts still feel personal?

They can when the draft is built from your own published writing and you edit it before it goes out. They cannot when nobody reads them before they publish, which is why Depost AI requires your approval on every post.

Is it okay to schedule LinkedIn posts in advance?

Yes. Scheduling through LinkedIn’s official API is a normal, supported thing to do, and it is the main reason people manage to stay consistent through busy weeks. Just be there for the comments afterwards. If you are choosing the hour, how to find your own best time to post walks through a four-week test that uses your own analytics rather than a generic chart.

What's the fastest way to start seeing results on LinkedIn?

Pick one thing you want to be known for, post about it twice a week, and reply to everyone who comments. Most people fail at the "keep doing it" part, not the strategy part.

None of that is necessary anymore. Write the slides, let a generator lay them out, download the PDF, and post it. About twenty minutes, and no design tool.

What a LinkedIn carousel actually is

There is no "carousel" button on LinkedIn. What everyone calls a carousel is a document post: you upload a file, and LinkedIn turns each page into a slide people swipe through in the feed.

That matters because it tells you what the constraints really are, and there are fewer of them than most guides suggest.

Two things follow. A square PDF works at whatever size your tool exports, as long as every page is the same size, so there is no official dimension to hunt for. And 300 pages is not an invitation: the limit that matters is how many slides somebody will actually swipe through, which is closer to ten.

If you want the sizes LinkedIn does publish, for banners, profile photos, and link images, they are in LinkedIn banner size in 2026.

Step 1: Write the slides before you design anything

The most common mistake is opening a design tool first. You end up with a beautiful deck that says nothing, because the layout decided the content.

Write the slides as plain text. One idea per slide, a heading and a line or two under it, nothing longer than about 35 words. If a slide needs two sentences to make its point, it is two slides.

If the blank page is the problem, the free carousel content generator takes rough material, who it is for, and what you want the carousel to do, and returns eight slides of copy built for that shape. No account, and what you type is sent once to generate the result and not stored.

Feed it something real. "How we run customer interviews that actually change the roadmap. 30 minutes, 5 questions, no feature talk allowed. We did 40 of these last year and killed two planned features because of what we heard" produces a usable deck. "Customer interviews" produces a generic one.

Step 2: Design the slides without a design tool

Paste the slides into the LinkedIn carousel generator, one box per slide, with the first line as the heading and the rest as the body.

Then make three choices, and only three:

• A style. Clean, Editorial, Bold, Frame, or Minimal. Judge them on your own cover text rather than the sample, since that is the slide that has to work.
• A palette. Twelve of them, or a custom color if you have a brand hex.
• A heading font. Sans, Serif, Rounded, Marker, Script, Poster, CAPS, or Mono.

Add an author line, which appears on every slide, and a photo or logo beside it. Slides come out at 1080 by 1080. Nothing is dragged into place unless you want it to be: you can click a heading or body to select it and move it, but the default layout is already sized for the feed.

Step 3: Export, then check slide one on a phone

Download the PDF, which is the format LinkedIn's document option wants. PNGs are there too if you want the same slides for somewhere else.

Then do the one check that separates a carousel that looks professional from one that does not: look at slide one at phone size. LinkedIn compresses uploads, and small type that was crisp on your laptop comes out soft on a phone held at arm's length. If the cover heading is hard to read, cut words rather than shrinking type. The free carousel tester shows your PDF inside a post at phone width and measures the smallest text on it.

Step 4: Write the caption

A carousel with no caption is a deck nobody has a reason to open. The caption is where the swipe gets sold.

Say what the deck contains and who it is for, in two or three lines. Do not summarize all ten slides, because then there is no reason to swipe. The hook generator is useful if the opening line is not landing, and the text formatter keeps line breaks intact through LinkedIn's editor.

Write your first comment at the same time. It is free space directly under the post, and most people leave it empty.

Step 5: Post it

LinkedIn's own steps, from its "Upload and share documents on LinkedIn" help page:

1. Click Start a post in the box at the top of your homepage
2. Click More, then the Add a document icon
3. Click Choose file and pick your PDF
4. Add a title to the document and click Done
5. Add the caption
6. Click Post

The title you give the document is shown with the slides in the feed, so it is worth more than "carousel-final-v3". Treat it as a second headline.

What goes on each slide

Structure is most of what makes a carousel work. This is the shape that holds up.

The cover is the whole post. If it does not earn a swipe, the other nine slides do not exist. What works is a claim somebody could disagree with, a question that names a problem the reader has, or a specific result. What does not work is a title. "Customer interviews" is a title. "Most customer interviews are too polite to be useful" is a cover.

Slide two says why it matters. Name the cost of getting it wrong and the reader decides the rest is worth their time.

The middle slides carry one idea each, in order. Order is doing real work here: if slide five only makes sense after slide six, people drop out at five and you never see why.

The mistakes slide is the one that shows you have actually done the thing rather than read about it. It is also the easiest slide in the deck to write, because you have made them.

The last slide asks for exactly one thing. A question worth answering beats a link, and both beat "thanks for reading", which wastes the slide where people are most willing to act.

Why carousels are worth the extra step

Carousels average a 6.6% engagement rate against 5.9% for video and 3.95% for text-only posts, and carry 39% more reach than the average LinkedIn post (Socialinsider and industry benchmarks, 2026). The full by-format table is in what a good LinkedIn engagement rate looks like in 2026.

The mechanism is not mysterious. Every swipe is a small decision to keep going, and sustained attention on a post, known as dwell time, is a signal LinkedIn reads as the post being worth showing to more people.

Two other things carousels do that a text post cannot:

• They hold a sequence. A process, a comparison, a thing that changed over time: each stage gets its own frame instead of a paragraph.
• They get saved. A deck people want to keep is a deck they come back to, although LinkedIn's saved posts list makes coming back harder than the save implies.

When the free generator is not enough

The free generator covers the common case: text slides, consistent styling, out the door in minutes. It makes one carousel at a time, in a style you pick each time.

The step after that is a deck in your own brand without choosing anything. Depost AI learns your colors and type from what you have already published, designs the graphic in the same chat as the post, and the two arrive together for approval. A photo, a chart, or an illustration on a specific slide is a job for a canvas, and the time it costs is the reason most people never made the carousel in the first place.

If you want to study decks that already work, the carousel downloader pulls a published carousel down so you can look at how it was built, slide by slide. To compare paid and AI carousel makers, see the best LinkedIn carousel makers.

What kills a carousel

• Paragraphs on a slide. Unreadable on a phone, which is where most people will see it.
• A cover that describes instead of promising. The most common failure and the most fixable.
• Inconsistent styling. Changing fonts or colors mid-deck reads as running out of time.
• Too many slides. The longer the deck, the fewer people reach the end, and reaching the end is the number that matters.
• A weak close. One ask, not four.
• No caption. The deck cannot do the persuading alone.

The numbers worth watching

Likes are the least useful signal available to you. Three are better:

• Completion rate, how many people reach the last slide. The only number that tells you whether the structure held.
• Saves, the strongest indication that something was genuinely useful.
• Comment quality. Three real questions beat forty replies saying "great post".

A carousel with 40 likes and two serious inbound messages beats one with 900 likes and none.

FAQ

What size should a LinkedIn carousel be?

LinkedIn does not publish a slide size. Square slides at 1080 by 1080 pixels are the convention, because square occupies more vertical space in the feed than landscape.

How many slides should a LinkedIn carousel have?

Eight to ten. LinkedIn allows up to 300 pages, but the number that matters is how many people reach the last slide, and that gets harder the longer the deck runs. Each slide should carry one idea.

What file types does LinkedIn accept for a carousel?

PPT, PPTX, DOC, DOCX, and PDF, up to 100MB. PDF is the safest, because it is the format that preserves your layout exactly as you exported it.

How do I post a carousel on LinkedIn?

Click Start a post, click More, then the Add a document icon, choose your PDF, give the document a title, add your caption, and post. The document title is displayed above the slides, so write it as a second headline.

Can I make a LinkedIn carousel with AI?

Yes, for the writing and the layout, which are the two slow parts. The free carousel content generator drafts the slide copy from your rough material, and the free carousel generator lays it out and exports the PDF. The judgment about what is worth saying is still yours.

Do I need Canva or Figma to make a carousel?

No. The free carousel generator lays out text slides with consistent styling and exports the PDF, and Depost AI designs the deck in your own brand alongside the post. A canvas is only worth opening when a specific slide needs photography or a chart.

Why does my carousel text look blurry on LinkedIn?

LinkedIn compresses uploads. Small type survives that badly, so the fix is fewer words in larger type rather than a higher-resolution export. Check the cover at phone size before you post.

What topics work best as carousels?

Anything with a sequence: steps, a breakdown of something that went wrong, a comparison, a framework. Opinions and short stories usually do better as text posts, where the argument arrives in one piece.

We started building it eight months ago to solve a problem we had ourselves:

We're not copywriters. But we wanted to build an audience.

It turned out a lot of people were stuck in the same place. They had something worth saying and no reliable way to get it written down and published week after week.

Why We Built Depost AI

Posting consistently is hard, but not for the reason people assume. The writing is not the bottleneck. Deciding what to write is, and so is everything that comes after: making a graphic, picking a time, replying to the comments that follow.

Each step is small. Together they are an hour a day, which nobody has.

So we built a platform that handles the steps and leaves you the decisions. It is trained on hundreds of thousands of high-performing posts, and it learns your voice from what you have already published rather than from a form you fill in.

The Journey So Far

We started small, with a simple idea and a big goal.

Five months ago we shipped the first version, a fairly narrow tool for drafting posts and replies. It was useful, and it taught us the thing we most needed to know: people did not want faster writing. They wanted the whole loop taken off their plate, from figuring out what to post through to knowing whether it worked.

Thousands of people used that first version. Their feedback, and their complaints, are most of what shaped this release.

What's New in Depost AI 1.0

• Post creation. Turn plain text, a blog URL, or a half-formed thought into a finished LinkedIn post. Adjust tone and length, and publish when you are happy with it.
• Content ideation. Ideas in your brand voice, so the week does not start with a blank page.
• Inspiration library. Over 100,000 high-performing posts, filterable by topic, format, and style, for when you want to see how someone else built an argument.
• Bookmarks. Save anything worth returning to and organize it by format, tone, and relevance.
• Repurposing. Turn an old post into a new one with a different angle or format.
• Post editor. Rewrite, restructure, and adjust tone, with your voice preserved.
• Scheduling. Write now, publish later, at times your audience is actually reading.
• Engagement. Replies drafted in your voice for LinkedIn, X, and Reddit. You read them, edit them, and send them yourself.
• Smart DMs. Context-aware replies to messages that still sound like you, ready for you to send.

The rule we set for ourselves early: nothing goes out that you have not seen. No automated comments, no automated connection requests, no posting on your behalf while you sleep. Those are the behaviors that get LinkedIn accounts restricted, and no amount of convenience is worth that.

What's Coming Next

This is a starting point, not a finished product. On the list for the coming months:

• Audio and video to post, so a conversation or a recording becomes a draft
• Multiple brand voices, for people writing as more than one person
• A content calendar you can see and plan against
• Analytics, so what you post next is decided by what already worked
• Image generation and infographics
• Workspaces and team collaboration
• Content agents that plan a whole month rather than a single post
• A mobile app, because most approvals happen away from a desk

Thank You

To everyone who tested this, sent us feedback, or told us plainly when something was bad: thank you. A lot of what shipped today exists because someone took the time to explain why the old version was annoying.

We are trying to build something for people who have something to say and no interest in becoming content marketers. If that is you, we would like to hear what we still get wrong.

Start a free trial and tell us.

The Depost AI Team

The accounts that grow fastest are usually not the best writers. They are the ones in the comments, every day, saying something worth reading on other people's posts. A good comment costs you ninety seconds and puts your name in front of an audience someone else spent months building.

The problem is not that people disagree with this. It is that commenting well, every day, is genuinely hard to sustain.

Why commenting is the cheapest reach on LinkedIn

One caution before the tactics: LinkedIn now names comment automation and engagement pods in its own enforcement language, so the shortcuts are riskier than they were. How the algorithm works in 2026 has the detail.

It puts you in front of somebody else's audience

LinkedIn's algorithm rewards active accounts. According to LinkedIn, members who engage weekly receive 5x more profile views, 4x more connection requests, and 7x more inbound messages.

A comment is also a post you did not have to write. It appears under content that is already getting attention, and if it is good, people click your name.

This is the part that surprises people: on a busy post, a thoughtful comment can be seen by more people than your own last three posts combined. You borrowed the author's audience, and it cost you a minute.

It starts relationships before you ask for anything

Connection requests from strangers get ignored. Connection requests from someone whose comments you have been reading for a month get accepted.

Commenting is how you stop being a stranger before you ask for anything. Most of the useful relationships on LinkedIn start with several months of showing up in someone's replies, not with a pitch.

It builds a reputation without publishing

This is not a soft benefit, and it is the core of LinkedIn personal branding. Three in four decision-makers trust thought leadership over product sheets and marketing material, and 95% of the hidden stakeholders in a buying group are more open to outreach after strong thought leadership (LinkedIn and Edelman, 2025).

You do not need to publish regularly to earn that. If you consistently say sharp things about pricing, or hiring, or infrastructure, in other people's threads, people start associating you with that subject.

That is a reputation built entirely out of comments, and it is available to anyone who does not have time to write posts.

It gets you read by people who hire and buy

Recruiters, buyers, and future co-founders read comments. Familiarity is most of what turns a name into a conversation, and comments generate familiarity far faster than an occasional post does.

What a good comment looks like

Say something only you could say

"Great post" and "Totally agree" are worse than nothing. They take up space and signal that you did not read it.

Say the specific thing you thought while reading:

"I've started using Pomodoro blocks for deep work and it's made a real difference. Good reminder."

Twenty words that could only have been written by someone who read the post beat two hundred generic ones.

Ask a real question

A question does two jobs: it shows you engaged with the argument, and it usually gets a reply from the author, which puts the exchange in front of more people.

"Interesting take. How do you see this working for remote teams spread across several time zones?"

Ask something you actually want answered. Rhetorical questions read as performance.

Add a number or an example

The most valuable comment is the one that adds information the post did not have. Your own experience counts, especially the parts that did not work.

"We tried switching to async standups and saved around 4 hours per week, definitely worth exploring."

Numbers from your own work are more persuasive than any statistic you could quote, and nobody can argue with them.

Praise the specific thing

Praise is fine. Inflated praise is not, because it makes everything else you say worth less.

Instead of: "This is the best post ever!"

Try:

"I appreciate how you broke this down. It's rare to see such a practical take."

Tag one person, with a reason

Tagging someone who genuinely has something to add is useful. Tagging three people to farm reach is obvious, and it annoys everyone you tagged.

"@AhmedAli this aligns with what you were saying about team productivity last week."

One tag, with a reason, or none.

The daily version is the hard part

Knowing what a good comment looks like is the easy part. Doing it on a Tuesday with four meetings is the hard part, and that is the gap Depost AI is built for.

Half the work is finding the posts worth your time. Instead of scrolling the home feed, you build targeted feeds around the people who matter to you, so what you should be replying to is in one place. The LinkedIn AI comment generator then reads the post and drafts a reply that fits it, in a voice learned from your own writing, and you pick the angle: add a point, ask a question, or back the author up.

Drafts are a starting point, not an output. Rewrite a line, change the tone, cut it in half, or throw it away. The point is never staring at an empty comment box, not outsourcing your opinion. Nothing is posted for you, which matters more than it sounds: software that comments under your name unattended is the behavior that gets accounts restricted.

If you want to see the drafting before connecting anything, the free AI comment generator takes a post and writes a reply in your tone, in the browser.

The same drafting works beyond LinkedIn: AI replies on X, AI Reddit comments, AI replies on Threads, and Facebook AI comments, each with its own rules for length and register.

The part most people quit

LinkedIn is not only about writing posts that travel. Half of it is being present in other people's threads, saying things worth reading, for long enough that people know who you are.

That is unglamorous work, and it is the part most people quit. Anything that makes the daily version easier is worth more than another posting tip.

FAQ

Why should I focus on commenting instead of just posting on LinkedIn?

Because comments are cheaper and compound faster. A post requires an idea, a draft, and an edit. A comment requires ninety seconds and puts you in front of an audience someone else built. According to LinkedIn, posts that attract comments can see up to 3x more visibility, because engagement signals tell the algorithm the content is worth showing more widely. Do both if you can, but if you only have time for one, comment.

How often should I engage with others on LinkedIn?

Ten thoughtful comments a day, in the same handful of threads and communities, is a rhythm most people can hold. If commenting is your main channel, 50 to 100 across two or three sessions is sustainable, and how many comments a day is safe covers the shape that keeps it that way. Twenty minutes done consistently beats a three-hour burst once a fortnight.

Can AI-generated comments sound authentic?

They can when they are drafted from your own writing and you edit them before sending. They cannot when they are posted automatically and untouched, which is why Depost AI never posts a comment for you.

Will using AI tools violate LinkedIn's terms?

Automating activity at scale will get an account restricted. Drafting a reply that you then read, edit, and send yourself is no different from using a spellchecker. Depost AI sends no automated comments, DMs, or connection requests by design.

How do I choose the right tone when replying to posts?

Match the room. A leadership post from a CEO and a career win from a peer call for different registers. If you are unsure, go plainer rather than warmer, since forced enthusiasm reads worse than being a little dry.

What makes a good LinkedIn comment?

It adds something the post did not have. Instead of "Great post," try:

"We ran this last year and it moved lead quality more than volume. Curious whether you saw the same split."

That gives the author something to reply to, which is what turns a comment into a conversation.

The posts that travel tend to share a handful of unglamorous habits. Here are ten of them, in roughly the order you should worry about them.

What Changed in 2026

LinkedIn rebuilt feed ranking in March 2026 and, unusually for a platform, published how it works. Two things in that announcement change how far a post travels.

Relevance can now outrank recency. A post is no longer racing a clock. LinkedIn matches posts to readers by meaning, using a retrieval stage built on an LLM and a ranking model that reads more than a thousand of a reader’s past interactions, so a strong post can surface days after you published it. The first hour still shapes how widely LinkedIn tests the post. It no longer closes the case.

Engagement bait is pushed down by name. LinkedIn listed what it reduces: repetitive low-substance posts, engagement bait, and video that does not match the text. It named comment automation and engagement pods separately, as enforcement rather than ranking, which is the harder consequence. How the LinkedIn algorithm works in 2026 has the detail and the sources.

The ten tips below still hold. What changed is why they work: they make a post worth showing to people who were not in the room when you published it.

1. Know Your Audience Inside and Out

Writing for "professionals" produces posts that could have been written by anyone, about anyone. They get skimmed and forgotten.

Get specific about three things:

• Who they are: industry, seniority, whether they buy or recommend
• What keeps them up: the problem they would pay to make go away
• How they talk: the words they use for that problem, not the words your marketing team uses

The last one matters more than people expect. If your audience says "we can't get anyone to reply to our outreach" and you write "optimizing top-of-funnel conversion," you have lost them in the first line.

You already have the research. Read the comments on your last ten posts, the questions in your DMs, and the objections in your sales calls. That is your content calendar.

2. Start with a Powerful Hook

LinkedIn shows roughly two lines before the "see more" cut. Everything you have written below that is invisible until someone decides those two lines were worth the click.

So write the hook last, once you know what the post actually says, and make it do one job: create a gap the reader wants closed.

A few openings that tend to work:

• A claim people will argue with: "Posting daily is the worst advice on LinkedIn."
• A surprising stat: "95% of posts get under 10 likes. Here's why."
• An admission: "I almost quit posting last year. Here is what changed."

What does not work: throat-clearing. "In today's fast-paced digital landscape" tells the reader nothing and costs you the only two lines you get.

If you want openings to choose between, the free hook generator drafts several for a post you have already written.

3. Make Your Content Mobile-Friendly

Over 65% of LinkedIn members scroll on mobile, in a queue, with about four seconds of patience. A dense paragraph on a laptop becomes a grey brick on a phone, and well-formatted posts can see 40% more engagement than the same words in a block.

• Keep paragraphs to one to three lines
• Put a blank line between thoughts
• Use bullets when you are listing, prose when you are arguing
• Cut the post by a third before you publish it

That last one is the only editing rule that reliably works. Almost every draft is too long, and the parts you cut are almost never missed.

4. Stay Relevant with Trending Topics

Commenting on something the industry is already talking about buys you attention you did not have to build. The catch is that everyone else is commenting too.

So bring something they cannot: your own numbers, your own client's reaction, the thing the coverage got wrong. A post that says "here is the news" is worth nothing. A post that says "here is what the news means for people doing my job" is worth a lot.

If you have nothing to add, skip it. Reposting the same take with a different emoji is how a feed goes stale.

5. Post at the Right Time