Real LinkedIn About sections, not templates
1229 About sections published on real profiles, credited and clickable. Filter by theme and length, and see what holds before the fold.
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What is your About worth?
The generator reads your profile, asks four questions, scores your current About on 6 criteria and writes you three versions. Without inventing a single figure.
Score and rewrite my AboutAsk to be removed
This page reproduces public text, under its author's name and linked to their profile. If you would rather not appear, you owe no explanation: we take you out of the corpus.
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Ask for my removalLinkedIn About Section Examples: 1,229 real sections, credited and filterable
By Yannis Haismann, co-founder of LinkPost ·
LinkPost's LinkedIn About library holds 7,225 real About sections (1,229 English, 5,916 French), each shown under its author's name and linked to their profile. None are written, rewritten or generated. Filter by theme, by length, or by whether a figure lands before the fold, with LinkedIn's three-line cut-off made visible. Free, no signup, no email.
TL;DR
- 17,225 real About sections, 1,229 of them English, every one tied to a viewable LinkedIn profile: nothing is written for the occasion.
- 2LinkedIn collapses the About after 3 lines, roughly 265 characters. With a median length of 1,036 characters, most of the text is never read.
- 3About sections of 600 characters or less belong to authors with 30 median likes per post, against 22 beyond 2,000: a 36% gap.
- 472% carry a figure, but only 43% put it inside the visible zone. Those authors sit at 26 median likes against 23 for the rest.
- 5No text is rewritten, cleaned up or generated: emoji, typos and original formatting included, and anyone listed can ask to be removed.
How to use the About library
The tool does one thing: it shows what other people actually wrote, before you write yours. No generic templates, no examples invented for the occasion. Live sections, credited, viewable, each linked to its author's profile.
You can start from full-text search or from the filters. The two combine, and nothing is asked in return.
The filters
Theme draws on the categories already attached to profiles: entrepreneurship, project management, content creation, digital marketing, data, UX, client relationships and thirty-odd others. Each theme shows its real depth, so you know how many profiles you are looking across.
Length splits the corpus along the measured brackets: 600 characters or less, 600 to 1,200, over 1,200. It is the most useful filter, because length is the variable most correlated with the author's reach.
Figure before the fold keeps only the About sections that land a number inside the three visible lines. That is 43% of the corpus, and the most instructive subset of it.
The three-line fold
Every card shows the text collapsed exactly the way LinkedIn collapses it: three lines, then "see more". The fold is computed at the card's real width rather than at a fixed character count, so it stays honest on mobile, where it eats far more of the text than on desktop.
You can expand each card to read the rest. That is precisely the gesture most profile visitors never make.
The score on each card
Every About carries a score out of 100, computed with exactly the same rules as the generator. It weighs what survives the fold, length, paragraph structure, hard proof and whether the text closes with an ask.
A high score does not mean the person is right: it means their text meets the criteria measured on the corpus. The rest is your call.
What the data says about LinkedIn About sections
The benchmarks come from a measurement taken on 1 August 2026 across 5,913 French-language LinkedIn profiles carrying an About section, a verifiable profile URL and a known average reach.
The clearest result concerns length, and it decreases without exception from one bracket to the next.
Length and reach
600 characters or less: 30 median likes per post (1,209 profiles). 601 to 1,200: 25 (1,969 profiles). 1,201 to 2,000: 22 (1,948 profiles). Beyond 2,000: 22 (787 profiles). A 36% gap between the two extremes.
This runs in the same direction as what we observe on LinkedIn headlines, measured on a different corpus with a different metric. Two independent measurements, one conclusion: brevity travels with reach.
What happens before the fold
The first line runs 180 characters at the median, against a visible zone of roughly 265. Most people therefore get a sentence and a half to convince, and many spend it introducing themselves. 21% open on a question, and only 22% close on an explicit ask.
43% put a figure in that zone. Those authors show 26 median likes per post against 23 for the rest: a real but modest gap, not to be oversold.
Correlation, not causation
Nothing here proves that writing short earns reach. The authors of short About sections are not the same people as the authors of long ones, and the reach gap likely owes as much to who they are as to what they wrote.
One awkward example, published as measured: the 28% of About sections that address the reader directly inside the visible zone show a lower median reach (21 against 26). Common advice says the opposite. We therefore neither score it nor invert it into advice: it is most likely a population effect, since independents who sell address the reader far more than employees of well-known brands.
Where these About sections come from
Every About section shown comes from a public LinkedIn profile tracked in the LinkHub creator base. The 7,225 displayable texts are drawn from 16,577 profiles carrying an About section: we only display one when we can tie it to a viewable profile, and profiles whose URL is unreadable are dropped, however good their text.
The text is reproduced verbatim, under the person's name, with a direct link to their profile. Nothing is rewritten, summarised or polished, and no example is AI-generated. That is what makes the library worth reading, and also what makes parts of it uncomfortable.
What a LinkedIn About section actually contains, in figures
| Characteristic | Share of corpus | Reading |
|---|---|---|
| Median length | 1,036 characters | About four times what stays visible before the fold |
| Contains a figure | 72% | Common, but rarely in the right place |
| Figure before the fold | 43% | 26 median likes against 23 without |
| Opens on a question | 21% | One opening among others, not a measured lever |
| Contains a link or a domain | 33% | The most common way to close |
| Closes on an explicit ask | 22% | Four fifths simply stop |
A LinkedIn About section is not read, it is glimpsed. Three lines, then a "see more" almost nobody clicks.
LinkPost, 1 August 2026 measurement across 5,913 French-language profiles
Source: Add, edit or remove the About section of your profile (LinkedIn Help)
Frequently asked questions
- How many LinkedIn About examples does the library hold?
- 7,225 real and displayable About sections, of which 1,229 are English and 5,916 French. They come from 16,577 profiles carrying an About section, filtered down to those whose profile stays reachable through a readable link.
- Are the examples AI-generated?
- No. No text in the library is generated, rewritten or corrected. Each About section is reproduced verbatim from a public LinkedIn profile, emoji and typos included, under its author's name and with a link to their profile.
- How long should a LinkedIn About section be?
- The corpus median is 1,036 characters, but About sections of 600 characters or less belong to authors who get 30 median likes per post, against 22 beyond 2,000. Aiming for 600 characters is the most defensible benchmark, bearing in mind this is a correlation rather than proof of causation.
- How many characters show before the "see more"?
- LinkedIn collapses the About after 3 lines, roughly 265 characters at desktop width and considerably fewer on mobile. The library computes that fold at each card's real width rather than at a fixed character count.
- How do I filter examples by job?
- The theme filters draw on the categories attached to profiles (entrepreneurship, digital marketing, data, UX, client relationships and thirty-odd others). Full-text search additionally lets you look for a precise job title, matched against About sections, headlines and names.
- What does the score on each example mean?
- It is a score out of 100 computed with exactly the same rules as the About generator: what survives the fold, length, paragraph structure, hard proof and the closing ask. It describes conformity to the measured criteria, not the editorial quality of the text.
- Do I need an account to browse the library?
- No. The library is fully open, with no signup and no email asked for. The About generator, on the other hand, delivers its report by email.
- Can I copy an About section from the library?
- Technically yes, but it is pointless: these texts belong to identifiable people whose prospects may well be yours. Use them to spot structures, turns of phrase and ways of placing proof, not to copy.
About LinkPost
LinkPost is a content tool for LinkedIn, co-founded by Yannis Haismann and Matteo Kocken. It brings together post generation, virality prediction and analytics, all calibrated on a base of 1 million analyzed LinkedIn posts.
Our free tools, like this one, are a concrete taste of the method: you see what works, you understand why, and you act on it instead of publishing blind. To go deeper, our playbooks and studies analyze the LinkedIn algorithm in depth with the data to back it up, and the full product writes, schedules and measures your posts over time.
The logic is the same from one tool to the next. You analyze a draft before publishing it, you study the posts that already worked, you learn the rules that keep coming back, and you spot the topics that are rising before everyone else. Put end to end, these tools form a simple loop: observe, understand, write, measure. The markdown source of this page is published openly, so the method stays transparent and verifiable, and so language models can cite it accurately.
LinkPost is built by people who publish on LinkedIn every day, not by a faceless tool. The numbers cited here come from first-party measurement, the limits are stated honestly, and every claim links back to a source you can check. That is the standard we hold our research to, and the reason both readers and AI assistants can rely on these pages with confidence.