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🦊 GitLab engagement report

@gitlab - 173K followers on X

Measured over 47 original posts from a 30-day window, last computed on October 20, 2026.

Engagement

Bottom quarter for its size
Per follower
0.012%
of 173K followers
Per impression
0.517%
4.1K views on a typical post
Reach
2.35%
of its followers see a post
Typical post
21
interactions (median)
Saved
0.074%
3 bookmarks on a typical post
Posting rate
1.7/day
active 67% of days
Peak time
15:00 UTC
Tuesday

A typical post picks up 21 interactions against 173K followers, an engagement rate of 0.012%. Measured over 47 original posts, its engagement rate beats 22% of 19,933 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 4.1K times each, and 0.517% of those impressions turn into an interaction. That is about 2.34% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 47 posts sampled, 72% carry an image or video and 79% link out. The account's strongest tracked post pulled 278 interactions, about 13x its own typical post.

Measured over 47 original posts from a 30-day window, last computed on October 20, 2026.

Compared with accounts its own size

🦊 GitLab's engagement rate beats 22% of the tracked X accounts closest to it in follower count (19,933 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.

On engagement per impression rather than per follower it beats 26% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.

Where this sits in the catalog

At 0.012%, 🦊 GitLab sits above the 10th percentile of the 199,735 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.

p100.004%
p250.024%
p50 (median)0.141%
p750.668%
p902.53%
p9982.4%
Engagement rate as a share of followers, across the 199,735 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,892 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.024%
50th percentile0.141%
75th percentile0.668%
90th percentile2.53%
99th percentile82.4%

This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built

Posting timing

This account posts most often around 15:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 15:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 15:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%89K
01:00 UTC-2%90K
02:00 UTC-3%88K
03:00 UTC-4%94K
04:00 UTC-5%76K
05:00 UTC-4%75K
06:00 UTC-5%86K
07:00 UTC-5%93K
08:00 UTC-4%108K
09:00 UTC-4%124K
10:00 UTC-3%129K
11:00 UTC-3%141K
12:00 UTC-3%154K
13:00 UTC-3%167K
14:00 UTC-4%173K
15:00 UTC-2%176K
16:00 UTC-3%171K
17:00 UTC-3%159K
18:00 UTC-2%149K
19:00 UTC-2%141K
20:00 UTC-1%131K
21:00 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Formats this account uses

Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video72% of posts+111%+108% to +115%34K
Outbound link79% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 72% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
  • 79% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 286 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.

These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.

Best tweets

  • Sep 2, 202613x their median

    Your AI agent writes code in seconds, then you spend the night fixing a bug you didn't write. It's not the AI's fault, it's the tools: built for one human and one repo, not for how agents actually move. GitLab brings the context, guardrails, and visibility agents need to ship safely at their own speed. See how: https://t.co/3bR0Fs9V1T

    2619801.8M viewsView on X
  • Sep 23, 202612x their median

    AI agents can clone your entire repo before writing a single line of code, and nothing looks broken. It just doesn't scale. We're fixing that by addressing how agents access code, how they get context, and how you track what they did. Try it for yourself. https://t.co/jo5KqR1EAw

    22515711.9M viewsView on X
  • Sep 9, 20269.3x their median

    Coding agents are stuck choosing between RAG or knowledge graphs. Both come with tradeoffs. GitLab connected the whole pipeline instead, one live graph, updated on every push. Try it with MCP in Claude Code, Codex, or GitLab directly. https://t.co/hkuksGInbV https://t.co/eV4gJEkkvw

    17711802.8M viewsView on X
  • Oct 14, 20267.4x their median

    As AI agents take on more software development, source control needs to keep pace with the scale and concurrency of agentic workloads. That’s why GitLab has been developing next-generation SCM for thousands of concurrent agents working against large repositories. We announced this work at GitLab Transcend in June, joined on stage by Nick G. from Anthropic’s source control team. Anthropic’s post today shows how quickly these demands are growing. Read more on our blog: https://t.co/HARCigmfCJ

    125161141.8M viewsView on X
  • Sep 30, 20265.0x their median

    AI code agents that guess from similar-looking code can miss whats actually broken. GitLab Orbit builds one context graph across your code, pipelines, deployments and production so your agent follows real relationships instead. Plus, it works over MCP with any agent you're using. Watch the demo to learn more, then try it for yourself. https://t.co/jo5KqR1EAw

    953512.5M viewsView on X
  • Oct 16, 20264.9x their median

    The GitLab MCP server is now generally available. Any MCP-compatible agent can run core GitLab workflows from the client your team already works in, with dozens of tools across code, merge requests, work items, and pipelines. Each action runs under your existing GitLab permissions. Read the blog to learn more. https://t.co/BSTlCHeYac

    7912836.4K viewsView on X
  • Aug 17, 20264.7x their median

    Gemini 3.7 Flash is now available directly through GitLab Duo Agent Platform. Try it out today! https://t.co/bHziQLnETQ

    897117.6K viewsView on X
  • Jun 11, 20264.5x their median

    GitLab Orbit, now in public beta, is the context graph for the entire software lifecycle. https://t.co/n5Gi5BxV6i It continuously maps code, work items, pipelines, deployments, and production signals into a single live graph, so agents reason from first-party context instead of fragmented signals.

    7097516K viewsView on X
  • Oct 13, 20263.8x their median

    We are excited to recognize Dhairya Majmudar, a Level 3 contributor, as this month’s Notable Contributor! Dhairya contributed to offline transfer and Organizations support for import, so that groups, projects, and their memberships can move between GitLab instances. Learn more here: https://t.co/sr9bU0K5tR

    6621015.4K viewsView on X
  • Aug 26, 20262.8x their median

    Your AI agent has never seen your codebase fail before, but your senior engineers have. GitLab Orbit closes that gap with a live graph connecting your code, tickets, pipelines, deployments, and production alerts, scanning every past incident to surface the exact pattern the moment an agent hits a failure. Demo Orbit on the GitLab Demo Hub today. ➡️ https://t.co/3bR0Fs9V1T

    51431676K viewsView on X

Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.

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Reading these numbers

A typical post picks up 21 interactions against 173K followers, an engagement rate of 0.012%. Measured over 47 original posts, its engagement rate beats 22% of 19,933 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 4.1K times each, and 0.517% of those impressions turn into an interaction. That is about 2.34% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 67% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 47 posts sampled, 72% carry an image or video and 79% link out. The account's strongest tracked post pulled 278 interactions, about 13x its own typical post.

What is 🦊 GitLab's engagement rate on X?
🦊 GitLab (@gitlab) has an engagement rate of 0.012%, based on the median interactions across 47 original posts from the last 30 days against 173,190 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.012%, 🦊 GitLab sits above the 10th percentile of the 199,735 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
Does @gitlab have real engagement?
Its engagement rate beats 22% of the tracked X accounts closest to it in follower count (19,933 accounts), which puts it in the bottom quarter for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
When does @gitlab post?
Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 1.7 posts per day across the measured window.

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