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Walrus engagement report

@WalrusProtocol - 347K followers on X

Measured over 27 original posts from a 30-day window, last computed on October 3, 2026.

Engagement

Middle of its size range
Per follower
0.026%
of 347K followers
Per impression
0.993%
9.8K views on a typical post
Reach
2.65%
of its followers see a post
Typical post
97
interactions (median)
Saved
0.01%
1 bookmarks on a typical post
Posting rate
4.17/day
active 80% of days
Peak time
17:00 UTC
Wednesday

A typical post picks up 97 interactions against 347K followers, an engagement rate of 0.026%. Measured over 27 original posts, its engagement rate beats 48% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 9.8K times each, and 0.993% of those impressions turn into an interaction. That is about 2.81% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.2 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 27 posts sampled, 78% carry an image or video and 41% link out. The account's strongest tracked post pulled 11K interactions, about 109x its own typical post. Recurring topics include #ai, #artificialintelligence, #humanx.

Measured over 27 original posts from a 30-day window, last computed on October 3, 2026. Recurring tags: #ai, #artificialintelligence, #humanx.

Compared with accounts its own size

Walrus's engagement rate beats 48% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 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 47% 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 0.026%, Walrus sits above the 25th percentile of the 205,475 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 205,475 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,893 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 percentile0.143%
75th percentile0.678%
90th percentile2.55%
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 17:00 UTC, and Wednesday 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: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 17:00 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: Wednesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Wednesday
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 video78% of posts+111%+108% to +115%34K
Outbound link41% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 78% 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.
  • 41% 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 354 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

  • Jun 4, 2026109x their median

    Your AI agent’s memory shouldn’t be locked to one app, model, or runtime. Walrus Memory gives agents portable memory they can carry across sessions, tools, and workflows.

    8.4K1.1K9794356M viewsView on X
  • Sep 9, 202644x their median

    There is a super shady data control setting on everyone's ChatGPT which makes it seem like your data can be used for training even if you explicitly say to *NOT* improve the model for everyone (1/9)

    3.6K4321421481.6M viewsView on X
  • Jun 4, 202649x their median

    Your AI agent’s memory shouldn’t be locked to one app, model, or runtime. Walrus Memory gives agents portable memory across the tools and workflows they run in.

    2.8K4444061314M viewsView on X
  • Aug 26, 202624x their median

    working with ai agents is like if all your employees had dementia

    2.0K1221052064K viewsView on X
  • Aug 28, 202612x their median

    I receive around 3-5 requests to test a new AI product daily. Almost all of them require creating a new account and logging into a separate website or app. Meanwhile, I basically only use ChatGPT, Grok, etc to do everything on my computer and browser. These harnesses already have all my context and I don't want to start over from scratch with a new website or tool that only understands a small slice of my data. So with a few exceptions, the new products that I'm mostly likely to adopt need to work in today's top AI harnesses. I realize only a small segment of users behave this way but I bet this segment will expand drastically soon.

    925402111182K viewsView on X
  • Sep 21, 20261.9x their median

    You spend an entire D&D session slaying a dark wizard, and three sessions later, the AI Dungeon Master brings him back to life because it forgot he died. As one of our Walrus Session 7 winners, @MrRobotJi solved this by building Aetheris World Engine on Walrus Memory. Instead of relying on fuzzy text similarity, Aetheris writes game state to an immutable ledger on Walrus Mainnet: → NPC status (Deceased NPCs stay dead) → World-tick timelines (prevents timeline bugs) → SEAL-encrypted secret notes (keeps plot twists hidden from players) Read the article on @Medium: https://t.co/asQErheAFV

    157181009.8K viewsView on X
  • Sep 30, 20261.8x their median

    AI agents can act. But your data is what makes them useful. Files, history, context, and everything your apps and agents accumulate becomes part of your edge. In the #AI era, that data needs to move with you. October 7th: A new way to connect your data across apps and AI tools is coming.

    1322212411K viewsView on X
  • Sep 6, 20261.6x their median

    Next stop: Sui Basecamp in Singapore. 🇸🇬 We'll be talking about state persistence, portable memory, and where agent stacks are going. Make your card and drop it below so we can see who's on the ground. https://t.co/pJc55xFMRL

    1351110113K viewsView on X
  • Aug 24, 20261.5x their median

    Moving your AI memory has never been more necessary. @kostascrypto @gazza_jenks on @theblockco. https://t.co/ssUV7ylVw5

    122188215K viewsView on X
  • Sep 23, 2026

    @kostascrypto and the rest of the Walrus team is on the ground at @HumanXCo in Amsterdam. 🇳🇱 We’re bringing Walrus Memory to the center of the AI stack, solving context loss for multi-agent systems. #HumanX2026 #AI #WalrusMemory https://t.co/y2jjOzLcrs

    1151414212K 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.

Recurring topics

#ai#artificialintelligence#humanx#humanx2026#suibasecamp

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 97 interactions against 347K followers, an engagement rate of 0.026%. Measured over 27 original posts, its engagement rate beats 48% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 9.8K times each, and 0.993% of those impressions turn into an interaction. That is about 2.81% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.2 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 27 posts sampled, 78% carry an image or video and 41% link out. The account's strongest tracked post pulled 11K interactions, about 109x its own typical post. Recurring topics include #ai, #artificialintelligence, #humanx.

What is Walrus's engagement rate on X?
Walrus (@WalrusProtocol) has an engagement rate of 0.026%, based on the median interactions across 27 original posts from the last 30 days against 347,302 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.026%, Walrus sits above the 25th percentile of the 205,475 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 @WalrusProtocol have real engagement?
Its engagement rate beats 48% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the middle of its size range 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 @WalrusProtocol post?
Most posts go out around 17:00 UTC, and Wednesday is its busiest day, at roughly 4.17 posts per day across the measured window.

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