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Andrew Wilkinson engagement report

@awilkinson - 392K followers on X

Measured over 16 original posts from a 30-day window, last computed on September 30, 2026.

Engagement

Middle of its size range
Per follower
0.09%
of 392K followers
Per impression
0.426%
83K views on a typical post
Reach
21.2%
of its followers see a post
Typical post
352
interactions (median)
Saved
0.078%
64 bookmarks on a typical post
Posting rate
1.73/day
active 57% of days
Peak time
18:00 UTC
Monday

A typical post picks up 352 interactions against 392K followers, an engagement rate of 0.09%. Measured over 16 original posts, its engagement rate beats 69% 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 83K times each, and 0.426% of those impressions turn into an interaction. That is about 21.1% 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 57% of days. Most posts go out around 18:00 UTC, and Monday is the busiest day of the week. Of the 16 posts sampled, 13% carry an image or video and 6% link out. The account's strongest tracked post pulled 4.5K interactions, about 13x its own typical post.

Measured over 16 original posts from a 30-day window, last computed on September 30, 2026.

Compared with accounts its own size

Andrew Wilkinson's engagement rate beats 69% 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 28% 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 0.09%, Andrew Wilkinson sits above the 25th percentile of the 205,582 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%
p750.679%
p902.56%
p9982.4%
Engagement rate as a share of followers, across the 205,582 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 percentile0.679%
90th percentile2.56%
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 18:00 UTC, and Monday 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: 18:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 18:00 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: Monday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Monday
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 video13% of posts+111%+108% to +115%34K
Outbound link6% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 13% 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.
  • 6% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
  • Its average post runs 332 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

  • Jul 21, 202613x their median

    Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface https://t.co/xxGeAgo35R

    3.5K4522202901.4M viewsView on X
  • May 9, 20257.6x their median

    This guy from The Netherlands emailed me asking if I'd come on his podcast. I didn't want to, so I used my usual line: "I'll only do it in person in Victoria." Welp, the SOB called my bluff. Flew 12 hours, mic in hand. Well played, @WouterTeunissen. It turned out great: https://t.co/R4zPOzLblp

    2.3K14318928692K viewsView on X
  • Jul 18, 20265.9x their median

    https://t.co/y17nfoH19a

    1.8K13690493.8M viewsView on X
  • Aug 3, 20263.8x their median

    Sam (@sama), I love Codex. But there's this one really dumb/subtle problem. Claude Code FEELS faster. I think this is primarily because as it does each thing, it immediately slams a text update into the terminal/app, whereas Codex keeps everything contained/compressed into a line that has that little side to side fade effect. It's almost like chain of thought - you were doing it in ChatGPT just not showing it, and when you added it people felt like it was smarter/faster. I think it would be worth adding a more verbose mode / experimenting with revealing more of what Codex is doing so that you viscerally feel it ripping.

    1.1K211809196K viewsView on X
  • Jul 31, 20263.4x their median

    “Never forget the six-foot-tall man who drowned crossing the stream that was five feet deep on average.” ― Howard Marks

    1.1K6422589K viewsView on X
  • Sep 5, 20263.4x their median

    I checked my Codex billing dashboard after 6 hours of obsessive Astra Ultra use and immediately threw up a little bit of blood.

    1.1K11795262K viewsView on X
  • Sep 28, 20262.8x their median

    Think about it: GrokBot, Instinct, and Muse JUST caught up with the functionality of OpenClaw. Just imagine what @steipete and @sama have in store tomorrow. They're 6 months ahead of everyone.

    895197316188K viewsView on X
  • Sep 10, 20262.3x their median

    I lied to my kids. https://t.co/00Qp9QvOsT

    722166914255K viewsView on X
  • Aug 31, 20262.1x their median

    Cold emails should look COLD. If it looks: 1. Like a template 2. Like it will take more than 15 seconds to read You are screwed. My best one of all time, that I would send to founders of startups when I was running @Metalab, my design agency: "love what you guys are doing, we should work together." That's it. 60% response rate.

    6442076584K viewsView on X
  • Sep 13, 20262.1x their median

    For the past few days I've been using Instinct, the text message assistant that just raised at a $2.5 BILLION valuation. It's an exceptional product, and the most proactive personal assistant agent I've used. But it's doomed and their investors are going to lose all their money. It's a perfect example of feature not a product. The moment that OpenAI or Anthropic launches an integrated iMessage agent, it's game over. Why would I want a personal assistant that doesn't have access to the memory/knowledge graph I've standardized on? But who knows - maybe I'm wrong. Would love to hear the counter arguments. So curious how their investors got there.

    558713921124K 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 352 interactions against 392K followers, an engagement rate of 0.09%. Measured over 16 original posts, its engagement rate beats 69% 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 83K times each, and 0.426% of those impressions turn into an interaction. That is about 21.1% 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 57% of days. Most posts go out around 18:00 UTC, and Monday is the busiest day of the week. Of the 16 posts sampled, 13% carry an image or video and 6% link out. The account's strongest tracked post pulled 4.5K interactions, about 13x its own typical post.

What is Andrew Wilkinson's engagement rate on X?
Andrew Wilkinson (@awilkinson) has an engagement rate of 0.09%, based on the median interactions across 16 original posts from the last 30 days against 392,234 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.09%, Andrew Wilkinson sits above the 25th percentile of the 205,582 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 @awilkinson have real engagement?
Its engagement rate beats 69% 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 @awilkinson post?
Most posts go out around 18:00 UTC, and Monday is its busiest day, at roughly 1.73 posts per day across the measured window.

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