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Martin Fowler engagement report

@martinfowler - 356K followers on X

Measured over 9 original posts from a 30-day window, last computed on September 2, 2026.

Engagement

Middle of its size range
Per follower
0.019%
of 356K followers
Per impression
0.519%
13K views on a typical post
Reach
3.68%
of its followers see a post
Typical post
68
interactions (median)
Saved
0.351%
46 bookmarks on a typical post
Posting rate
0.3/day
active 27% of days
Peak time
13:00 UTC
Tuesday

A typical post picks up 68 interactions against 356K followers, an engagement rate of 0.019%. Measured over 9 original posts, its engagement rate beats 36% of 6,874 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 13K times each, and 0.519% of those impressions turn into an interaction. That is about 3.67% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 9 posts sampled, 100% link out. The account's strongest tracked post pulled 1.0K interactions, about 15x its own typical post.

Measured over 9 original posts from a 30-day window, last computed on September 2, 2026.

Compared with accounts its own size

Martin Fowler's engagement rate beats 36% of the tracked X accounts closest to it in follower count (6,874 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 31% 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 0.019%, Martin Fowler sits above the 25th percentile of the 66,128 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.

p100.002%
p250.016%
p50 (median)0.1%
p750.499%
p902.09%
p99119.9%
Engagement rate as a share of followers, across the 66,128 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 57,111 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 percentile0.002%
25th percentile0.016%
50th percentile0.1%
75th percentile0.499%
90th percentile2.09%
99th percentile119.9%

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 13:00 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: 13:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 13:00 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 video0% of posts+111%+108% to +115%34K
Outbound link100% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 0% 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.
  • 100% 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 205 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.

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

  • Apr 28, 202615x their median

    NEW POST Thoughtworks internal IT use a workflow for agentic programming called Structured-Prompt-Driven Development (SPDD). @WeiZhang595190 and Jessie Jie Xia describe how this works with a simple example plus details in a github project. https://t.co/6cHnSPWr6L

    861121251096K viewsView on X
  • Jul 14, 202614x their median

    NEW POST LLMs generate code incredibly fast, but to ensure they generate exactly what is intended, they need clear boundaries. @unmeshjoshi shares his experience using abstractions and Domain-Specific Languages (DSLs) to provide a strong harness. https://t.co/AIn45L4m5I

    799111211588K viewsView on X
  • Aug 11, 202612x their median

    Does telling a coding agent to do TDD by itself make a difference? Or is it one of the rare examples where what's good for the human is irrelevant or bad for an agent? Birgitta Böckeler runs experiments and shares her thoughts. https://t.co/q2vgv8IdOR

    69186441790K viewsView on X
  • Jul 28, 20265.4x their median

    NEW POST Subagents get justified by time saved and parallel execution, but @techygarg explains that what really matters is protecting the orchestrator's context. https://t.co/6f5pFKjayw

    3114312433K viewsView on X
  • May 14, 20265.3x their median

    NEW POST When I need to feed an LLM a lot of context, I can write it myself, or I can get an LLM to interview me for it. https://t.co/n0IavQLGGZ

    326238384K viewsView on X
  • May 12, 20264.8x their median

    NEW POST Will there be source code in the future? To wrestle with this, we have to understand what code is. Unmesh Joshi sees code as having two distinct but intertwined purposes: instructions to a machine and a conceptual model of the problem domain. https://t.co/GsjgtYysno

    266479729K viewsView on X
  • Jun 16, 20264.5x their median

    NEW POST We've used AI with Bayer to help pharmaceutical researchers query decades of information buried in PDF reports. Sarang Sanjay Kulkarni describes the evolution from keyword-based search to a research assistant that can draft regulatory reports https://t.co/Oi74GFzrYL

    271266225K viewsView on X
  • Apr 29, 20264.0x their median

    Fragments: updated guide on AI coding, video on harness engineering, how long should a function be, the problems of Software Brain and why AI is unpopular https://t.co/7jSOtMhEE8

    232331329K viewsView on X
  • Jul 7, 20262.9x their median

    NEW POST Birgitta Böckeler recently spent some time trying out running local LLMs for some programming tasks. In this memo she outlines the factors that influence how viable they are for the job. https://t.co/ALp5q6p3g5

    160305227K viewsView on X
  • Apr 8, 20262.8x their median

    NEW POST @techygarg finishes his series on reducing the friction in AI-Assisted Development with a practice that feeds back learnings from AI sessions into the team's shared artifacts, turning individual experience into collective improvement. https://t.co/sQ9bkAGlbQ

    168183319K 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 68 interactions against 356K followers, an engagement rate of 0.019%. Measured over 9 original posts, its engagement rate beats 36% of 6,874 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 13K times each, and 0.519% of those impressions turn into an interaction. That is about 3.67% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 9 posts sampled, 100% link out. The account's strongest tracked post pulled 1.0K interactions, about 15x its own typical post.

What is Martin Fowler's engagement rate on X?
Martin Fowler (@martinfowler) has an engagement rate of 0.019%, based on the median interactions across 9 original posts from the last 30 days against 356,383 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.019%, Martin Fowler sits above the 25th percentile of the 66,128 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 @martinfowler have real engagement?
Its engagement rate beats 36% of the tracked X accounts closest to it in follower count (6,874 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 @martinfowler post?
Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 0.3 posts per day across the measured window.

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