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
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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.9% |
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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 0% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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.