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chad fowler engagement report

@chadfowler - 305K followers on X

Measured over 11 original posts from a 30-day window, last computed on October 17, 2026.

Engagement

Bottom quarter for its size
Per follower
0.007%
of 305K followers
Per impression
0.588%
3.7K views on a typical post
Reach
1.23%
of its followers see a post
Typical post
22
interactions (median)
Saved
0.134%
5 bookmarks on a typical post
Posting rate
1.67/day
active 43% of days
Peak time
12:00 UTC
Wednesday

A typical post picks up 22 interactions against 305K followers, an engagement rate of 0.007%. Measured over 11 original posts, its engagement rate beats 20% of 21,185 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 3.7K times each, and 0.588% of those impressions turn into an interaction. That is about 1.23% 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, though only 43% of days saw any activity at all. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 11 posts sampled, 18% carry an image or video and 45% link out. The account's strongest tracked post pulled 1.2K interactions, about 57x its own typical post.

Measured over 11 original posts from a 30-day window, last computed on October 17, 2026. Recurring tag: #acmtechtalk.

Compared with accounts its own size

chad fowler's engagement rate beats 20% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 9 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 29% 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 0.007%, chad fowler sits above the 10th percentile of the 212,482 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.

p100.004%
p250.025%
p50 (median)0.146%
p750.696%
p902.61%
p9982.9%
Engagement rate as a share of followers, across the 212,482 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,403 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 percentile0.025%
50th percentile0.146%
75th percentile0.696%
90th percentile2.61%
99th percentile82.9%

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 12:00 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: 12:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 12:00 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 video18% of posts+111%+108% to +115%34K
Outbound link45% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 18% 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.
  • 45% 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 277 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

  • Aug 28, 202657x their median

    macbooks having 4g connection and not fall asleep when closed would contribute billions to gdp from agents running more

    1.1K15831093K viewsView on X
  • Sep 15, 202642x their median

    Would be funny if Anthropic's internal Slack leaks and it turns out everyone there talks like Claude. "Lunch is in the kitchen" "The kitchen is no longer where lunch gets made. It’s where intent becomes consumption. Food was the easy part. Presence is the hard problem."

    8532738130K viewsView on X
  • Sep 9, 202620x their median

    Everybody's trying to figure out how to scale PRs/code review, but the bigger bottleneck is going to be deployment. If models keep getting cheaper, eventually you have 100s of agents working autonomously. What is the plan for getting all that code merged and deployed? You can't put them all in a single merge train. We need to start letting agents validate changes against tiny slices of production traffic before the PR is merged.

    3581456939K viewsView on X
  • Jul 31, 202612x their median

    I'm excited to share the early access release of my upcoming book, Regenerative Software, which will be released in full early next year by O'Reilly, one of my all time favorite tech publishers! As a self-taught developer, I learned a lot of what was foundational to my career from early O'Reilly books on UNIX, Perl, sed, awk, etc. Thrilled to finally have one of these animal covers with my name on it. (link in thread)

    2212513114K viewsView on X
  • Oct 1, 20269.3x their median

    Biggest source of confusion is that everyone - spent wildly different # of tokens in last 2 years - works in diff codebases - with diff languages - and diff requirements - in diff teams - with diff processes - in diff companies and then says "models are [good|bad]"

    167824516K viewsView on X
  • Mar 18, 20263.3x their median

    https://t.co/7LaD9PM2P9

    93218824K viewsView on X
  • Aug 27, 20263.6x their median

    I have long argued that software components should be tiny: small enough to understand, change, or throw away. This paper suggests something I hadn’t considered then: The developers can be tiny too. 1,000+ finite-lived agents build a 250K-line compiler. The project persists. The programmers don’t. https://t.co/2pJd6C2hLX

    713517.5K viewsView on X
  • Aug 25, 20263.6x their median

    If you can reliably tell whether generated software is correct, secure, fast, and architecturally sound, you can care a lot less about how it was generated. The stronger the verification, the more the generator becomes a commodity. Interesting new paper in this direction: https://t.co/t4VATAsWRM

    694705.8K viewsView on X
  • Sep 4, 20263.6x their median

    I called AWS WAF a tax on running a public API a few weeks back. One question kept coming up after: okay, so what do you do instead? 😅 What actually came out of that discussion:

    7206125K viewsView on X
  • Sep 30, 20263.2x their median

    Creating DSLs to provide a golden path for agents is an under-rated tactic. Building DSLs is much easier now, and you get to decide exactly what the agent can do instead of trying to deny it. Also, the DSL can be linted for common mistakes.

    593719.4K 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

#acmtechtalk

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 22 interactions against 305K followers, an engagement rate of 0.007%. Measured over 11 original posts, its engagement rate beats 20% of 21,185 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 3.7K times each, and 0.588% of those impressions turn into an interaction. That is about 1.23% 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, though only 43% of days saw any activity at all. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 11 posts sampled, 18% carry an image or video and 45% link out. The account's strongest tracked post pulled 1.2K interactions, about 57x its own typical post.

What is chad fowler's engagement rate on X?
chad fowler (@chadfowler) has an engagement rate of 0.007%, based on the median interactions across 11 original posts from the last 30 days against 305,053 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.007%, chad fowler sits above the 10th percentile of the 212,482 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 @chadfowler have real engagement?
Its engagement rate beats 20% of the tracked X accounts closest to it in follower count (21,185 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 @chadfowler post?
Most posts go out around 12:00 UTC, and Wednesday is its busiest day, at roughly 1.67 posts per day across the measured window.

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