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Cory House engagement report

@housecor - 169K followers on X

Measured over 40 original posts from a 30-day window, last computed on October 23, 2026.

Engagement

Middle of its size range
Per follower
0.139%
of 169K followers
Per impression
1.19%
20K views on a typical post
Reach
11.6%
of its followers see a post
Typical post
234
interactions (median)
Saved
0.212%
42 bookmarks on a typical post
Posting rate
2.2/day
active 97% of days
Peak time
12:00 UTC
Wednesday

A typical post picks up 234 interactions against 169K followers, an engagement rate of 0.139%. Measured over 40 original posts, its engagement rate beats 65% of 21,185 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 20K times each, and 1.19% of those impressions turn into an interaction. That is about 11.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 40 posts sampled, 13% carry an image or video and 15% are part of a thread. The account's strongest tracked post pulled 3.9K interactions, about 17x its own typical post.

Measured over 40 original posts from a 30-day window, last computed on October 23, 2026.

Compared with accounts its own size

Cory House's engagement rate beats 65% 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 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 0.139%, Cory House sits above the 25th percentile of the 212,825 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.

p100.004%
p250.025%
p50 (median)0.146%
p750.697%
p902.61%
p9983.0%
Engagement rate as a share of followers, across the 212,825 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,436 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 percentile0.697%
90th percentile2.61%
99th percentile83.0%

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 video13% of posts+111%+108% to +115%34K
Outbound link0% 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.
  • 0% 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 321 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

  • Sep 4, 202617x their median

    A few thoughts on switching lines. https://t.co/3JIXR2G4eJ

    3.1K529931412.1M viewsView on X
  • Sep 14, 202114x their median

    10 lessons I've learned about handling React state over the last 7 years... (thread) #react #reactjs

    2.4K5977752View on X
  • Oct 18, 202613x their median

    Before you launch anything, have Opus 5.5 send 5 subagents in as your first users. Paste this into Claude Code: "Act as my launch QA lead. Spin up 5 subagents, each a different first-time user: 1. The impatient one: skims, clicks the first button, quits at the first error 2. The skeptic: reads pricing, terms and privacy before signing up 3. The mobile user: does everything on a 375px screen on slow 3G 4. The non-native English speaker: only follows what the UI literally says 5. The breaker: pastes junk into forms, hits back mid-flow, double-clicks everything Each one opens the app in a real browser, goes from landing page to first success, and screenshots every moment they get confused, stuck or annoyed. Merge everything into one list ranked by how many users hit it. Fix it all in one pass, then rerun the 5 users to prove it's fixed." Your first 100 real users won't tell you why they left. these 5 will. (Works best with a browser MCP connected, Playwright or Chrome DevTools)

    2.8K1316221264K viewsView on X
  • Oct 8, 202610x their median

    I am taking the plunge. https://t.co/TB2FIvTIfR

    2.3K3211724137K viewsView on X
  • Aug 26, 202610x their median

    I'm seeing team leads under immense pressure. Why? Because AI decoupled effort from output. Developers can create giant PRs in a few minutes. But that code may take hours to vet, test, fix, and review. In 2024, bad developers were easy to spot. They produced little code, or struggled to write code that even ran. In 2026, bad developers can use AI to quickly generate tons of code they don't understand. It seems okay at a glance, but contains subtle problems and fundamental issues that are hard to spot. "The energy needed to refute nonsense is an order of magnitude bigger than the energy needed to produce it." -Brandolini's law (aka the bullshit asymmetry principle)

    2.1K15811738143K viewsView on X
  • Oct 3, 20267.9x their median

    Probably the best interview of 2026. @poteto and Matt are both awesome 😎 👏 https://t.co/kYYoy5PGbR

    1.6K1412327276K viewsView on X
  • Oct 7, 20267.5x their median

    Just joined a team with nearly 500 .md files for AI. Nearly half a million words. Just reading all the AI related instructions, skills, hooks, etc would take me about 30 hours! I keep seeing teams with elaborate setups like this. I'm increasingly skeptical it's worthwhile.

    1.4K2829929141K viewsView on X
  • Aug 13, 20266.8x their median

    Are we ever going to get back to talking about languages, frameworks, libraries, and packages? AI sucked all the oxygen out of the room

    1.3K5121125131K viewsView on X
  • Oct 20, 20266.2x their median

    I spent 25 years getting good at web dev. I memorized syntax, browser APIs, accessibility techniques, and performance optimizations, security patterns, and learned dozens of libraries and frameworks. Now I'm trying to decide what knowledge still has value.

    1.2K441721098K viewsView on X
  • Oct 11, 20265.3x their median

    English is the new programming language. So what’s still worth learning? Vocabulary. Architectures Design patterns Refactoring techniques Security protocols Performance optimizations UI/UX patterns The greater my technical vocabulary, the more precise my prompt.

    1.0K93821634K 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 234 interactions against 169K followers, an engagement rate of 0.139%. Measured over 40 original posts, its engagement rate beats 65% of 21,185 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 20K times each, and 1.19% of those impressions turn into an interaction. That is about 11.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 12:00 UTC, and Wednesday is the busiest day of the week. Of the 40 posts sampled, 13% carry an image or video and 15% are part of a thread. The account's strongest tracked post pulled 3.9K interactions, about 17x its own typical post.

What is Cory House's engagement rate on X?
Cory House (@housecor) has an engagement rate of 0.139%, based on the median interactions across 40 original posts from the last 30 days against 168,981 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.139%, Cory House sits above the 25th percentile of the 212,825 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 @housecor have real engagement?
Its engagement rate beats 65% of the tracked X accounts closest to it in follower count (21,185 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 @housecor post?
Most posts go out around 12:00 UTC, and Wednesday is its busiest day, at roughly 2.2 posts per day across the measured window.

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