Back to @getjonwithit's profile

Jonathan Gorard engagement report

@getjonwithit - 48K followers on X

Measured over 5 original posts from a 30-day window, last computed on October 23, 2026.

Engagement

Per follower
0.228%
of 48K followers
Per impression
0.58%
19K views on a typical post
Reach
39.4%
of its followers see a post
Typical post
109
interactions (median)
Saved
0.25%
47 bookmarks on a typical post
Posting rate
0.93/day
active 57% of days
Peak time
20:00 UTC
Thursday

Early reading. We have captured 5 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 109 interactions against 48K followers, an engagement rate of 0.228%. Posts are seen about 19K times each, and 0.58% of those impressions turn into an interaction. That is about 39.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.93 post a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 20:00 UTC, and Thursday is the busiest day of the week. Of the 5 posts sampled, 40% carry an image or video and 20% link out. The account's strongest tracked post pulled 1.3K interactions, about 12x its own typical post. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 5 original posts from a 30-day window, last computed on October 23, 2026.

Where this sits in the catalog

At 0.228%, Jonathan Gorard sits above the 50th percentile of the 212,349 accounts in this comparison. That places it in the above the median band, which runs 0.146% to 0.696%.

p100.004%
p250.025%
p50 (median)0.146%
p750.696%
p902.61%
p9983.1%
Engagement rate as a share of followers, across the 212,349 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,452 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 percentile83.1%

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 20:00 UTC, and Thursday 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: 20:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 20:00 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: Thursday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Thursday
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 video40% of posts+111%+108% to +115%34K
Outbound link20% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 40% 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.
  • 20% 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 434 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 8, 202612x their median

    Physics will be next, and this time we have a chance to learn from the mistakes of math and make the sociology a little less toxic. Let’s not screw it up.

    1.2K80431785K viewsView on X
  • Sep 24, 20269.8x their median

    It is an unfortunate reality that people who talk incessantly about themselves and their achievements really do get taken more seriously than those who consider such things to be gauche. I need to stop being surprised by this.

    9853740332K viewsView on X
  • Sep 30, 20262.7x their median

    We’re organizing Vibecheck, a formal methods hackathon with some of the best folks in the space - @maxvonhippel, @qd_forall, @nolanlwin, @jessemhan, @emiyazono, and @workersio. You’ll get a weekend to build real production software, and formally verify it. We’ll have people who really know their stuff around to help, and you can come with a team, find one there, or just hack on something yourself. We are glad to have a phenomenal group of sponsors, including @harmonicmath, @mathematics_inc, @theoremlabs, @thegp, @omnicom, @primeintellect, @astriolabs, @buildwithparty, @wearerandomlabs, and @lanyon_ai, as well as some more we will announce in the coming days! If you’re a hacker, a formal methods person, or just someone who thinks “how do we know it works?” is an interesting question, we’d love to have you. Signup Now: https://t.co/sAKQaDo0bV

    20741212177K viewsView on X
  • Sep 10, 2026

    I wrote a few words about where I think humanity is right now, what has led us to this point, and what I think an optimistic vision for the future (at least for science, mathematics, and technology) might look like. Link below. 👇 https://t.co/urh0ogbqGz

    1138728.4K viewsView on X
  • Oct 2, 2026

    good to see mts friends in nyc this was pretty last minute and went from 30 rsvps to 300 fast we have some secret projects and can’t wait to be in nyc more regularly! https://t.co/zoy5Rb7J4Z

    9818215K viewsView on X
  • Oct 16, 2026

    Some of y’all have not studied the implications of computational irreducibility, and it shows… Local determinism wrt nontrivially interacting rule sets does not help predict the “long term” behavior of a computationally irreducible system, and when it comes to AI the iterative frequency is such that “long term” is not actually that long from the human reference frame. This is as true of our current neural nets as it is of the initial cellular automata in which computational irreducibility was first discovered and studied. It’s also what Anthropic was (re)discovering wrt their escape cone diagrams wrt agentic unpredictability over long-horizon work—especially work with somewhat ambiguous constraints and success metrics. Except that they got the cone geometry wrong, as the region of an agent’s potential state space trajectory looks more like an exponential horn.

    8145119K viewsView on X
  • Oct 8, 2026

    per axiomata ad astra

    665815.4K viewsView on X
  • Sep 11, 2026

    This inference could have been a tool call.

    653315.1K viewsView on X
  • Jul 31, 2026

    @lanyon_ai /simulate Simulate the flow of a Mach 2 shock over a rotating triangular object. The triangle is equilateral and one of the corners is initially pointed towards the flow. Let the triangle turn twice as the shock passes over it. A few minutes later, Lanyon produces this amazing animation of the complex interaction of shocks and the wake created by a rotating, triangular prism. Notice the vortex shedding and the complex Mach stem, shock diffraction and other patterns. Hypersonic reentry, rocket combustion, and many other fun and critically important simulations are not far away!

    3122122K viewsView on X
  • Sep 2, 2026

    Lanyon AI is one of the organizations supporting the permanent endowment of the William D. Dorland Prize in Computational Plasma Physics by the American Physical Society. Bill was a dear friend and colleague of the Lanyon AI founding team, acting as PhD supervisor to Chief Scientist Jimmy Juno (@JunoRavin) at the University of Maryland, and later as the Head of the Computational Sciences Department at the Princeton Plasma Physics Laboratory, where CTO Ammar Hakim (@minmodammar) was Deputy Head and Jimmy was a Research Physicist. The prize is intended to support and recognize outstanding contributions to astrophysics, nuclear fusion, and numerical and computational methods in plasma physics. In recognition of Bill’s profound impact on the lives of the founding team, Jimmy Juno has written a short reflection on our company blog. Link below. 👇

    212147.4K 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.

Buy or sell Twitter (X) accounts - escrow-protected

PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.

Reading these numbers

A typical post picks up 109 interactions against 48K followers, an engagement rate of 0.228%. Posts are seen about 19K times each, and 0.58% of those impressions turn into an interaction. That is about 39.3% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.93 post a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 20:00 UTC, and Thursday is the busiest day of the week. Of the 5 posts sampled, 40% carry an image or video and 20% link out. The account's strongest tracked post pulled 1.3K interactions, about 12x its own typical post. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is Jonathan Gorard's engagement rate on X?
Jonathan Gorard (@getjonwithit) has an engagement rate of 0.228%, based on the median interactions across 5 original posts from the last 30 days against 47,797 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.228%, Jonathan Gorard sits above the 50th percentile of the 212,349 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 @getjonwithit have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @getjonwithit post?
Most posts go out around 20:00 UTC, and Thursday is its busiest day, at roughly 0.93 posts per day across the measured window.

Keep going