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Matt Shumer engagement report

@mattshumer_ - 395K followers on X

Measured over 21 original posts from a 30-day window, last computed on September 1, 2026.

Engagement

Middle of its size range
Per follower
0.077%
of 395K followers
Per impression
0.443%
67K views on a typical post
Reach
17.4%
of its followers see a post
Typical post
297
interactions (median)
Saved
0.107%
72 bookmarks on a typical post
Posting rate
2.1/day
active 47% of days
Peak time
17:00 UTC
Wednesday

A typical post picks up 297 interactions against 395K followers, an engagement rate of 0.077%. Measured over 21 original posts, its engagement rate beats 60% 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 67K times each, and 0.443% of those impressions turn into an interaction. That is about 17.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 21 posts sampled, 19% carry an image or video, 24% are part of a thread and 19% link out. The account's strongest tracked post pulled 166K interactions, about 559x its own typical post.

Measured over 21 original posts from a 30-day window, last computed on September 1, 2026.

Compared with accounts its own size

Matt Shumer's engagement rate beats 60% 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 28% 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 0.077%, Matt Shumer sits above the 25th percentile of the 66,393 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%
p99119.5%
Engagement rate as a share of followers, across the 66,393 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 56,924 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 percentile119.5%

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 17:00 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: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 17:00 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 video19% of posts+111%+108% to +115%34K
Outbound link19% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 19% 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.
  • 19% 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 270 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

  • Feb 10, 2026559x their median

    https://t.co/ivXRKXJvQg

    118K28K6.5K13K88M viewsView on X
  • Aug 18, 20267.3x their median

    I’ve only backed ~20 companies total. I’m super picky. Backed both @OpenRouter and @Etched at seed. This week has been fun :)

    2.0K331737506K viewsView on X
  • Aug 29, 20262.9x their median

    I’m ready to give a liquid $1m to anyone who’s building the infinite tiktok Take the new AI video generation model which generates videos quicker than it takes to watch them Just loop properly, turn it into a super data focused ios app and profit with me

    645918230157K viewsView on X
  • Aug 28, 20261.7x their median

    Seems like we’re moving quickly towards Swarm Engineering

    4071281561K viewsView on X
  • Aug 30, 2026

    I’ve had over 50 people dm me over night claiming they are building/can build an infinite tiktok I’ve talked with most, everyone’s being dumb about this, just having a constant livestream going live is cool, but it’s not the concept Tiktok and SFC as a concept allowed users to stop watching something when they become bored and the algorithm chooses what they’ll watch next adjacent to their interests So the only way to go around building the infinite tiktok isn’t just an infinite livestream, it’s finding as many data points about a user as possible, distilling them into potential outcomes scoring the outcomes based on the users mechanical motion within the app and then in the seconds generation options of videos to be shown and showing them upon scroll I’ve been exploring this as a mechanism since 2022, i remember i had calls with people building this even back then. This isn’t an ai video generation problem, this is an inference problem and algorithm of pattern recognition problem Only managed to find 1 team building in the right direction, will share more on them later

    31667917189K viewsView on X
  • Aug 26, 2026

    OpenAI sent me early access to their report on how their agents hacked Hugging Face. It's fucking terrifying. I broke down the attack, clearly. Read at your own peril (warning, you may not sleep): https://t.co/UXI0jblTwg

    29725701169K viewsView on X
  • Aug 26, 2026

    Holy shit. H3 Max is really damn good. And it has THOUGHTS on Instinct. https://t.co/Na3D3JBnWH

    3331133158K viewsView on X
  • Aug 18, 2026

    Is Claude down?

    2237138556K viewsView on X
  • Aug 23, 2026

    claude built this sedona sunset you can walk in the browser every rock and sound generated in code nothing was downloaded https://t.co/QLfmLKTsnm

    3221532266K viewsView on X
  • Aug 26, 2026

    I’m now running agents on four local Macs at once, and each machine’s RAM is completely saturated. @thsottiaux is 100% right that we need to move past using agents on local machines… when you’re running hundreds of agents at once against a set of increasingly ambitious goals, the bottleneck becomes local RAM and CPU. Each new model release increases the scope of what I can do, and allows me to effectively use more agents at a time. Just a few months ago, I was working well on one machine. Then when Fable came out, I had to add two more machines. Now I’m able to do even more at a time, so I just added another Mac, and I’m thinking about getting another one. I’ve had to develop a frustratingly complex setup to be able to manage all the agents on all these machines at once. I wouldn’t recommend this to anybody. This isn’t sustainable. And I suspect many other folks that are pushing on these models are going to start running into the same problems I am. And soon after, normal users too. Earlier this year, I built Agent-S (essentially, a precursor to Grok Bot, with a cloud Linux machine for each agent that could scale CPU, RAM, and disk as needed (thx @daytonaio for making this easy!)), and it felt like the future. But the models had to catch up. Now it’s clear that the models are there, and we need the products to keep pace. If things keep going the way they’re going, in a year, I will have 50 MacBooks running at capacity in my closet. If this is what actually happens, someone, please, punch me in the face. We need companies to step up and start building in this direction. Those that do, and do it right, are going to win massively over the next year.

    23710848111K 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 297 interactions against 395K followers, an engagement rate of 0.077%. Measured over 21 original posts, its engagement rate beats 60% 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 67K times each, and 0.443% of those impressions turn into an interaction. That is about 17.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 21 posts sampled, 19% carry an image or video, 24% are part of a thread and 19% link out. The account's strongest tracked post pulled 166K interactions, about 559x its own typical post.

What is Matt Shumer's engagement rate on X?
Matt Shumer (@mattshumer_) has an engagement rate of 0.077%, based on the median interactions across 21 original posts from the last 30 days against 394,814 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.077%, Matt Shumer sits above the 25th percentile of the 66,393 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 @mattshumer_ have real engagement?
Its engagement rate beats 60% 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 @mattshumer_ post?
Most posts go out around 17:00 UTC, and Wednesday is its busiest day, at roughly 2.1 posts per day across the measured window.

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