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kache engagement report

@yacineMTB - 413K followers on X

Measured over 73 original posts from a 30-day window, last computed on September 29, 2026.

Engagement

Middle of its size range
Per follower
0.079%
of 413K followers
Per impression
1.54%
21K views on a typical post
Reach
5.14%
of its followers see a post
Typical post
322
interactions (median)
Saved
0.124%
26 bookmarks on a typical post
Posting rate
6.43/day
active 37% of days
Peak time
22:00 UTC
Monday

A typical post picks up 322 interactions against 413K followers, an engagement rate of 0.079%. Measured over 73 original posts, its engagement rate beats 67% of 20,444 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 21K times each, and 1.54% of those impressions turn into an interaction. That is about 5.07% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 6.4 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 22:00 UTC, and Monday is the busiest day of the week. Of the 73 posts sampled, 38% carry an image or video and 19% are part of a thread. The account's strongest tracked post pulled 5.7K interactions, about 18x its own typical post.

Measured over 73 original posts from a 30-day window, last computed on September 29, 2026.

Compared with accounts its own size

kache's engagement rate beats 67% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 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 59% 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 0.079%, kache sits above the 25th percentile of the 205,152 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 205,152 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,894 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 percentile0.024%
50th percentile0.143%
75th percentile0.678%
90th percentile2.55%
99th percentile82.4%

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 22:00 UTC, and Monday 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: 22:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 22:00 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: Monday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Monday
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 video38% of posts+111%+108% to +115%34K
Outbound link4% of posts-41%-42% to -40%32K
Typical length--3%-3% to -2%54K
  • 38% 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.
  • 4% 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 149 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below 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 28, 202618x their median

    this book has made me millions of dollars https://t.co/rUGeWt5D55

    5.3K17613721186K viewsView on X
  • Sep 8, 202617x their median

    The good ending to this is that they put OpenAI into a pissing contest. You know what would prove that they solved it themselves? Solving a few more millennium problems How hard could it possibly be, anyways

    5.2K885314320K viewsView on X
  • Sep 10, 202613x their median

    I love that deepseeks solution to their increased demand was to make their model cheaper and faster

    4.1K135561363K viewsView on X
  • Sep 27, 202613x their median

    I'm like 98% certain I am further ahead than everyone doing this. And I am doing this as a hobby on my free time as a dad with two under two https://t.co/YCzlqgAdCi

    3.9K7632823368K viewsView on X
  • Sep 27, 202611x their median

    Dear x engineering team, I understand that Elon Musk is breathing down your neck at this very moment. I understand that you're extremely stressed out. The truth is that you should be. Your app is garbage. Spaces never f****** works. Computers literally code themselves. The data center that your boss owns has a AI inside of it. Literally lives in your boss's data center. It's free. It's actually free. Here's what you should do. I'm going to only say this once. Ask the AI to fix the bugs. Set up a for loop which runs every day that asks the AI to fix the bugs. Give the AI a literal f****** phone. Ask her to use the website. And tell it if it finds any bugs to fix them

    3.2K6813522266K viewsView on X
  • Sep 7, 20267.3x their median

    I used GPT-6 Astra Ultra to track a tennis ball. The results are impressive until I tell you the following. Ball annotation, using 3 parallel agents took 8m 35s and 17,611 tokens. Complete workflow: 11m 49s and 30,481 tokens. The complete workflow also processed 313,914 uncached input tokens and 7,528,320 cached input tokens. That's a total of 7.87 million total, mostly context reused across frame-inspection calls.

    1.9K153161881.1M viewsView on X
  • Sep 8, 20266.7x their median

    Millennium problem only awards 1 million dollars??? That's like the biweekly stock bonus at OpenAI

    2.1K1965856K viewsView on X
  • Sep 28, 20265.9x their median

    Claim: we've solved the AI slop problem (!) 💩🧹✨ Blog post: https://t.co/mXuwYiy6rq 🧵1/5 Key idea: take *expert* human writing and learn rubrics that find the gap between experts and models. Train with those rubrics. We train with RL-XAR (RL with eXpert Aligned Rubrics) & see large performance gains on writing scientific paper sections, Pulitzer prize novel continuations and high quality Wikipedia pages.

    1.7K150159177K viewsView on X
  • Sep 10, 20265.4x their median

    I cannot believe how much work hasn't been automated

    1.6K641151630K viewsView on X
  • Sep 8, 20265.2x their median

    I know that OpenAI was first to solve it purely based on how buttmad everyone else involved seems

    1.6K2837347K 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 322 interactions against 413K followers, an engagement rate of 0.079%. Measured over 73 original posts, its engagement rate beats 67% of 20,444 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 21K times each, and 1.54% of those impressions turn into an interaction. That is about 5.07% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 6.4 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 22:00 UTC, and Monday is the busiest day of the week. Of the 73 posts sampled, 38% carry an image or video and 19% are part of a thread. The account's strongest tracked post pulled 5.7K interactions, about 18x its own typical post.

What is kache's engagement rate on X?
kache (@yacineMTB) has an engagement rate of 0.079%, based on the median interactions across 73 original posts from the last 30 days against 412,880 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.079%, kache sits above the 25th percentile of the 205,152 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 @yacineMTB have real engagement?
Its engagement rate beats 67% of the tracked X accounts closest to it in follower count (20,444 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 @yacineMTB post?
Most posts go out around 22:00 UTC, and Monday is its busiest day, at roughly 6.43 posts per day across the measured window.

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