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Pratham engagement report

@Prathkum - 449K followers on X

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

Engagement

Middle of its size range
Per follower
0.017%
of 449K followers
Per impression
0.526%
14K views on a typical post
Reach
3.18%
of its followers see a post
Typical post
75
interactions (median)
Saved
0.039%
6 bookmarks on a typical post
Posting rate
2.13/day
active 73% of days
Peak time
16:00 UTC
Thursday

A typical post picks up 75 interactions against 449K followers, an engagement rate of 0.017%. Measured over 30 original posts, its engagement rate beats 41% 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 14K times each, and 0.526% of those impressions turn into an interaction. That is about 3.18% 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 73% of days. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 30 posts sampled, 40% carry an image or video and 17% link out. The account's strongest tracked post pulled 22K interactions, about 295x its own typical post.

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

Compared with accounts its own size

Pratham's engagement rate beats 41% of the tracked X accounts closest to it in follower count (21,185 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 32% 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 0.017%, Pratham sits above the 10th percentile of the 212,656 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%
p750.697%
p902.61%
p9982.9%
Engagement rate as a share of followers, across the 212,656 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,418 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 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 16:00 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: 16:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 16:00 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 link17% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 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.
  • 17% 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 252 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

  • Sep 7, 2026295x their median

    I think I can officially say: we are so back

    19K4932.1K1501.4M viewsView on X
  • Oct 28, 2026231x their median

    very weird thing happening rn technical people are sad, they think their skills are now useless non-technical people are desperately wishing they were more technical so they could use agents better

    15K898767243414K viewsView on X
  • Aug 28, 2026134x their median

    GLM-5.3 is now open-weight. Our most capable model for agentic coding and cyber defense is now available to download, run, and customize. Weights: https://t.co/v1IbWMXxg4 Tech blog: https://t.co/ekQkO83jCv https://t.co/f8XlJksKyf

    8.5K9512762981.5M viewsView on X
  • Oct 14, 202631x their median

    Introducing Agent-Native: an open-source framework for building agentic apps. Agents love walls of text. But humans don't. An agentic app let's you interact with agents through a visual interface. Engage with agents visually, via text, or both at once. Now in beta. https://t.co/zvaLIYcKOl

    2.1K10184295.3M viewsView on X
  • Sep 15, 202626x their median

    Anthropic CEO: "AI development needs to slow down" OpenAI CEO: "Completely agree" Two months later: Anthropic: Introducing Fable 5.2 OpenAI: Introducing GPT 6.1

    1.8K66841480K viewsView on X
  • Oct 26, 202619x their median

    Microsoft is offering voluntary exit packages to about 60,000 employees That’s roughly 27% of its workforce across the US, Canada, India and the UK Staff must decide in December, and those who accept leave on Dec. 31 https://t.co/HgwfsnUzhK

    1.1K1925448167K viewsView on X
  • Oct 4, 20269.3x their median

    Landed in SF. My Uber driver on my way to the hotel is not building a startup. Not even a side project. I’m concerned. https://t.co/5fqoS6UaWR

    617772222K viewsView on X
  • Aug 23, 20268.1x their median

    Hot take: We don't need GitHub anymore. Or at least we need a new version of it, designed agent-first. GitHub made sense when humans wrote code by hand. Developers needed a place to store code, iterate on it, and keep it backward compatible so if something broke, you could go back to a previous log and fix it. The whole workflow, PRs, issues, review, was built around a human being the one writing and reading every change. Coding has changed now. Agents write code and iterate at lightning speed. Why would an agent waste time reviewing its own code the way a human reviews a PR, when it can just reiterate and fix things in the same breath? The bottleneck GitHub was designed to manage doesn't really exist anymore. We are early, so most agent workflows just bolt onto GitHub because it's what's there, not because it's the right interface. The real shift is from reviewing diffs to grading outcomes. Whatever wins here won't be GitHub with an AI button, it'll be built agent-first from scratch.

    390121911565K viewsView on X
  • Sep 11, 20267.8x their median

    Everyone forgets DeepSeek started this whole open-weight movement. I still remember R1 dropped in Jan 2025 and everyone realized open-weight models could be useful. They were competitive with frontier closed models, which made everyone realize how expensive closed AI is. On paper, DeepSeek V4.1 Flash already beat Astra in SWE bench. DeepSeek V4.1 Flash: 74.2% GPT-6 Astra: 74.1% This number definitely looked inflated and they might increase the compute just to go above 74.1%. But the cost difference is massive. DeepSeek V4.1 Flash (open) → $0.30/$1.20 GPT-6 Astra (closed) → $10/$50 per MTok Open-weight models are something we all should take seriously.

    5182435531K viewsView on X
  • Oct 20, 20266.9x their median

    How he knew that?? Where this data is published?? And UBER pays more than Jane Street, Rubrik etc ??? https://t.co/DCYDCViQtE

    483326494K 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 75 interactions against 449K followers, an engagement rate of 0.017%. Measured over 30 original posts, its engagement rate beats 41% 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 14K times each, and 0.526% of those impressions turn into an interaction. That is about 3.18% 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 73% of days. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 30 posts sampled, 40% carry an image or video and 17% link out. The account's strongest tracked post pulled 22K interactions, about 295x its own typical post.

What is Pratham's engagement rate on X?
Pratham (@Prathkum) has an engagement rate of 0.017%, based on the median interactions across 30 original posts from the last 30 days against 448,837 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.017%, Pratham sits above the 10th percentile of the 212,656 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 @Prathkum have real engagement?
Its engagement rate beats 41% 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 @Prathkum post?
Most posts go out around 16:00 UTC, and Thursday is its busiest day, at roughly 2.13 posts per day across the measured window.

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