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Akshay 🚀 engagement report

@akshay_pachaar - 292K followers on X

Measured over 7 original posts from a 30-day window, last computed on October 22, 2026.

Engagement

Per follower
0.077%
of 292K followers
Per impression
0.817%
28K views on a typical post
Reach
9.45%
of its followers see a post
Typical post
225
interactions (median)
Saved
0.867%
239 bookmarks on a typical post
Posting rate
1.27/day
active 80% of days
Peak time
13:00 UTC
Saturday

Early reading. We have captured 7 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 225 interactions against 292K followers, an engagement rate of 0.077%. Posts are seen about 28K times each, and 0.817% of those impressions turn into an interaction. That is about 9.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 13:00 UTC, and Saturday is the busiest day of the week. Of the 7 posts sampled, 57% carry an image or video, 14% are part of a thread and 71% link out. The account's strongest tracked post pulled 6.5K interactions, about 29x its own typical post. Only 7 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 7 original posts from a 30-day window, last computed on October 22, 2026.

Where this sits in the catalog

At 0.077%, Akshay 🚀 sits above the 25th percentile of the 204,313 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%
p750.677%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 204,313 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,878 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 percentile0.677%
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 13:00 UTC, and Saturday 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: 13:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 13:00 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: Saturday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Saturday
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 video57% of posts+111%+108% to +115%34K
Outbound link71% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 57% 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.
  • 71% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
  • Its average post runs 861 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 18, 202629x their median

    https://t.co/haL8IGhx3h

    5.6K6641311322.5M viewsView on X
  • Apr 6, 202619x their median

    https://t.co/VoQByJUrBU

    3.5K589891022.7M viewsView on X
  • Nov 14, 202317x their median

    My lecture at MIT!✨ From Physics to Linear Algebra & Machine learning, I have learned a lot from MIT! Yesterday, I had the honour of delivering a guest lecture on The state of AI Engineering, exploring: - Prompt Engineering - Retrieval Augmented Generation. - Fine-Tuning Large Language Models. - And, how LLMs are bringing a paradigm shift Thank you @RoyShilkrot for the invitation! 🙏 Grateful for the opportunity to engage with such brilliant minds! If you are interested AI Engineering, I write a free weekly newsletter → @ML_Spring

    3.3K397134121.0M viewsView on X
  • Jun 22, 20268.4x their median

    https://t.co/q4DvRIfStN

    2.0K30252452.2M viewsView on X
  • Mar 4, 20269.9x their median

    https://t.co/VhUJPU8alY

    1.9K2843120922K viewsView on X
  • Jun 8, 20266.6x their median

    https://t.co/YUGDPxpYvy

    1.6K21541191.8M viewsView on X
  • Jun 27, 20265.9x their median

    https://t.co/5lb52h4oT1

    1.4K2282512774K viewsView on X
  • Feb 9, 20266.5x their median

    https://t.co/YFjQ2dQx4H

    1.2K1882111670K viewsView on X
  • Oct 21, 20265.3x their median

    Another blow to Anthropic! Devs built a free and better Claude desktop alternative that: - runs locally - works with any LLM - SOTA deep research - has Cowork-like capabilities - connects to 40+ data sources - self-hosts via Docker, and more. 100% open-source. https://t.co/RpKuow34sD

    1.0K13349260K viewsView on X
  • Oct 3, 20265.1x their median

    everyone is sleeping on this new JSON extraction model! - open-weights - matches Gemini Flash 3.5 - while being 3x faster pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON. lift, from @datalabto, does all of this in a single step. you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema. the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers. here's what you get with lift: - 90.2% field accuracy on 11,000 fields - 9B model, nearly matches Gemini Flash 3.5 at 3x the speed - built not to hallucinate; returns null if missing - self-hostable with vLLM or Hugging Face GitHub repo: https://t.co/akcdjgT6ei (don't forget to star 🌟)

    97212047461K 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 225 interactions against 292K followers, an engagement rate of 0.077%. Posts are seen about 28K times each, and 0.817% of those impressions turn into an interaction. That is about 9.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 13:00 UTC, and Saturday is the busiest day of the week. Of the 7 posts sampled, 57% carry an image or video, 14% are part of a thread and 71% link out. The account's strongest tracked post pulled 6.5K interactions, about 29x its own typical post. Only 7 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 Akshay 🚀's engagement rate on X?
Akshay 🚀 (@akshay_pachaar) has an engagement rate of 0.077%, based on the median interactions across 7 original posts from the last 30 days against 291,773 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.077%, Akshay 🚀 sits above the 25th percentile of the 204,313 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 @akshay_pachaar have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @akshay_pachaar post?
Most posts go out around 13:00 UTC, and Saturday is its busiest day, at roughly 1.27 posts per day across the measured window.

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