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andrew chen engagement report

@andrewchen - 456K followers on X

Measured over 19 original posts from a 30-day window, last computed on September 24, 2026.

Engagement

Middle of its size range
Per follower
0.087%
of 456K followers
Per impression
1.10%
35K views on a typical post
Reach
7.94%
of its followers see a post
Typical post
387
interactions (median)
Saved
0.249%
88 bookmarks on a typical post
Posting rate
1.7/day
active 73% of days
Peak time
03:00 UTC
Sunday

A typical post picks up 387 interactions against 456K followers, an engagement rate of 0.087%. Measured over 19 original posts, its engagement rate beats 68% 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 35K times each, and 1.10% of those impressions turn into an interaction. That is about 7.74% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 03:00 UTC, and Sunday is the busiest day of the week. Of the 19 posts sampled, 26% carry an image or video, 26% are part of a thread and 5% link out. The account's strongest tracked post pulled 1.7K interactions, about 4.3x its own typical post.

Measured over 19 original posts from a 30-day window, last computed on September 24, 2026.

Compared with accounts its own size

andrew chen's engagement rate beats 68% 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 50% 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 0.087%, andrew chen sits above the 25th percentile of the 204,956 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 204,956 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 03:00 UTC, and Sunday 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: 03:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 03:00 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: Sunday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Sunday
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 video26% of posts+111%+108% to +115%34K
Outbound link5% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 26% 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.
  • 5% 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 326 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

  • Aug 23, 20264.3x their median

    2012: acquire company for users 2021: acquire company for engineers 2026: acquire company for training data

    1.4K1238941109K viewsView on X
  • Sep 1, 20263.8x their median

    Before AI: - hardware is hard - teams > solo founders - technical founders only - PhDs build tech in search of a problem - hot SaaS startups get 100x ARR multiples After AI: - hardware is the only thing AI can't build - solo founders are 100x engineers - anyone can build - PhDs building AI infra name their price - SaaSpocalypse Am I missing anything?

    1.2K8218634175K viewsView on X
  • Sep 20, 20263.5x their median

    Jev is going to change the prosumer/consumer AI landscape by unlocking a specific thing: Ad-supported + free AI native apps this is going to usher in a generation of new AI-native marketplaces, social networks, photo apps, messaging, calendars, email, collab tools, and much more. Why? Previously, if you wanted to have an AI-native app with multiple/fast LLM calls on every screen, the math just didn’t work - inference costs just couldn’t be paid back from throwing a few video ads or affiliate links. So there were really just two solutions: 1) be a massive company and subsidize AI costs 2) charge a subscription fee to cover inference (or both) So what happens when you take a different approach that’s looks to be, initially, >400x cheaper than a more general LLM? I think it means that app developers are going to make their apps smarter and different in interesting ways. It won’t be as generically powerful as LLMs, but instead folks will create highly differentiated point solutions. You may not have a generically powerful virtual admin to schedule all your meetings, but you may have a free/ad-powered assistant that can comb through your inbox, filter for only important emails, figure out important dates, prioritize contacts, and do other interesting 80/20 work. Same for generically powerful virtual companions, or app builders, etc - it may be that we can hybridize the LLM and future Jev-like models that adds “wow” features at 1/1000ths of the cost, to make the economics work. If folks hear of interesting new apps/interactions/etc built on Jev, I’d love to hear it!

    1.1K8113321101K viewsView on X
  • Aug 1, 20262.8x their median

    Unlimited mathematical proofs but limited skilled mathematicians who can verify them Unlimited code but limited skilled programmers to review them Unlimited videos but limited attention to watch them Unlimited lawsuits but limited courts/judges/lawyers to adjudicate them When the creation of something goes to zero, the cost moves elsewhere…

    8421051052892K viewsView on X
  • Aug 30, 20262.4x their median

    15M npm downloads / week 🚀 https://t.co/X4Tcos6YGR

    796397030162K viewsView on X
  • Aug 10, 20261.6x their median

    COMING SOON - a new a16z office launching in SF Want to guess the meeting room naming scheme? https://t.co/A8i5LbjCsh

    506101108106K viewsView on X
  • Sep 24, 20261.6x their median

    Epic https://t.co/wOIVIamS4m

    475903626266K viewsView on X
  • Aug 24, 2026

    it’s live! we’re flying 20 top builders from around the world to San Francisco for Tech Week Oct 5-11th Applications for the Global Founder Sprint by @speedrun are now live What? - up to $3K to cover travel costs to SF (by @Airwallex) - small dinner with a16z investors - invites to VIP a16z events @Techweek_ - access to executive buyers & customers at our AI Faire - US sales coaching Who? - very early global builders just thinking about starting >>> Apply below! App open for 7 days <<< https://t.co/MFAAZDoK4w

    385376019127K viewsView on X
  • Sep 4, 2026

    i'd pay 2x for an iPhone where siri/dication/other AI features actually were good

    350496130K viewsView on X
  • Jul 31, 2026

    Run DeepSeek v4 Flash GA (0731) on 2x @NVIDIAAI DGX Sparks with easy ✨ 82 tok/s single stream, peak ~95 tok/s 135 tok/s at 3 sessions Uses the official FP8 weights. Special thanks to @0xSero for a PR on this. Further improvements to the recipe are planned! Get it here: https://t.co/6HXa9pxqhj

    36823401565K 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 387 interactions against 456K followers, an engagement rate of 0.087%. Measured over 19 original posts, its engagement rate beats 68% 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 35K times each, and 1.10% of those impressions turn into an interaction. That is about 7.74% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 03:00 UTC, and Sunday is the busiest day of the week. Of the 19 posts sampled, 26% carry an image or video, 26% are part of a thread and 5% link out. The account's strongest tracked post pulled 1.7K interactions, about 4.3x its own typical post.

What is andrew chen's engagement rate on X?
andrew chen (@andrewchen) has an engagement rate of 0.087%, based on the median interactions across 19 original posts from the last 30 days against 456,317 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.087%, andrew chen sits above the 25th percentile of the 204,956 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 @andrewchen have real engagement?
Its engagement rate beats 68% 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 @andrewchen post?
Most posts go out around 03:00 UTC, and Sunday is its busiest day, at roughly 1.7 posts per day across the measured window.

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