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Stanford AI Lab engagement report

@StanfordAILab - 268K followers on X

Measured over 9 original posts from a 30-day window, last computed on September 8, 2026.

Engagement

Middle of its size range
Per follower
0.098%
of 268K followers
Per impression
0.45%
58K views on a typical post
Reach
21.7%
of its followers see a post
Typical post
261
interactions (median)
Saved
0.374%
217 bookmarks on a typical post
Posting rate
1.67/day
active 63% of days
Peak time
19:00 UTC
Thursday

A typical post picks up 261 interactions against 268K followers, an engagement rate of 0.098%. Measured over 9 original posts, its engagement rate beats 61% of 6,493 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 58K times each, and 0.45% of those impressions turn into an interaction. That is about 21.6% 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 63% of days. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 9 posts sampled, 89% carry an image or video and 78% link out. The account's strongest tracked post pulled 2.5K interactions, about 9.6x its own typical post. Recurring topics include #paperclip, #rlc2026, #wiairpodcast.

Measured over 9 original posts from a 30-day window, last computed on September 8, 2026. Recurring tags: #paperclip, #rlc2026, #wiairpodcast.

Compared with accounts its own size

Stanford AI Lab's engagement rate beats 61% of the tracked X accounts closest to it in follower count (6,493 accounts, accounts of similar size (decile 7 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 27% 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 0.098%, Stanford AI Lab sits above the 25th percentile of the 65,864 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%
p99120.1%
Engagement rate as a share of followers, across the 65,864 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 57,191 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 percentile120.1%

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 19:00 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: 19:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 19:00 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 video89% of posts+111%+108% to +115%34K
Outbound link78% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 89% 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.
  • 78% 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 354 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 2, 20269.6x their median

    This fall @michaelryan207 @jyangballin and I are teaching a new course CS329Z "Engineering AI Agents" on how to build AI Agents from scratch. Come join us and learn how to build them 🤖

    2.2K2675317314K viewsView on X
  • Aug 27, 20268.0x their median

    Sonic-3.6 is now generally available. In January we made a bet: stop tuning the existing paradigm, rebuild from the architecture up. How we topped our own best model in two months → https://t.co/lYA5Ym6hCX https://t.co/B3EjR0TyOK

    1.6K33863372.2M viewsView on X
  • Sep 3, 20264.6x their median

    💫 Astra is out Highlights: - Much smarter (evals are really saturating...) - Much more aligned & trustworthy - Better CUA (see this house Astra made in Blender) So proud of the team for training it! Still some issues: - Too much code slop - Astra asks for confirmation too often, which can feel lazy. Given how smart it is, we wanted it to be cautious, but likely overcorrected. We’ll fix those next! What else should we fix? Note: Astra follows instructions even more carefully than 5.6, including unwanted ones buried in old skills. I’d clean those up before using it. https://t.co/6R6dZOj0xu

    1.0K588219151K viewsView on X
  • Jul 28, 2026

    Robot policies can move but can't think. LLMs can think but can't move. So we connected them. Real robot: 16.7% → 97.3% Sim (LIBERO-PRO): 12.8% → 53.3% https://t.co/4ZrIMR86gv

    3015423894K viewsView on X
  • Aug 27, 2026

    new paper: Prefix Sliding for efficient test-time scaling vanilla full attention OOMs on long tasks & compaction loses important details -- prefix sliding is a simple & fast alternative that can outperform both 📜https://t.co/fUw7yJAN5D https://t.co/PgcyrsfIZb

    3074218858K viewsView on X
  • Jul 27, 2026

    Catalog of incorrect AI predictions. Doomers from the rationalist cosplay community are left out, presumably because their predictions have a near 100% failure rate. When people proudly proclaim that we need to 'act now' or make laws because something is inevitable, point them at this list and tap it loudly because it ain't. https://t.co/VDVJeECxDr

    2374923676K viewsView on X
  • Jul 22, 2026

    MLPs store facts in language models. Can we write them into Transformers without training? New work w/ amazing team @garctrob @ronnygjunkins @EyubogluSabri, Atri Rudra & @HazyResearch gives a ✨closed-form✨ recipe for fact-storing, Transformer-ready MLPs. Accepted at COLM 2026! https://t.co/Lt8I432HAa

    215497763K viewsView on X
  • Aug 17, 2026

    We’re asking the research community to help us build a benchmark for research taste. Scientific discovery starts with a fundamental step: which prior work is worth building on? We want to capture this undocumented layer through our collective knowledge. Please sign up: https://t.co/T4vfwQnHUY ↓

    2093661074K viewsView on X
  • Aug 17, 2026

    https://t.co/OBCqmctb5S Data repetition during pretraining, when non-repeated data is available to fill the remaining portion to keep TPP constant. It is another observation on how high quality data could be repeated more, with the twist that a larger model (!) and shorter LR decay tolerate more repetition better. This could interact with the "non-repeated" web data part, as it could be a balance of noise fitting between noisy unique data and high quality repeated data.

    204245120K viewsView on X
  • Sep 3, 2026

    What a week for Terminal-Bench-Science & AI for Science in general! 🥂 Just 1 week after launch, Terminal-Bench-Science 0.1 is now also the #1 featured benchmark on today's GPT-6 Astra release by @OpenAI. GPT-5.6 Sol → 22.4% GPT-6 Astra → 64.6% The +42.2pp jump is out of this world, and that's on top of the Claude Fable 5.1 numbers from two days ago. To all scientists out there: We need to buckle up and bring the world's toughest scientific challenges together for Terminal-Bench-Science 0.2. Deadline is October 5. Let the games begin! https://t.co/GLzLVT0waU

    116129214K 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.

Recurring topics

#paperclip#rlc2026#wiairpodcast

The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.

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Reading these numbers

A typical post picks up 261 interactions against 268K followers, an engagement rate of 0.098%. Measured over 9 original posts, its engagement rate beats 61% of 6,493 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 58K times each, and 0.45% of those impressions turn into an interaction. That is about 21.6% 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 63% of days. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 9 posts sampled, 89% carry an image or video and 78% link out. The account's strongest tracked post pulled 2.5K interactions, about 9.6x its own typical post. Recurring topics include #paperclip, #rlc2026, #wiairpodcast.

What is Stanford AI Lab's engagement rate on X?
Stanford AI Lab (@StanfordAILab) has an engagement rate of 0.098%, based on the median interactions across 9 original posts from the last 30 days against 268,170 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.098%, Stanford AI Lab sits above the 25th percentile of the 65,864 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 @StanfordAILab have real engagement?
Its engagement rate beats 61% of the tracked X accounts closest to it in follower count (6,493 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 @StanfordAILab post?
Most posts go out around 19:00 UTC, and Thursday is its busiest day, at roughly 1.67 posts per day across the measured window.

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