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
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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 120.1% |
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.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 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 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
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.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 89% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 78% 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 🤖
- 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
- 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
- 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
- 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
- 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
- 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
- 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 ↓
- 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.
- 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
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
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.