Stanford AI Lab engagement report
@StanfordAILab - 277K followers on X
Measured over 12 original posts from a 30-day window, last computed on October 29, 2026.
Engagement
A typical post picks up 69 interactions against 277K followers, an engagement rate of 0.025%. Measured over 12 original posts, its engagement rate beats 35% of 21,624 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 12K times each, and 0.562% of those impressions turn into an interaction. That is about 4.42% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 12 posts sampled, 42% carry an image or video and 67% link out. The account's strongest tracked post pulled 2.5K interactions, about 37x its own typical post.
Measured over 12 original posts from a 30-day window, last computed on October 29, 2026. Recurring tag: #icml2026.
Compared with accounts its own size
Stanford AI Lab's engagement rate beats 35% of the tracked X accounts closest to it in follower count (21,624 accounts, accounts of similar size (decile 9 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 28% 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 0.025%, Stanford AI Lab sits above the 10th percentile of the 217,518 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.148% |
| 75th percentile | 0.704% |
| 90th percentile | 2.63% |
| 99th percentile | 82.5% |
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 17:00 UTC, and Wednesday 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 | 42% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 67% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 42% 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.
- 67% 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 366 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, 202637x 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 🤖
- Oct 28, 202631x their median
I improved NanoGPT Speedrun from 39.9s → 6.7s (-83.2%), more than every record since 2025 combined (-81.4%)! The web is full of rare, repeated content that the LM can’t memorize in time. I solve this with retrieval-augmented n-gram hints🧵 https://t.co/PKQdSpWdxP
- Aug 27, 202629x 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
- Oct 14, 202620x their median
🚀 Introducing Next-Concept Modeling (NCM): our new paradigm for fast, parallel, coarse-to-fine text generation. Compared to strong diffusion language models (DLMs) at similar parameter size, NCM achieves: ⚡ Up to 31× faster generation on downstream tasks like summarization and machine translation with comparable or better quality. 🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity and using 3–5× fewer training tokens. 🧩 Better performance on reasoning tasks like Sudoku, outperforming both AR and diffusion baselines with fewer sampling steps. With @ishanskhare, @IddahMlauzi, @ekellbuch, @simran_s_arora, @Azaliamirh, and @HazyResearch. More details in the thread 🧵👇
- Sep 3, 202617x 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
- Sep 17, 202611x their median
Super excited that #VirtualBiotech is published in @ScienceMagazine today! Tens of thousands of AI scientist agents work together across drug discovery + development, from target discovery to clinical trial design. Our blueprint for agentic R&D 🧵 https://t.co/WKFKrvx1eW
- Jul 8, 202610x their median
How can we extract richer signals from AI Feedback? Introducing LLM-as-a-Verifier✨— a simple verification scaling framework that achieves SOTA on agentic benchmarks 🚀 The key idea: - Use fine-grained scoring granularity (e.g., 1-20 instead of the standard 1-5 scale) - Take the expectation over the full logprob distribution of score tokens - Scale repeated evaluation and criteria decomposition You can use these fine-grained signals for more effective test-time scaling, RL, and agent monitoring! It achieves SOTA across Terminal-Bench V2, SWE-Bench Verified, RoboRewardBench, and MedAgentBench 👑 Advised by @Azaliamirh @istoica05 @drmapavone @chelseabfinn 🧵👇
- Oct 21, 20266.8x their median
Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter. Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇
- Jul 28, 20261.6x their median
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, 20265.5x their median
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
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 69 interactions against 277K followers, an engagement rate of 0.025%. Measured over 12 original posts, its engagement rate beats 35% of 21,624 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 12K times each, and 0.562% of those impressions turn into an interaction. That is about 4.42% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Wednesday is the busiest day of the week. Of the 12 posts sampled, 42% carry an image or video and 67% link out. The account's strongest tracked post pulled 2.5K interactions, about 37x its own typical post.
- What is Stanford AI Lab's engagement rate on X?
- Stanford AI Lab (@StanfordAILab) has an engagement rate of 0.025%, based on the median interactions across 12 original posts from the last 30 days against 277,444 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.025%, Stanford AI Lab sits above the 10th percentile of the 217,518 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 35% of the tracked X accounts closest to it in follower count (21,624 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 17:00 UTC, and Wednesday is its busiest day, at roughly 2.2 posts per day across the measured window.