Berkeley AI Research engagement report
@berkeley_ai - 299K followers on X
Measured over 6 original posts from a 30-day window, last computed on October 21, 2026.
Engagement
Early reading. We have captured 6 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 384 interactions against 299K followers, an engagement rate of 0.129%. Posts are seen about 35K times each, and 1.10% of those impressions turn into an interaction. That is about 11.7% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 18:00 UTC, and Tuesday is the busiest day of the week. Of the 6 posts sampled, 83% carry an image or video and 50% link out. The account's strongest tracked post pulled 766 interactions, about 2.0x its own typical post. Recurring topics include #colm2026, #aaai2027, #aiforscience. Only 6 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 6 original posts from a 30-day window, last computed on October 21, 2026. Recurring tags: #colm2026, #aaai2027, #aiforscience.
Where this sits in the catalog
At 0.129%, Berkeley AI Research 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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.677% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
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 18:00 UTC, and Tuesday 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 | 83% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 50% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 83% 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.
- 50% 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 300 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
- Oct 6, 20262.0x their median
Can “chicken” make a base model reason? 🐔 Yes! Why? With the right first tokens, base models can match RL in reasoning. We trace this to learned training data associations and use a simple data edit to make “chicken” elicit reasoning too. blog: https://t.co/O84Sn5GC4F https://t.co/E52I6SRQhS
- Jun 16, 20261.8x their median
Why aren’t Diffusion Language Model smart yet? Lacking stable post training is a major bottleneck! Meet DiPOD: the tripod for diffusion model post-training. DiPOD boosts accuracy across reasoning tasks, with Sudoku jumping from 22% to 97%, through a one-line code change. 🧵1/5 https://t.co/MByCn57HGA
- Oct 2, 2026
Almost all bimanual robots include wrist cameras. They add bulk, cost, and complexity, but are critical for performance. Are they really needed? Excited to finally share EyeRobot 2.0, where we use active gaze from an ego stereo camera instead 🧵 (with @kushtimusprime) https://t.co/iAjzZaxIm2
- Oct 6, 2026
Excited to introduce QF3: Fast Flow RL with Filtered Q-Gradients! Train humanoids from scratch, fine-tune VLAs, even fine-tune image models... all with one simple off-policy update ✨ https://t.co/y80f3KYdl6
- Jul 25, 2026
«دنیای ماهو» ــ گفتگو با سه دانشمند ایرانی هوش مصنوعی هوش مصنوعی چه امکانها و خطرهای تازهای برای آیندهی ما دارد؟ چه تأثیری بر اقتصاد، مشاغل، تحصیل، و حکومتداری خواهد داشت؟ آیا عاملیت ما و نگاهمان به زندگی متحول خواهد شد؟ آیا تشخیص واقعیت دشوارتر یا آسانتر میشود؟ آیا ممکن است که کنترل هوش مصنوعی از دست بشر خارج شود؟ در این فیلم، ماهو که خودش ساختهی هوش مصنوعیست برای یافتن پاسخ به این سؤالها به سراغ نیکا حقطلب استاد مهندسی برق و علوم کامپیوتر دانشگاه کالیفرنیا در برکلی، آتوسا کثیرزاده پژوهشگر هوش مصنوعی در زمینهی فلسفه و اخلاق و استادیار دانشگاه کارنگی ملون، و نیلوفر میرعشقالله استادیار علوم کامپیوتر دانشگاه کارنگی ملون میرود که هر سه از دانشمندان برجسته در حوزهی هوش مصنوعیاند. این فیلم را در یوتیوب آسو ببینید. https://t.co/liTVI9qzgu
- Sep 18, 2026
Happy to announce the full ABC release! We’re also excited that ABC was accepted to CoRL 2026! Check out our website for code, 400+ hours of sim data on 24 tasks, and 5,850 labeled policy-evaluation episodes. @arthurallshire @Cinnabar233 @ritvik_singh9 @redstone_hong @davidrmcall
- Oct 14, 2026
AI is cracking math problems. What's holding it back in engineering? The cost of evaluation. Agents can generate ideas in seconds, but testing them can take hours or days. 💡 Meet DuoDiscover: where evaluation is part of what agents discover 👇🧵 Some highlights: 🏆 #1 on NVIDIA's SOL-ExecBench 🐍 2× the experts on SWE-fficiency 🚀 1.92× on vLLM; 1.44× on FreeToken 🔥 2.68× over production TPU kernels 🖥️ Beats the best human and AI-designed CPU prefetchers
- Jul 13, 2026
🤖How well do today's VLMs actually understand real-world robotics? 👀 Excited to share RoboVista at #RSS2026 — a systematic evaluation and benchmark for VLMs across diverse, real-world robot applications. Website, dataset and paper: https://t.co/saNSk3iVsg Developed by researchers at @UCBerkeley, @GoogleDeepMind, and @Princeton. 🧵👇
- Jul 29, 2026
Property guidance for protein sequence generative models with ProteinGuide https://t.co/xCysnYtD3a https://t.co/jYZY46uCeI
- Sep 14, 2026
🚨🚨🚨 New paper alert 🚨🚨🚨 To understand LLMs impact on work, we first need to examine their capabilities across the entire occupation distribution. Doing this in a scalable way is hard and costly. Presenting - "ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge" a new working paper with @serinachang5 and @ShreyasK1609 that takes this challenge head on! paper: https://t.co/81wU349DFn dashboard: https://t.co/wJv9INqcj5
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 384 interactions against 299K followers, an engagement rate of 0.129%. Posts are seen about 35K times each, and 1.10% of those impressions turn into an interaction. That is about 11.7% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 18:00 UTC, and Tuesday is the busiest day of the week. Of the 6 posts sampled, 83% carry an image or video and 50% link out. The account's strongest tracked post pulled 766 interactions, about 2.0x its own typical post. Recurring topics include #colm2026, #aaai2027, #aiforscience. Only 6 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 Berkeley AI Research's engagement rate on X?
- Berkeley AI Research (@berkeley_ai) has an engagement rate of 0.129%, based on the median interactions across 6 original posts from the last 30 days against 299,125 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.129%, Berkeley AI Research 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 @berkeley_ai have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @berkeley_ai post?
- Most posts go out around 18:00 UTC, and Tuesday is its busiest day, at roughly 1.57 posts per day across the measured window.