Jeff Dean engagement report
@JeffDean - 520K followers on X
Measured over 2 original posts from a 30-day window, last computed on August 27, 2026.
Engagement
Early reading. We have captured 2 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 400 interactions against 520K followers, an engagement rate of 0.077%. Posts are seen about 314K times each, and 0.127% of those impressions turn into an interaction. That is about 60.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.57 post a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 00:00 UTC, and Tuesday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 7.7K interactions, about 19x its own typical post. Only 2 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 2 original posts from a 30-day window, last computed on August 27, 2026.
Where this sits in the catalog
At 0.077%, Jeff Dean sits above the 25th percentile of the 41,976 accounts in this comparison. That places it in the below the median band, which runs 0.013% to 0.083%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.013% |
| 50th percentile | 0.083% |
| 75th percentile | 0.448% |
| 90th percentile | 2.08% |
| 99th percentile | 142.9% |
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 00:00 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% | 59K |
| 01:00 UTC | -2% | 60K |
| 02:00 UTC | -3% | 58K |
| 03:00 UTC | -4% | 62K |
| 04:00 UTC | -6% | 50K |
| 05:00 UTC | -5% | 49K |
| 06:00 UTC | -5% | 56K |
| 07:00 UTC | -5% | 60K |
| 08:00 UTC | -4% | 70K |
| 09:00 UTC | -4% | 81K |
| 10:00 UTC | -3% | 83K |
| 11:00 UTC | -3% | 91K |
| 12:00 UTC | -2% | 100K |
| 13:00 UTC | -2% | 109K |
| 14:00 UTC | -3% | 113K |
| 15:00 UTC | -2% | 116K |
| 16:00 UTC | -3% | 113K |
| 17:00 UTC | -3% | 105K |
| 18:00 UTC | -2% | 98K |
| 19:00 UTC | -2% | 93K |
| 20:00 UTC | -1% | 87K |
| 21:00 UTC | -1% | 77K |
| 22:00 UTC | -2% | 66K |
| 23:00 UTC | -1% | 60K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 256K |
| Monday | +1% | 328K |
| Tuesday | -2% | 354K |
| Wednesday | -4% | 320K |
| Thursday | -2% | 271K |
| Friday | -3% | 276K |
| Saturday | +3% | 250K |
Best tweets
- Dec 3, 201819x their median
In April, '17, @jsomers of @NewYorker reached out & said he wanted to do a small profile of me & my longtime colleague Sanjay Ghemawat, watch us work for a few hours, maybe dinner, etc. It came out today. I think it captures our working style really well. https://t.co/cRSMiFfwAP
- Jun 12, 20267.2x their median
Today on the blog, we discuss a pathway for the second life of phones through the exploration of “phone cluster computing”, which can directly reduce the environmental footprint of computing by avoiding the need for further raw material extraction. More →https://t.co/FFUNjfaEm5 https://t.co/Fvs7ju2r0Y
- Jun 11, 20266.9x their median
What can a neuron compute? Real biological neurons are complex, but how capable are they? Using a new method, we found that a single cortical neuron can classify cats vs dogs, recognize spoken words, and solve 10-bit parity, all tasks thought to require entire networks. (1/15) https://t.co/SqQKjrEjUF
- Jul 20, 20265.6x their median
Yesterday I was fortunate enough to go to my first-ever World Cup game, with my long-time colleagues/friends @OriolVinyalsML and @quocleix and their spouses). We were sitting in the corner area and had a quite good view of the goal! I present to you a 43 second multi-part drama filled with emotion: The initial promising-looking cross coming in, but looking overhit Nico Williams cleverly knocking it down at the back post into a dangerous area Ferran Torres striking it cleanly into the roof of the net The crowd rising as one (forcing me to stand up as well) The elation of the Spanish players racing off the bench to celebrate The dejection of the Argentinian players, their defense having finally been breached in extra time The elation of my Spanish colleague Oriol and his wife Meire next to me (he and I are both Barça fans, so it was nice to see a Barça player score the winning goal) The entire stadium reacting Whew!
- Jul 17, 20264.6x their median
After the K3 drop, Huawei has now released a real 950 SuperPoD 1 EFLOPS fp8 & 2 EFLOPS fp4 256TB of unified memory https://t.co/luehBqDogu
- May 31, 20264.3x their median
This is not simply a new pancreatic cancer drug. It is a reminder that even “undruggable” biology can become treatable with persistence. Daraxonrasib doubled median OS vs chemotherapy in RAS G12 metastatic pancreatic cancer: 13.2 vs 6.6 months. A remarkable ASCO moment. #ASCO26 @DrChoueiri @TiansterZhang @CathyEngMD @montypal @tompowles1 @brian_rini @cdanicas @GlopesMd @PGrivasMDPhD @nataliagandur @yekeduz_emre @neerajaiims @ASCO @ONCOassist @OpenMedicineHQ @MedwatchKate @scserendipity1 @CParkMD @urotoday @OncLive @crisbergerot @urologysummit @SuyogCancer @Larvol @IMG_Oncologists
- Jun 18, 20263.4x their median
My @Google colleagues @NormJouppi, Sridhar Lakshmanamurthy, Cliff Young, and David Patterson recently wrote a paper that will appear in the July/August 2026 edition of @ieeemicro titled "Google's Training Supercomputers from TPU v2 to Ironwood: Architectural Stability, Scale, Resilience, Power Efficiency, and Sustainability Across Five Generations". It's chock full of interesting data about the evolution of TPU chip generations, as well as how workloads at Google have transformed over time (hint: lots more transformer-based models!), and how the generations have gotten ~30X more energy efficient per flop. Lots of changes over these generations: Air cooling in TPUv2 to water cooling in TPUv3 onwards 2D to 3D torus-based interconnects 30X improvement TFLOPS/Watt 256 chips (TPUv2) to 9216 chips (Ironwood) per pod Read the full paper: https://t.co/D5NFYFv19V
- Jun 13, 20262.0x their median
I enjoyed giving the commencement address at the University of Washington Allen School @uwcse graduation this evening. So many happy students and their families and friends! Congratulations to all the graduates of the class of 2026! 🎓 Thanks for inviting me, @MBalazinska! https://t.co/ulXPe8Ruiv
- Jul 30, 20261.7x their median
In 2001, @JeffDean and Sanjay Ghemawat did the math and realized Google’s entire search index would fit in RAM — then shipped it in a few days, and search got fast. In 2013, another napkin calculation showed that three minutes of daily speech recognition per user would require doubling Google’s server fleet. That one became the TPU. At Startup School 2026, Google’s Chief Scientist talks with YC’s @sdianahu through the thought experiments behind both, why inference hardware is the next specialization, and where two or three people in a room can still win. 00:07 — Are AI Models Already Junior Engineers? 01:44 — AI Systems That Improve Themselves 02:40 — The Google Search Breakthrough That Changed Everything 04:38 — AI Agents Will Run for Weeks 05:58 — The Napkin Math That Led to TPUs 09:20 — How to Find Breakthrough Ideas 10:25 — The AI Engineer's New Mental Model 12:33 — Why AI Is Really an Energy Problem 16:11 — Context Engineering Is the Next Frontier 19:46 — The Skill That Made AI Better at Optimization 22:13 — Why Long-Running Agents Fail 25:21 — Where Startups Can Still Beat Google 31:19 — How to Become an AI-Native Founder 36:36 — Question Your Biggest Assumptions 42:08 — AI That Builds Better AI 50:02 — Build Something That Truly Matters
- Jun 9, 2026
Speech translation has been one of the longest-running ML efforts at Google, and we’ve come a long way. Gemini 3.5 Live Translate is our latest speech-to-speech model, supporting 70+ languages. It enables more natural conversations across languages in everyday products and apps. Here’s an example of how partners at @InsideGrab are helping connect travelers with drivers. 🚗 Rolling out in Google Translate and via the Live API in @GoogleAIStudio.
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 400 interactions against 520K followers, an engagement rate of 0.077%. Posts are seen about 314K times each, and 0.127% of those impressions turn into an interaction. That is about 60.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.57 post a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 00:00 UTC, and Tuesday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 7.7K interactions, about 19x its own typical post. Only 2 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 Jeff Dean's engagement rate on X?
- Jeff Dean (@JeffDean) has an engagement rate of 0.077%, based on the median interactions across 2 original posts from the last 30 days against 519,659 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.077%, Jeff Dean sits above the 25th percentile of the 41,976 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 @JeffDean have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @JeffDean post?
- Most posts go out around 00:00 UTC, and Tuesday is its busiest day, at roughly 0.57 posts per day across the measured window.