Russ Salakhutdinov engagement report
@rsalakhu - 129K followers on X
Measured over 5 original posts from a 30-day window, last computed on October 2, 2026.
Engagement
Early reading. We have captured 5 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 130 interactions against 129K followers, an engagement rate of 0.101%. Posts are seen about 18K times each, and 0.724% of those impressions turn into an interaction. That is about 13.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.5 post 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 Wednesday is the busiest day of the week. Of the 5 posts sampled, 80% carry an image or video and 20% link out. The account's strongest tracked post pulled 3.1K interactions, about 24x its own typical post. Only 5 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 5 original posts from a 30-day window, last computed on October 2, 2026.
Where this sits in the catalog
At 0.101%, Russ Salakhutdinov sits above the 25th percentile of the 205,261 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 | 0.678% |
| 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 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 | 80% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 20% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 80% 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.
- 20% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 400 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
- Jul 18, 202624x their median
I’m afraid to tell you that it is effectively impossible to do the kind of writing I used to do on this website, not because anyone at OpenAI censors me but because of the sheer volume of hostility I get for sharing my analysis as a frontier lab employee. I enjoyed writing quick takes on this website for one basic reason: I could get rapid feedback on my own ideation process in real time. Post the early version of the take here, see the criticism; then refine, sharpen, and repeat. Unfortunately now that feature of this site is gone, because the feedback I get is now almost exclusively colored by resentment at the fact that I work at a frontier lab or other forms of hatred for my employer. The feedback signal is essentially useless now, so writing on here is not fruitful for me anymore. Literally everything I write now is responded to with “of course you said that because <OpenAI>.” I am truly just writing what I think and would have written anyway, but everyone reads what I say in the shrieking tone of “this is what openai thinks!!!!” (to be clear, my posts are not what openai thinks). This is an unpleasant and more importantly unproductive pattern for me. I anticipate that the shape of this account will change significantly as a result. I do not currently know how. It will not become a LinkedIn feed. It will change in some other way. It will no longer be a real-time accounting of my own thinking as it develops, since this is precisely the thing that seems impossible to do now. That will have to shift to private channels.
- Aug 15, 20265.8x their median
What UK AI startups need: @KanishkaNarayan In California, one can start a company the day after leaving Google. My colleagues Jeff Dean, Oriol Vinyals et al made this very clear recently with their impressive speed. In the UK, the same American companies impose 1 year garden leaves on senior AI researchers and 6 months on junior researchers. @GoogleDeepMind for example forced people to sign these contracts at the time of promotion, not the time of hiring. What this means is that researchers cannot start new companies for up to 1 year, cannot easily hire, and in short: they cannot compete. American VCs cannot understand why we move so slow. It is time for UK Gov to do the right thing: Make garden leaves optional for employees. If you’re an AI entrepreneur or VC in the UK or Europe, I would appreciate your comments here. You’ve all confided in me. It is time you let government know the urgency of this — everyone should speak up.
- Sep 2, 20264.7x their median
Is RL optimizing the right objective? 🤔 Should we maximize mean reward? Best-of-k? Which k? Standard RL pulls on the mean and often the distribution collapses to a spike. The tail dies 🥲 We introduce Tail-Likelihood Reinforcement Learning (TailRL). It maximizes the mean reward while simultaneously maximizing coverage over high reward outputs. 🧵 1/n
- Sep 30, 20261.9x their median
What if an agent could decide how to structure its own computation? Agentic Meta-Reasoning: Let the model decide what to explore, what to build on, what to verify, and where to spend its next unit of compute: effectively constructing its own computational graph as it reasons. 🧵 Paras Dahal, @anton_bakhtin , @TacoCohen , Zhengxing Chen, Carole-Jean Wu, Rob Fergus, Scott Yih, @syhw , @rsalakhu , @prfsanjeevarora , @jaseweston
- Aug 1, 2026
I rarely do podcasts, as they tend to be less technical and more speculative, but I enjoyed this conversation with Forward Deployed @realbasilchatha. We talked about the early days of deep learning, what frontier AI labs are working on (data, infra, engineering), some of our recent research on agentic AI, prediction markets, and decision-making. https://t.co/d5fKcWFPei https://t.co/cgCKJL4mUu https://t.co/LjyTZBaDSR
- Aug 22, 2026
Forking-Sequences — Part I: Statistically and Computationally Efficient Multi-Horizon Forecasting – Machine Learning Blog | ML@CMU | Carnegie Mellon University https://t.co/qR66tkv81H
- Oct 1, 2026
Computer-use agents are extremely good! But they need to improve on a metric which most benchmarks don't track. How much time do CUAs take to complete a task? Introducing cua-speedrun: a benchmark for accuracy, speed, and cost under matched infra. https://t.co/JQKNrkGKHs 👇🧵 https://t.co/1O6rDvpRy6
- Jul 29, 2026
Looks like my X account has been hacked. Someone has been sending DMs from my account to random people, inviting them to schedule a meeting with me via a Calendly link. Those messages are not from me. Please ignore them. Help me retweet this message. Hey @X, I changed my password but I could really use your help resolving this.
- Sep 30, 2026
1/8 How do we train a model to adapt to tasks it has never seen, without updating its weights at test time? With Harness Learning, we train a model to revise harnesses using execution feedback. At test time, the model applies this learned skill to improve harnesses on unseen tasks, while model weights stay fixed. Improving agent harnesses takes experimentation. We want a model to learn from those revisions and make better edits on unseen tasks. Models trained with harness learning nearly double their performance on challenging unseen reasoning and multihop question-answering tasks.
- Sep 20, 2026
Quite a few people have been asking me about the probability of doom and AI existential risk. Well, I am more worried about my X account getting hacked. And someone actually hacked it this morning. For some reason, it happens more often than I thought it would. So if you saw me posting some crazy Crypto ads, I am sorry.
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 130 interactions against 129K followers, an engagement rate of 0.101%. Posts are seen about 18K times each, and 0.724% of those impressions turn into an interaction. That is about 13.9% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.5 post 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 Wednesday is the busiest day of the week. Of the 5 posts sampled, 80% carry an image or video and 20% link out. The account's strongest tracked post pulled 3.1K interactions, about 24x its own typical post. Only 5 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 Russ Salakhutdinov's engagement rate on X?
- Russ Salakhutdinov (@rsalakhu) has an engagement rate of 0.101%, based on the median interactions across 5 original posts from the last 30 days against 129,227 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.101%, Russ Salakhutdinov sits above the 25th percentile of the 205,261 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 @rsalakhu have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @rsalakhu post?
- Most posts go out around 18:00 UTC, and Wednesday is its busiest day, at roughly 0.5 posts per day across the measured window.