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Keith Rabois engagement report

@rabois - 489K followers on X

Measured over 11 original posts from a 30-day window, last computed on August 28, 2026.

Engagement

Middle of its size range
Per follower
0.105%
of 489K followers
Per impression
0.608%
84K views on a typical post
Reach
17.3%
of its followers see a post
Typical post
511
interactions (median)
Saved
0.3%
252 bookmarks on a typical post
Posting rate
1.73/day
active 50% of days
Peak time
15:00 UTC
Tuesday

A typical post picks up 511 interactions against 489K followers, an engagement rate of 0.105%. Measured over 11 original posts, its engagement rate beats 69% of 3,882 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 84K times each, and 0.608% of those impressions turn into an interaction. That is about 17.2% 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 50% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 11 posts sampled, 36% carry an image or video and 55% link out. The account's strongest tracked post pulled 15K interactions, about 29x its own typical post.

Measured over 11 original posts from a 30-day window, last computed on August 28, 2026.

Compared with accounts its own size

Keith Rabois's engagement rate beats 69% of the tracked X accounts closest to it in follower count (3,882 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 38% 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 0.105%, Keith Rabois sits above the 50th percentile of the 37,582 accounts in this comparison. That places it in the above the median band, which runs 0.081% to 0.439%.

p100.002%
p250.012%
p50 (median)0.081%
p750.439%
p902.10%
p99158.1%
Engagement rate as a share of followers, across the 37,582 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 105,409 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.002%
25th percentile0.012%
50th percentile0.081%
75th percentile0.439%
90th percentile2.10%
99th percentile158.1%

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 15:00 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.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 15:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 15:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%52K
01:00 UTC-2%53K
02:00 UTC-3%51K
03:00 UTC-4%55K
04:00 UTC-6%44K
05:00 UTC-4%43K
06:00 UTC-4%50K
07:00 UTC-5%54K
08:00 UTC-4%62K
09:00 UTC-3%72K
10:00 UTC-2%74K
11:00 UTC-3%81K
12:00 UTC-2%89K
13:00 UTC-2%98K
14:00 UTC-4%101K
15:00 UTC-2%104K
16:00 UTC-3%102K
17:00 UTC-3%94K
18:00 UTC-1%88K
19:00 UTC-2%83K
20:00 UTC-1%77K
21:00 UTC-1%68K
22:00 UTC-2%59K
23:00 UTC-2%53K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%237K
Monday0%300K
Tuesday-3%299K
Wednesday-1%256K
Thursday-1%249K
Friday-3%258K
Saturday+3%232K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Jun 2, 201729x their median

    Formula for startup success: Find large highly fragmented industry w low NPS; vertically integrate a solution to simplify value product.

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  • Aug 23, 202627x their median

    Phillips once owned: 100% of ASML (market cap currently $687B) 28% of TSMC (market cap currently $1.95T) Phillips is currently worth $27B 😅

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  • Aug 11, 202614x their median

    Florida is now the safest big state in America. Miami, Orlando and Tampa combined had just 29 murders in the first half of 2026. That’s incredible leadership and police work. Miami had just 8 murders so far in 2026, down 50% from last year. https://t.co/qElUkYvTBx

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  • Aug 23, 20261.9x their median

    The full enterprise sales cycle, step by step with @jjen_abel Most people think there are 5 sales stages. There are actually 15. Skip a step and 💀 We discuss: 🔸 The “pincer model” for landing the first meeting 🔸 How to craft a winning 2-3 sentence cold outreach pitch 🔸 How to run an intro call that extracts maximum intelligence 🔸 The correct 2-3 day pilot structure 🔸 Pro tips for navigating pricing and procurement 🔸 So much more 84 minutes of enterprise sales alpha. Listen now 👇 https://t.co/HnZK0FyNON

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  • Aug 10, 2026

    Interesting to see SaaS companies starting to report MCP usage on earnings calls: - Datadog: MCP tool calls up 4x q/q, 22x since Q4 2025 - Figma: MCP write usage up 75% q/q - Atlassian: MCP calls up 400% q/q

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  • Aug 23, 2026

    Everything you need to know about enterprise sales: https://t.co/tHVgF36zWM

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  • Aug 12, 2026

    after 4 years of research, i wrote the world's definitive guide to what palantir actually is. thank you to @nikunj, @gokulr, @bennstancil, @jasnonaz, @dkrevitt, @_amankishore, @edraluk, @caelin_sutch, & others for reviewing this essay https://t.co/K1AncDHWXt

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  • Aug 11, 2026

    Ready for prime time. https://t.co/oHf5lO6TDd

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  • Aug 27, 2026

    https://t.co/rSoZNsALNJ

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  • Aug 25, 2026

    Leaders Must Manufacture Discomfort @frank_slootman (Frank Slootman), former CEO of Data Domain, @ServiceNow , and @Snowflake, interviewed by @nakul (Nakul Mandan), @AudaciousHQ (Audacious) (Knuckle Up with Nakul) Summary: Frank Slootman is the only person to take three enterprise software companies public, and he thinks the CEO's job is to keep the organization permanently uncomfortable. Most leaders set goals they already know they can hit and keep people they have privately written off. Slootman's answer is to compress every timeframe, buy aptitude rather than experience, and attack doubt by acting on it. Take that seriously and you stop protecting a calm quarter and start finding out what the business can actually do. 1. Time Compression. The CEO job is confrontational because you spend it compressing timeframes. Slootman treats every meeting and hallway conversation as a chance to change the intensity, the urgency, the pace, and whether the team is aiming high enough. He opens exec conversations with "how do you think it's going," then keeps opening the aperture until the person gets to his conclusion on their own. Most of them work out a week later that they got their ass kicked. 2. Drivers And Passengers. Come home on a Friday night, look in the mirror, and ask whether it mattered that you were there and where you moved the dials. An engineer once asked Slootman at an all-hands how to tell which one he was, and the answer was "you better find out before I do." Passengers are usually articulate, well-liked, and never stand out in a negative way, which is what makes them hard to see. Every company carries some, and Slootman says that in big companies it becomes a disease that eventually gets treated with a layoff. 3. The Aptitude Purchase. Slootman's first question to an exec candidate is what they are innately, effortlessly good at, the thing everyone else marvels at. "I'm not buying your experience because I can get you experience. I cannot get you aptitude." The inverse question follows: what job is absolutely lost on you, what should we never ask you to do. Most candidates treat both as trick questions, and Slootman says many have never been asked either one. 4. Back-Channel References. An interview gives you a vibe, and a vibe is a feeling. The real signal comes from superiors, subordinates, and peers going back at least 5 years, and Slootman puts his time there rather than into a 15-person interview loop. His favorite reference question is which of those three groups the candidate had trouble with, since almost everyone had trouble with one. He likes hearing peers, because ambitious people who take ownership clash; subordinates is the answer he does not want to hear. 5. The Empty Seat. "When there's doubt, there's no doubt" was the tiebreaker on every hire, and Slootman used it on every decision, not just recruiting. Once someone is clearly wrong for a role, coaching is usually a fool's errand that costs you time and ends in separation anyway. Having nobody is better than having somebody who is not the right person. CEOs sit on mediocrity because moving makes them look wrong, and "people don't just watch what you do, they watch what you don't do." 6. The 125 Question. At Data Domain, coming off $45 million, the VP of Sales built a plan for $100 million that the board would have rubber-stamped. Slootman asked what he would do differently if the number were $125 million, listened to the answer, and said "well, why don't we just do that, then?" They hit $125 million by changing the assumptions rather than the resources. Goals are insanely powerful because people immediately start breaking them down, which is why a limp goal quietly wastes the business. 7. Heels And Tips. 99% of people do not lean in hard enough. Slootman puts it in ski terms: skis are built to be ridden forward, and people ride them on their heels. Push until evidence piles up that you are overdoing it, because with a good product the evidence almost always piles up that you are underdoing it. If the resources genuinely do not convert to yield, you have just learned something more important about the business. 8. Decision Velocity. Getting everybody aligned is nice, and getting the right people aligned is essential, so Slootman never treated consensus as a goal in its own right. The question is who is carrying the execution, because those are the people who have to buy in. Waiting is a bigger risk than going, and acting triggers energy and speeds up the learning that follows. On harassment and integrity violations he moves the same day, because the organization reads response time as the real policy. 9. Standards Are The Culture. Culture exists to serve the mission, and a high-growth operation is uncomfortable by construction. The useful question is which behaviors the mission needs, which usually means a tolerance for growing faster, risking more, and working harder than feels comfortable. Integrity and respectful interaction were non-negotiable at Slootman's companies, and violations got prosecuted, because consequences are how people learn a standard is real. Culture you are unwilling to prosecute stays a set of good intentions. 10. Manufactured Anxiety. Leaders should drive high anxiety through the ranks on purpose so nobody takes their position for granted. Slootman lives in anxiety even when the numbers are good, because believing tomorrow will be fine is how people go back to sleep. He reads the current AI moment as one giant wake-up call: you may have been comfortable before, but you probably should not have been. The question he puts to CEOs is whether they are actually processing it. 11. Evaporating Swim Lanes. Slootman changed course at Snowflake because the company's swim lanes were evaporating, turning a predictable, profitable selling motion into a mega market with unfamiliar boundaries. He compares it to Intel leaving memory chips, where you commit to the move before you can see the other side. That changes how you operate, because you now have to try many things knowing most will fail. He also handed the CEO job to Sridhar Ramaswamy while holding the largest individual stake, on the view that the company needed something he did not have. 12. Providence Follows Commitment. Slootman's answer to doubt is to attack it with everything available rather than wall it off, and he describes wanting to face his demons "for breakfast." In Data Domain's first year the product moved 25 megabytes a second and everyone told him it would never fly. They found use cases small enough to sell, did $3 million, and stayed alive long enough for Intel's multi-core gains to carry the product. "When you commit, providence commits as well," and when you hesitate the world does nothing.

    110179133K viewsView on X

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 511 interactions against 489K followers, an engagement rate of 0.105%. Measured over 11 original posts, its engagement rate beats 69% of 3,882 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 84K times each, and 0.608% of those impressions turn into an interaction. That is about 17.2% 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 50% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 11 posts sampled, 36% carry an image or video and 55% link out. The account's strongest tracked post pulled 15K interactions, about 29x its own typical post.

What is Keith Rabois's engagement rate on X?
Keith Rabois (@rabois) has an engagement rate of 0.105%, based on the median interactions across 11 original posts from the last 30 days against 488,630 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.105%, Keith Rabois sits above the 50th percentile of the 37,582 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 @rabois have real engagement?
Its engagement rate beats 69% of the tracked X accounts closest to it in follower count (3,882 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 @rabois post?
Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 1.73 posts per day across the measured window.

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