Tyler Winklevoss engagement report
@tyler - 1.1M followers on X
Measured over 38 original posts from a 30-day window, last computed on October 24, 2026.
Engagement
A typical post picks up 260 interactions against 1.1M followers, an engagement rate of 0.024%. Measured over 38 original posts, its engagement rate beats 47% of 20,444 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 40K times each, and 0.645% of those impressions turn into an interaction. That is about 3.64% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.3 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 38 posts sampled, 82% carry an image or video and 8% link out. The account's strongest tracked post pulled 18K interactions, about 69x its own typical post.
Measured over 38 original posts from a 30-day window, last computed on October 24, 2026.
Compared with accounts its own size
Tyler Winklevoss's engagement rate beats 47% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 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 37% 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 0.024%, Tyler Winklevoss sits above the 10th percentile of the 204,694 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
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 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.
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 | 82% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 8% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 82% 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.
- 8% 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 307 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
- Aug 24, 202669x their median
Windows 95 was launched this day 31 years ago https://t.co/KoPrnQpLSe
- Sep 15, 202646x their median
Not a single Democrat voted for Clarity. There's a lesson in that.
- Jan 6, 202672x their median
When Emily Wilson published her translation of The Odyssey, it quietly but decisively shifted how many readers understood one of the foundations of Western literature. For centuries, English versions of the poem had been shaped by male translators who often filtered Homer’s Greek through Victorian, Edwardian, or mid-20th Century assumptions about gender, class, and morality. Wilson approached the text with a different aim: fidelity not to tradition, but to the language itself. Her translation pays close attention to what the Greek actually says, rather than what earlier translators assumed it meant. Where previous versions softened Odysseus into a noble hero or exaggerated the moral failings of female characters, Wilson strips away editorial judgment. Words that had long been rendered with moralizing or misogynistic overtones—especially when applied to women, servants, or the enslaved—are reexamined and translated with consistency and precision. A term describing women as sexually suspect, for example, is no longer quietly upgraded to “faithful” or “pure” when it suits male sympathy. Equally important is what Wilson avoids. She resists anachronistic language that romanticizes violence, hierarchy, or domination. Her Odysseus is clever and ruthless, not automatically admirable; Penelope is intelligent and strategic, not merely patient and chaste. Enslaved women are named as enslaved, not euphemized into “maids,” forcing modern readers to confront the social realities the poem assumes rather than smoothing them away for comfort. Wilson’s choices don’t modernize Homer—they clarify him. By refusing to insert gendered judgment or inherited bias, her translation reveals how much earlier versions reflected the values of their translators rather than the ancient text. The result is an Odyssey that feels sharper, more unsettling, and more honest: a poem about power, survival, and storytelling itself, finally allowed to speak without centuries of moral varnish. © Reddit #archaeohistories
- Sep 15, 202634x their median
Being mean makes one old and ugly.
- Sep 24, 202633x their median
Former Anthropic researcher Jacob Coxon became a media superstar after going public with his AI fears. He insisted that he wasn’t working with any third party organizations. Familiar sources told us a different story: DEY., a PR firm representing many of the most prominent AI safetyists, was booking his interviews. One source, who had direct knowledge, even said DEY. preemptively booked Nate Soares, a prominent AI safety figure, for interviews that directly overlapped with Jacob going public. Jacob working with DEY. is notable for two reasons: first, as mentioned, he previously said he wasn’t working with third parties. Second, we are in the middle of a national conversation about the future of AI that is actively determining how we regulate the most powerful technology in the world, largely thanks to the panic stirred up by Jacob — and it’s in the public’s interest to know who, exactly, is behind it. Scoop from @huntryerson 👇
- Jan 2, 202644x their median
On this day, 63 years ago, Federico Fellini's "8½" (1963) premiered in Acapulco film festival, Mexico. https://t.co/wdwqQGl9AT
- Oct 16, 202627x their median
F1 engine sounds. https://t.co/6RDSxDTDRN
- Aug 25, 202625x their median
Steve Jobs literally predicted the iPhone, the Internet, AI and the next 50 years of technology in a single speech from 1983: https://t.co/qSfPW3FBUp
- Sep 4, 202620x their median
A dream I once had https://t.co/InP0OvTct6
- Sep 23, 202619x their median
Researchers built an AI that doxes any "anonymous" reddit account in under a minutes for $2. eth zurich and anthropic published a terrifying paper proving that "practical anonymity" on the internet is officially dead. they built a fully autonomous ai pipeline that takes your pseudonymous posts, extracts your identity signals, searches the web, and figures out exactly who you are. no human investigator needed. the numbers are actually mindblowing.. - 67% of hacker news users identified correctly - when the system makes a guess, it is right 90% of the time - it even unmasked scientists whose interview transcripts were explicitly redacted for privacy the scariest part? even time doesn't protect you.. they tested users who took a full year break and changed their interests. the ai still matched their old and new profiles with 90% precision. it sees through your persona changes like they aren't even there. there is no defense against this. the agent splits the work into tiny, benign tasks like "summarizing a profile" or "ranking candidates." no api safety guardrail is going to flag it because no single step looks malicious.. every throwaway account. every "nobody will connect this to me" comment. it’s all just searchable micro-data now.
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 260 interactions against 1.1M followers, an engagement rate of 0.024%. Measured over 38 original posts, its engagement rate beats 47% of 20,444 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 40K times each, and 0.645% of those impressions turn into an interaction. That is about 3.64% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.3 posts a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 15:00 UTC, and Tuesday is the busiest day of the week. Of the 38 posts sampled, 82% carry an image or video and 8% link out. The account's strongest tracked post pulled 18K interactions, about 69x its own typical post.
- What is Tyler Winklevoss's engagement rate on X?
- Tyler Winklevoss (@tyler) has an engagement rate of 0.024%, based on the median interactions across 38 original posts from the last 30 days against 1,107,058 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.024%, Tyler Winklevoss sits above the 10th percentile of the 204,694 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 @tyler have real engagement?
- Its engagement rate beats 47% of the tracked X accounts closest to it in follower count (20,444 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 @tyler post?
- Most posts go out around 15:00 UTC, and Tuesday is its busiest day, at roughly 3.3 posts per day across the measured window.