Steven Sinofsky engagement report
@stevesi - 213K followers on X
Measured over 17 original posts from a 30-day window, last computed on October 5, 2026.
Engagement
A typical post picks up 112 interactions against 213K followers, an engagement rate of 0.053%. Measured over 17 original posts, its engagement rate beats 48% 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 13K times each, and 0.892% of those impressions turn into an interaction. That is about 5.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 14:00 UTC, and Tuesday is the busiest day of the week. Of the 17 posts sampled, 59% carry an image or video and 29% link out. The account's strongest tracked post pulled 846 interactions, about 7.6x its own typical post.
Measured over 17 original posts from a 30-day window, last computed on October 5, 2026.
Compared with accounts its own size
Steven Sinofsky's engagement rate beats 48% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 9 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 40% 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 0.053%, Steven Sinofsky sits above the 25th percentile of the 205,048 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 14:00 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 | 59% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 29% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 59% 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.
- 29% 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 474 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
- Sep 29, 20267.6x their median
Sigh. I wrote this post below, but then I didn't post it because I am tired of the consistent pushback I get from folks with the same safety worldview as Anthropic. I guess that means I should send it. Here goes! This post is pretty solipsistic and doesn't properly take recent events in cyber risks into it's discussion. It is really hard for me to watch how the US frontier labs like Anthropic don't have the ability to consider other approaches to safety and ways things could play out. It insinuates that Z ai (Chinese lab, builds GLM series) doesn't really care about safety and is reckless to take their business strategy. Saying things like "Given this evidence, we think it's likely both state and non-state actors will use models like GLM-5.3 to cause real-world harm." and "This is unlike any other similarly capable AI model, all of which were released with safeguards or through limited access programs." while closed models have been used on more of the documented cyber attacks is just bowing out of the interesting question. A plausible view is that open model weights and closed model apis (with some safe guards) are both far closer to being easy to mis-use, rather than API models being closer to safe. The trope "Open Dangerous, Closed Safe" may be closer to "Open Unsafe, Closed Unsafe" Closed models have stronger capabilities and stronger safeguards, but the stronger capabilities part could matter more in net harm if both the safeguards are porous. At the same time, as the authors do acknowledge (thanks - thats progress!), open models without extreme cyber guardrails are important to rapidly diffuse cyber readiness in the economy -- as programs like project glasswing are not perfect in getting all critical industry onboarded. There will be more issues like the time when Fable was released and Amazon found a workaround, which allowed them to access the full capabilities of the model served readily at an API. The blog overall is reasonable in it's narrow line, but it's a very effective tool in a complicated, rapidly evolving media ecosystem to reinforce a certain type of safety thinking.
- Sep 11, 20263.9x their median
Read this to understand Anthropic's latest barrage of lies https://t.co/7QxJn986cl
- Sep 11, 20262.7x their median
PIRATE WIRES PODCAST: WE’RE SO BACK Not a drill... the gang is back together for your favorite weekly pod. PW alum @metaversehell returns to the show with @micsolana and @rylzdigital where we talk: The 25th anniversary of 9/11 and how much the world has changed since then, the Lindsay Clancy case and brainrot phone addiction, an Anthropic researcher’s warning, a deep dive into H1-B visas, and... are we all living in a simulation?
- Aug 24, 20262.1x their median
Every day I'm reminded that I spent my youth in meetings with a stop watch arguing about how to shave fractions of a second off execution of programs. Then came dialup internet. Today the most used program is so slow they rebranded slow as "agentic" and everyone is thrilled!
- Sep 10, 20261.9x their median
"Most domains are fundamentally pretty shallow," says @RyanGreenblatt. A concise statement of what the superintelligence worldview gets wrong, IMO. My view is the opposite: many domains are surprisingly deep, and AI won't be able to "quickly pick up a bunch of domains." https://t.co/sRLetf6049
- Sep 10, 20261.9x their median
This episode teaches you a lot about the AI lunatics. None of them offered any substantial counterarguments. Most AI safety debates are basically one side presenting their beliefs and arguments, while the other resorts to mockery, ad hominem attacks, insults, and pure hate. They just *make up* some story that this must be an organized psyop with some hidden motive. They *lie* about Coxon having worked there for only six weeks. I personally responded to many of them with a source showing that this wasn't true. Not one of them corrected their mistake after their lie was caught; instead, they either ignored the correction or hurled another attack at me. These people do not care about arguments or the truth. They care about mockery, about destroying their enemy, about winning.
- Sep 10, 20261.7x their median
When elites try to convince you a new technology is a threat to YOU, it is often because it is a threat to THEM.
- Sep 30, 2026
Lots of power packed into a slim design on our new Fire TV Stick 4K. https://t.co/KdgU1EuhkI
- Sep 10, 2026
It seems we will now be entering a phase of AI congressional hearings and legislation. There are many no-regret ways to increase AI safety. Mandatory transparency. Audits. An NTSB-like investigatory safety agency that identifies root cause of incidents. Proactive ways to use AI and other tools to bolster digital and biological security. Incentives and anti-trust exceptions to encourage lateral sharing on safety and alignment techniques between labs. Liability law to align lab incentives even further with society at large. There are also policies that would impede the positive developments of AI, or which would give governments undo influence on AI models, or dangerous new powers of surveillance and censorship. I want to register again that loss of control is not the AI risk that worries me most. It's over-control that leads to less freedom and a more authoritarian system of government that I see as the most plausible large scale harm of AI to society. As we have these debates over the coming weeks and months, I urge everyone to ask of any policy: How would a government use this for ill? How could this be used to surveil, suppress, manipulate, or control a populace? We have no hard evidence that AI poses an existential threat to humanity. That doesn't mean we shouldn't be cautious and embrace no-regret policies. But in doing so, let's not make the obvious and most historically likely threat - further rollback of democratic freedoms - more likely. Thanks.
- Sep 30, 2026
So glad I parked the domain https://t.co/9qlVwqMrJE ages ago.
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 112 interactions against 213K followers, an engagement rate of 0.053%. Measured over 17 original posts, its engagement rate beats 48% 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 13K times each, and 0.892% of those impressions turn into an interaction. That is about 5.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 14:00 UTC, and Tuesday is the busiest day of the week. Of the 17 posts sampled, 59% carry an image or video and 29% link out. The account's strongest tracked post pulled 846 interactions, about 7.6x its own typical post.
- What is Steven Sinofsky's engagement rate on X?
- Steven Sinofsky (@stevesi) has an engagement rate of 0.053%, based on the median interactions across 17 original posts from the last 30 days against 213,053 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.053%, Steven Sinofsky sits above the 25th percentile of the 205,048 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 @stevesi have real engagement?
- Its engagement rate beats 48% 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 @stevesi post?
- Most posts go out around 14:00 UTC, and Tuesday is its busiest day, at roughly 4 posts per day across the measured window.