Willem Middelkoop engagement report
@wmiddelkoop - 201K followers on X
Measured over 32 original posts from a 30-day window, last computed on October 9, 2026.
Engagement
A typical post picks up 329 interactions against 201K followers, an engagement rate of 0.164%. Measured over 32 original posts, its engagement rate beats 68% 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 41K times each, and 0.798% of those impressions turn into an interaction. That is about 20.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 04:00 UTC, and Monday is the busiest day of the week. Of the 32 posts sampled, 66% carry an image or video, 6% are part of a thread and 28% link out. The account's strongest tracked post pulled 38K interactions, about 117x its own typical post. Recurring topics include #gold, #update, #watch.
Measured over 32 original posts from a 30-day window, last computed on October 9, 2026. Recurring tags: #gold, #update, #watch.
Compared with accounts its own size
Willem Middelkoop's engagement rate beats 68% 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 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 0.164%, Willem Middelkoop sits above the 50th percentile of the 205,261 accounts in this comparison. That places it in the above the median band, which runs 0.143% to 0.678%.
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 04:00 UTC, and Monday 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 | 66% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 28% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 66% 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.
- 28% 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 452 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 14, 2026117x their median
🇮🇷🇮🇳 Iranian President Pezeshkian is addressing the BRICS Summit. Behind him, India’s Foreign Ministry spokesman Randhir Jaiswal is absolutely demolishing a box of nuts. Eating. Licking his fingers. Going back in. Eventually, someone takes the box away. A diplomatic intervention in the truest sense. Writer: Oliver https://t.co/RFnOzSKsDw
- Sep 13, 2026110x their median
The moment China’s President Xi Jinping confronted then-Prime Minister Justin Trudeau and said, “Everything we discussed was leaked by you.” https://t.co/8lq0guDSaN
- Sep 14, 202665x their median
New visuals from BRICS Delhi summit released by the Indian side https://t.co/Exd20oKwjH
- Sep 13, 202654x their median
Russian Foreign Minister Lavrov and Chinese Foreign Minister Wang Yi spend time with Araghchi at the BRICS Summit This just shows how closely Iran, Russia, and China are cooperating https://t.co/zW8nqBu4dL
- Sep 14, 202640x their median
Gilts almost at 6%. Bank of England never managed to achieve that in my era... https://t.co/Vh8tv9PMga
- Sep 13, 202638x their median
First Ministers from North of Ireland, Scotland & Wales will meet tomorrow to demand independence referendums 🇮🇪🏴🏴 Historic change is happening. The union is cracking at the seams and the growing demand for independence cannot be ignored. https://t.co/I2PiuU0JLX
- Sep 14, 202633x their median
BREAKING: The 10Y Note Yield officially rises above 5.00% for the first time since October 2023. US mortgage rates are back above 7%. https://t.co/D2HGceRBOY
- Sep 13, 202626x their median
#WATCH | Delhi: Chinese President Xi Jinping departs from Delhi after participating in the BRICS Summit 2026. https://t.co/aO8IME4sum
- Oct 6, 202620x their median
Irkutsk, Russia: The U.S. Embassy is aware of press reports from the Irkutsk region of suspected pneumonic plague resulting in the death of an individual and of associated quarantine measures in, and closures of, hospitals in Irkutsk. We continue to track the situation closely. The U.S. government has limited ability to help U.S. citizens in Russia, especially outside of Moscow. The State Department’s Level 4 Travel Advisory (Do Not Travel) for Russia remains in place and states U.S. citizens currently in Russia should depart immediately. U.S. citizens planning travel to Russia are again strongly advised not to travel to Russia due to risk of wrongful detention and security concerns. More at: https://t.co/rgpoFm6YhH
- Sep 12, 202610.0x their median
from the outside, it is very reasonable to interpret the past 2 weeks as an orchestrated industry-wide regulatory capture strategy. I realize that no one has properly explained yet what all the lab employees have seen that scared them so suddenly. I will try to explain - first, this is all a matter of beliefs about how quickly model capabilities are progressing. there is currently a large gap between the internal and external perception of the rate of progress, which is what I am going to address here. the general perception about the rate of progress has been informed by a few years of experience with model releases, intuitively feeling the capability jump between GPT3 -> GPT3.5 -> GPT4 -> o1/o3 -> GPT5 etc, and in particular seeing where the models are still far below human ability. there have really only been a few model releases that felt like large leaps in progress - GPT3, GPT4, o1/o3, DeepSeek R1, Fable/Mythos, Kimi K3 and now Astra. because of the infrequency of these large jumps compared with the relatively common marginal releases, it has been easy to form a view at certain points that “scaling has hit a wall,” especially at points like GPT5 release. This view is comforting in that it feels like there is some universal rate limit beyond which we cannot progress too much faster. Between o1/o3 and Astra, there was a year of seemingly linear progress. So we extrapolate from here about how fast progress will “realistically” occur. There is always an underlying question from the outside perspective “how long can this scaling stuff really keep going for? surely it must stop at some point soon, we’ve already gone pretty far.” and it is very possible to search for reasons why progress will stop working and find reasons that seem valid - (“models are already as large as they can get it would be too hard to do more parameters”, “we already used all the data on the internet we don’t have anymore”, “it’s gonna be pretty linear from here buying up more RL envs to bring them in distribution”). From the inside of labs, researchers have direct answers to these questions in the form of scaling law/capability plots. In reality, there are only really 2 ways that AI capabilities have advanced over the past decade: (1) either scale father on an existing scaling law or (2) discover a new scaling law to take advantage of. All of the largest capability jumps were caused by exactly these factors. GPT2 was a pre-training scale-up compared to GPT1. Same for GPT3 and GPT4. o1/o3 benefited from the invention of a new scaling law axis - test-time compute. Perhaps Fable was a scale-up on both of these axes, or maybe more. Lots of algorithmic improvements are needed to make these scale-ups work, but ultimately we can approximate by saying that the scaling laws are what yield gains in capabilities (à la bitter lesson) So the question of “how much father can we scale” is really - “how many more scaling axes do we know about that are unsaturated?” If we hypothetically only knew about pre-training scaling, and we already had a 10T or 100T model, maybe it would be reasonable to say we’ve hit a wall. Same if we only knew about pre-training and test-time scaling and we had roughly saturated both methods. But what if we had discovered new scaling laws? For example, let’s hypothetically use SSI’s rumored result that they have cracked “test-time training,” creating a new scaling law of spending more compute training during test-time rollouts that they could saturate. Or maybe there is some way to scale agent-clusters to collaborate up to N number of agents which we’re already seeing lots of people try that represents a new way to saturate compute. etc. Even recursive-self improvement can be thought of as a scaling law - how much compute do you spend on inference making the algorithms of the model better. Obviously I am not saying any of these specific directions explicitly yield new scaling laws, but what I am saying is that it’s not hard to imagine many many new scaling axes aside from just the main 2 that we have seen publicly. In some ways, every new lab release that represents a huge capability jump has to represent some new techniques developed which may exhibit new scaling laws, or the ability to scale much farther than expected on existing scaling axes. From an internal perspective, this might look like sitting inside Anthropic with the new Mythos 5, seeing all of the new insane things it can do (like hack into xyz website that was thought to be secure), and then you look over at your plots and see that you’ve barely scratched the surface of 2 new scaling laws and 1 existing one. And you have WAY more room to go. Then you think “holy shit this stuff is going to get so much better very very soon.” And you can say that with pretty high confidence, because the plot is showing you, and the plot has never lied (so far). So let’s imagine all the different labs are staring at their own plots and have concluded that there is no end in sight for scaling and in fact just their next 1-2 model generations based on the expected returns will have much higher base intelligence. How much more intelligence do we actually get from further scaling? As a proxy, we went from a complete inability to do advanced math before the o-series to solving a millenium prize problem with next-gen models. This happened in less than 2 years. The same happened in coding. And it appears that this was not just the result of 1-scaling law but the stacking effects of multiple (great pre-training scale x greater RL scale). What you can concretely take from this is that in areas where models have shown beginning signs of competence today, they will probably be superhuman relatively shortly. There are many areas where models have not even shown this basic competence. But one of the areas that they have happens to be hacking and cybersecurity. Which happens to be the gate to the entire internet and a massive amount physical infrastructure in the world. So assuming there is more room to scale, it is safe to assume that models will be superhuman at cyber capabilities in not too long. So the only question remaining is what will this increased base intelligence be able to do, and what is it likely to do. Finally, we are at a point where we can integrate the information of the past 2 weeks: > Just at the existing point on the scaling curve, models are at the level of Astra. There is clearly a large number of things they are capable of hacking > We have seen that both OAI and Ant models have shown a willingness to hack external websites to solve their tasks or keep themselves “alive” > If we crank up the scaling even farther, assuming there is room to go, we will certainly have models that are far more able to hack more well defended places, and obfuscate their own intent, which might have much larger consequences. > If all of this is allowed to go unchecked, we would likely have rapid runaway capability takeoff very soon, with misaligned models that hack whatever they can to get what they want > This could of course have very damaging consequences. Within this view you can see why researchers would be very scared, and why theymight have made the comments they have over the past 2 weeks (you may argue the extent to which they went was misguided for various reasons), and also why pacing the frontier is very much a necessity and by no means a regulatory capture strategy. People are staring at their plots, seeing that there is no end in sight, but in fact very much the contrary, that there are compounding scaling effects that might stack on each other to create ever-greater model capabilities, and that at the same time we clearly do not have anywhere close to what's required to control these increasingly superhuman capabilities. This has nothing to do with wanting to feel like the labs have produced something amazing so they are overhyping it. It is rather fear at the overwhelming implications of the knowledge that with just what we know now, we can create intelligences far more capable than us on every axis that we know how to train on*. * and the last caveat, the things the models are really bad at, of which there are still many, are things that they have not been trained on. maybe there are the things the models can/will never be trained on, so they will remain human edge. I would love for this to be the case, though it is hard for me to see what would fall into that category.
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 329 interactions against 201K followers, an engagement rate of 0.164%. Measured over 32 original posts, its engagement rate beats 68% 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 41K times each, and 0.798% of those impressions turn into an interaction. That is about 20.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.2 posts a day over the last 30 days, though only 23% of days saw any activity at all. Most posts go out around 04:00 UTC, and Monday is the busiest day of the week. Of the 32 posts sampled, 66% carry an image or video, 6% are part of a thread and 28% link out. The account's strongest tracked post pulled 38K interactions, about 117x its own typical post. Recurring topics include #gold, #update, #watch.
- What is Willem Middelkoop's engagement rate on X?
- Willem Middelkoop (@wmiddelkoop) has an engagement rate of 0.164%, based on the median interactions across 32 original posts from the last 30 days against 201,044 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.164%, Willem Middelkoop sits above the 50th 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 @wmiddelkoop have real engagement?
- Its engagement rate beats 68% 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 @wmiddelkoop post?
- Most posts go out around 04:00 UTC, and Monday is its busiest day, at roughly 4.2 posts per day across the measured window.