Noam Brown engagement report
@polynoamial - 182K followers on X
Measured over 2 original posts from a 30-day window, last computed on October 19, 2026.
Engagement
Early reading. We have captured 2 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 1.3K interactions against 182K followers, an engagement rate of 0.689%. Posts are seen about 182K times each, and 0.687% of those impressions turn into an interaction. That is about 99.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 12:00 UTC, and Sunday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 19K interactions, about 7.0x its own typical post. Only 2 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 2 original posts from a 30-day window, last computed on October 19, 2026.
Where this sits in the catalog
At 0.689%, Noam Brown sits above the 75th percentile of the 205,048 accounts in this comparison. That places it in the top 25% band, which runs 0.678% to 2.55%.
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 12:00 UTC, and Sunday 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 | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | -3% | -3% to -2% | 54K |
- 100% 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.
- 0% 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 161 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below 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
- Jun 18, 20267.0x their median
I’m excited to share that I’ll be joining OpenAI and look forward to working with the exceptional team there. It was a difficult decision to move on. I’m incredibly proud of the amazing team at Google and everything we’ve built together. It has been an honor and a pleasure to work with all of you.
- Jul 10, 20266.2x their median
Yesterday, we made GPT-5.6 Sol Ultra generally available. Today, we're sharing that it produced a proof of the 50-year-old Cycle Double Cover Conjecture using 64 subagents in just under one hour. We're sharing the prompt and proof below. We're excited to see what you all do with Ultra!
- Jun 9, 20263.2x their median
https://t.co/oWqzT12RtZ
- Jun 18, 2026
Together with researchers at Boston Children’s Hospital and Harvard, we published a study in NEJM AI showing how o3 Deep Research helped clinicians revisit previously unsolved rare pediatric disease cases, and find answers for families who had waited years. https://t.co/HVVDlEkuYR
- Jun 18, 2026
I am pleased and honored to announce that, on July 6, I'll be joining @OpenAI as leader of a new team called Strategic Futures. Our mandate will be to help the company's leadership shape frontier AI policy. There is a ton of work to do, and I'm excited to get started. https://t.co/hqoNbOEYbN
- Sep 8, 20262.4x their median
It is extremely sad that this didn't end up as an example of how the labs could cooperate/coordinate, because the stakes will be so much higher in the future.
- Sep 8, 20262.2x their median
Very sad to see Levent double down on the plagiarism accusation. I hope my friends at @AnthropicAI stand up to this internally. It should be clear by now what the truth is.
- Jul 14, 20262.0x their median
AI has helped resolve an important question in statistics. In the area of multiple hypothesis testing, the goal of controlling the false discovery rate (FDR) has been introduced in a seminal paper by Benjamini and Hochberg (1995). They also introduced a method (the Benjamini-Hochberg or BH method) and proved it controls the FDR. This method has been widely adopted in modern high-throughput science, including in genomics, astronomy, economics, etc. The paper has has garnered more than 130,000 citations to date. However Benjamini and Hochberg showed FDR control only when the data for the individual tests are *independent*. In practice, these data are often dependent; a good example is data on genetic variants due to linkage disequilibrium. Later work has focused on extending the validity of the BH procedure, e.g., to a form of positive dependence by Benjamini and Yekutieli (2001). The question of when the BH procedure controls the FDR has remained open. Over the last twenty years, many authors, including Reiner-Benaim (2007), Kim and van de Wiel (2008), Benjamini (2010), Sarkar (2023), Sarkar and Zhang (2025), have conjectured that the BH procedure controls the FDR for two-sided tests using any correlated Gaussian data. These authors have presented both theoretical and empirical evidence supporting, but not directly showing, the conjecture. With the help of AI (specifically GPT-5.6 Sol Pro), I have settled the question in the negative: The Benjamini-Hochberg procedure does *not* generally control the false discovery rate at the desired level for correlated two-sided Gaussian tests. This was done by exhibiting a Gaussian factor model for which, at a nominal level alpha=0.01, the false discovery rate is proved to be FDR>0.0104. There is a lot of interesting commentary to be made: 1. This result should be of interest to everybody in the field of statistics. Emmanuel Candes of Stanford University once called the false discovery rate and the Benjamini-Hochberg procedure "one of the two most important developments in statistics after 1950" (the other being James-Stein shrinkage). The present conjecture is probably the most central question about FDR/BH that was unresolved to date. 2. GPT-5.6 one-shot the problem after 90 minutes of reasoning, whereas with 5.5 I was not able to solve it even after iterating with multiple parallel agents for perhaps 20 hours. So the capability improvement is quite real. Exciting times to live in! 3. The argument is not especially surprising, but it does combine an asymptotic approach (standard for FDR analysis, see e.g., Genovese and Wasserman, Efron, etc) with a numerical certificate in a way that would be pretty non-standard in the field. Once we have the specific example, then straightforward simulations also support that the false discovery rate is indeed higher than the nominal value (see attached fig). 4. The current degree of violation over the nominal level is relatively small (0.104 vs 0.1). So the importance of this result is mainly conceptual. The practical implications remain to be determined. Overall, an exciting development! Preprint is available here (https://t.co/YgiwgDF2qr) and will be on arxiv tonight; supporting code is here (https://t.co/KZhj15qDXC).
- Jul 20, 20261.8x their median
Long-running models can solve hard open-ended problems, but their persistence can create safety risks that shorter-horizon evaluations miss. We’re sharing what we learned from studying a long-running model, and how those findings are shaping our approach to evaluations, alignment, monitoring, and user control. https://t.co/yePIzJGsAU
- Sep 17, 20261.7x their median
New episode with @polynoamial We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. 0:00:00 – Multi-agent and Navier-Stokes 0:15:28 – How will AI firms work? 0:22:02 – What math progress tells us about recursive self improvement 0:40:22 – Hugging Face and alignment 1:01:18 – The internal/external model gap 1:08:34 – Chain of thought is degrading 1:14:12 – How will we know when alignment is solved?
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 1.3K interactions against 182K followers, an engagement rate of 0.689%. Posts are seen about 182K times each, and 0.687% of those impressions turn into an interaction. That is about 99.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.3 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 12:00 UTC, and Sunday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video. The account's strongest tracked post pulled 19K interactions, about 7.0x its own typical post. Only 2 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 Noam Brown's engagement rate on X?
- Noam Brown (@polynoamial) has an engagement rate of 0.689%, based on the median interactions across 2 original posts from the last 30 days against 182,350 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.689%, Noam Brown sits above the 75th 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 @polynoamial have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @polynoamial post?
- Most posts go out around 12:00 UTC, and Sunday is its busiest day, at roughly 0.3 posts per day across the measured window.