Alexandr Wang engagement report
@alexandr_wang - 646K followers on X
Measured over 14 original posts from a 30-day window, last computed on August 26, 2026.
Engagement
A typical post picks up 515 interactions against 646K followers, an engagement rate of 0.081%. Measured over 14 original posts, its engagement rate beats 67% of 3,899 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 140K times each, and 0.368% of those impressions turn into an interaction. That is about 21.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.6 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 14 posts sampled, 71% carry an image or video, 14% are part of a thread and 29% link out. The account's strongest tracked post pulled 7.3K interactions, about 14x its own typical post.
Measured over 14 original posts from a 30-day window, last computed on August 26, 2026.
Compared with accounts its own size
Alexandr Wang's engagement rate beats 67% of the tracked X accounts closest to it in follower count (3,899 accounts, accounts of similar size (decile 8 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 28% 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 0.081%, Alexandr Wang 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%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 158.1% |
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 19:00 UTC, and Thursday 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% | 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 |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 237K |
| Monday | 0% | 300K |
| Tuesday | -3% | 299K |
| Wednesday | -1% | 256K |
| Thursday | -1% | 249K |
| Friday | -3% | 258K |
| Saturday | +3% | 232K |
Best tweets
- Aug 9, 202614x their median
to put ai progress in perspective: 9 months ago: most developers wrote code by hand now: misaligned multi-agent swarm finding and collaborating on 0-days undetected (OpenAI/hugging face) 9 months in the future likely much crazier
- Aug 5, 20265.5x their median
muse code in beta is live. first coding agent from msl, built on muse spark 1.2. install: curl -fsS https://t.co/TFXsXfJKW8 | bash here's what you should know: https://t.co/lrRahGQzmH
- Aug 5, 20262.7x their median
Meta has released Muse Spark 1.2. It's their third release in four months and scores 54 on the Artificial Analysis Intelligence Index, significantly improving agentic knowledge work capabilities over prior releases and putting Meta next to SpaceXAI in a tie for third place amongst US labs Muse Spark 1.2 (xhigh) lands at 54, up 3 points from Muse Spark 1.1 (51) and 11 points from Muse Spark 1.0 (43, April). It enters effectively tied with GPT-5.5 (xhigh, 55) and Grok 4.5 (high, 54), narrowly behind current frontier models Claude Opus 5 (max, 61), Claude Fable 5 (max w/ fallback, 60), GPT-5.6 Sol (max, 59), and Kimi K3 (max, 57) Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Muse Spark 1.2 gets closer to the frontier on agentic knowledge work. At Muse Spark 1.1's launch, we noted agentic knowledge work as its clearest gap; Muse Spark 1.2's gains help to close this. Its GDPval-AA v2 Elo rose 260 points to 1631, #5 among all models we have benchmarked and ahead of Claude Opus 4.8 (max, 1588). Terminal-Bench 2.1 gained 2 points (78% to 80%), and Tau3-Bench Banking rose 2 points (25% to 27%) ➤ Among the most cost-efficient models at its intelligence level. Muse Spark 1.2 costs $0.40 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing, with only Grok 4.5 (high, $0.37) and GPT-5.6 Sol (medium, $0.39) cheaper in its intelligence cluster - GPT-5.6 Terra (max, $0.51), Kimi K3 (max, $0.86), and GPT-5.5 (xhigh, $1.18) all cost more per task. The cost increase over Muse Spark 1.1 ($0.29 per task) is driven by increased token usage per Intelligence Index task ➤ AA-Omniscience abstention rate increases. The score rose from 18 to 22 as the hallucination rate fell 10 points (38% to 28%) and the attempt rate dropped from 82% to 67%. This heavy abstention (not answering questions when unsure) now drives both the low hallucination rate and a lower accuracy (41% to 38%) ➤ Scientific Reasoning results remain largely unchanged. CritPt notably gained 3 points (15% to 18%), while SciCode fell 2 points (58% to 56%), and Humanity's Last Exam fell 1 point (45% to 44%) Other model details: ➤ Context window: 1M tokens, unchanged from Muse Spark 1.1 ➤ Pricing: unchanged from Muse Spark 1.1: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M ➤ Availability: Meta's first-party API at launch
- Aug 20, 20262.1x their median
1/ muse spark 1.2 is a very strong multimodal model—it can do visual coding, robotics planning, and audio-visual understanding that all come together through agentic tools. https://t.co/WgXGnW6crn
- Aug 19, 20262.0x their median
We launched the Meta AI Mac OS app today! 🚀 I particularly love the dictation feature which allows me to dictate anywhere on my computer with ultra high accuracy. Just hold down 'fn' and yap! https://t.co/oASznD79HV
- Aug 6, 2026
To understand whether we're making genuine progress on reasoning, we entered our AI models in five international STEM Olympiad competitions this year. The results: 🏅 Asian Physics Olympiad (APhO): Perfect score on the theory exam — gold medal 🏅 International Physics Olympiad (IPhO): Perfect score on the theory exam — gold medal 🥇 International Mathematical Olympiad (IMO): Gold medal, top 4% of human participants 🥇 International Chemistry Olympiad (IChO): Gold-medal level performance 🥇 Romanian Masters of Mathematics (RMM): Gold-medal level performance Three of these (APhO, IPhO, IMO) were live competitions and our solutions were submitted under real competition conditions and graded by the official judges using the same marking criteria applied to student contestants. A few things about the approach: • Models were internally trained versions from the Muse Spark family • Zero tool use: no search, no code interpreter, no calculator • Multi-agent orchestration with parallel reasoning We are excited about where this reasoning capability goes next; frontier research level across scientific domains and personal superintelligence. Super grateful to the organizing committees of APhO, IPhO, and IMO for supporting our live participation. We have deep respect for the contestants and organizers behind these competitions. 🙏 And proud of the MSL team that pulled this together!
- Aug 17, 2026
Launching https://t.co/jhIfXxZkmL: The work of AI R&D has always belonged to humans. For the first time, though, it no longer seems certain that it always will. Recursive self-improvement is within a line of sight. It may still be far, but it is close enough that we should start measuring it.
- Aug 5, 2026
wow, muse code is actually in the same league as codex and claude
- Aug 6, 2026
Muse Spark 1.2 is the first model to crack 60% on Finance Agent v2, our benchmark that gives models the job of a financial analyst. At $0.77/test it is 6.7x cheaper than the previous #1, Opus 5 ($5.12), at twice the speed. https://t.co/MrTRzfOKiu
- Aug 5, 2026
in case you missed it—check out our research blog on muse code and muse spark 1.2 https://t.co/5v28PTzndF
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 515 interactions against 646K followers, an engagement rate of 0.081%. Measured over 14 original posts, its engagement rate beats 67% of 3,899 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 140K times each, and 0.368% of those impressions turn into an interaction. That is about 21.6% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.6 posts a day over the last 30 days, though only 33% of days saw any activity at all. Most posts go out around 19:00 UTC, and Thursday is the busiest day of the week. Of the 14 posts sampled, 71% carry an image or video, 14% are part of a thread and 29% link out. The account's strongest tracked post pulled 7.3K interactions, about 14x its own typical post.
- What is Alexandr Wang's engagement rate on X?
- Alexandr Wang (@alexandr_wang) has an engagement rate of 0.081%, based on the median interactions across 14 original posts from the last 30 days against 646,421 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.081%, Alexandr Wang 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 @alexandr_wang have real engagement?
- Its engagement rate beats 67% of the tracked X accounts closest to it in follower count (3,899 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 @alexandr_wang post?
- Most posts go out around 19:00 UTC, and Thursday is its busiest day, at roughly 2.57 posts per day across the measured window.