Aran Komatsuzaki engagement report
@arankomatsuzaki - 198K followers on X
Measured over 18 original posts from a 30-day window, last computed on October 12, 2026.
Engagement
A typical post picks up 113 interactions against 198K followers, an engagement rate of 0.058%. Measured over 18 original posts, its engagement rate beats 49% 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 10K times each, and 1.09% of those impressions turn into an interaction. That is about 5.23% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 18 posts sampled, 89% carry an image or video, 33% are part of a thread and 56% link out. The account's strongest tracked post pulled 2.7K interactions.
Measured over 18 original posts from a 30-day window, last computed on October 12, 2026. Recurring tag: #neurips2026.
Compared with accounts its own size
Aran Komatsuzaki's engagement rate beats 49% 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 45% 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 0.058%, Aran Komatsuzaki sits above the 25th percentile of the 204,798 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 16:00 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% | 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 | 89% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 56% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 89% 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.
- 56% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 258 characters, which falls in the 180 - 280 characters band. Across the catalog, posts of 180 to 280 characters match the same accounts' other posts almost exactly.
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
- May 2, 2026
I spent 1.7B tokens on Codex (Pro 5x) and 80M tokens on Claude Code (Max 20x) yesterday. Guess which one gave me a usage-limit warning.
- Apr 28, 2026
The non-English tax is real. Sutton's Bitter Lesson, translated across languages and normalized to OpenAI English token count: Hindi: OpenAI 1.37×, Anthropic 3.24× Arabic: OpenAI 1.31×, Anthropic 2.86× Chinese: OpenAI 1.15×, Anthropic 1.71× Claude’s tokenizer charges a much higher linguistic tax.
- Jul 24, 202610x their median
Bubble Boi reveals the Kimi breakthrough that made him sell his entire portfolio overnight "Kimi is using a technique called linear attention that cuts down memory growth entirely to a fixed size, and they didn't just get okay results, they got world-class results. This same technique was used by the Qwen models but nobody thought it would scale. It's looking more and more likely that it scales pretty well, and if that's the case the memory per user is always fixed." "So if every frontier model goes to this, the demand for DRAM drops significantly. They already bought the memory, they just serve more and more users. It's like you had a car and someone found a way to make it 10 times faster, you're not going to buy a new car." "But you also have to readjust the assumption that hyperscalers are going to crazily spend for the next two years. What's to say there's not another innovation that shrinks this more and now you go from serving a thousand users to a hundred thousand users on one server rack? How do you justify spending 500k on a new rack at that point?"
- Apr 23, 2026
Anthropic just introduced forked subagents in their latest update. Unlike regular subagents, forked subagents can inherit the same context as the main agent. This looks convenient for cases where richer context matters more. This is just what I needed! https://t.co/Y4PXCJOcIc
- Apr 23, 2026
Google presents Vision Banana - SOTA unified model for both image understanding and generation. - Claims that image generation serves as a universal interface for diverse vision tasks https://t.co/6fYs5d2qge
- Sep 28, 20266.5x their median
Meta's team proposes DCE + SRCL as an alternative to on-policy self-distillation (OPSD). Achieved significant gains: 30.76% avg acc -> 65.97% on Qwen3-8B https://t.co/nAIHRZWNDx https://t.co/u2hHD6QmpU
- Sep 30, 20266.2x their median
Meta presents Context Language Models CLM treats context as an editable file rather than an append-only conversation. +65% score with the same compute on a 24-hour multi-repository agent-swarm task https://t.co/9NkkThLmk0
- Jul 8, 20266.2x their median
How can we extract richer signals from AI Feedback? Introducing LLM-as-a-Verifier✨— a simple verification scaling framework that achieves SOTA on agentic benchmarks 🚀 The key idea: - Use fine-grained scoring granularity (e.g., 1-20 instead of the standard 1-5 scale) - Take the expectation over the full logprob distribution of score tokens - Scale repeated evaluation and criteria decomposition You can use these fine-grained signals for more effective test-time scaling, RL, and agent monitoring! It achieves SOTA across Terminal-Bench V2, SWE-Bench Verified, RoboRewardBench, and MedAgentBench 👑 Advised by @Azaliamirh @istoica05 @drmapavone @chelseabfinn 🧵👇
- Oct 1, 20265.9x their median
Looped Diffusion Transformer - Scales computation by repeatedly running shared Transformer blocks within each denoising step - Surpasses a model 6.5× larger across T2I benchmarks while requiring 4.9× lower inference compute https://t.co/hw1EA2K6Rk https://t.co/dwYJvqHymL
- Oct 2, 20265.6x their median
Apple presents LoopCD: - Enables halving the number of recurrent loops while still matching or exceeding full-depth baselines - AIME 2024 pass@1 from 61.88% to 73.33% https://t.co/rUc748WtYj https://t.co/UespG5vQru
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 113 interactions against 198K followers, an engagement rate of 0.058%. Measured over 18 original posts, its engagement rate beats 49% 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 10K times each, and 1.09% of those impressions turn into an interaction. That is about 5.23% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.5 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 18 posts sampled, 89% carry an image or video, 33% are part of a thread and 56% link out. The account's strongest tracked post pulled 2.7K interactions.
- What is Aran Komatsuzaki's engagement rate on X?
- Aran Komatsuzaki (@arankomatsuzaki) has an engagement rate of 0.058%, based on the median interactions across 18 original posts from the last 30 days against 197,824 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.058%, Aran Komatsuzaki sits above the 25th percentile of the 204,798 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 @arankomatsuzaki have real engagement?
- Its engagement rate beats 49% 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 @arankomatsuzaki post?
- Most posts go out around 16:00 UTC, and Thursday is its busiest day, at roughly 1.5 posts per day across the measured window.