elvis engagement report
@omarsar0 - 326K followers on X
Measured over 22 original posts from a 30-day window, last computed on October 14, 2026.
Engagement
A typical post picks up 388 interactions against 326K followers, an engagement rate of 0.12%. Measured over 22 original posts, its engagement rate beats 63% 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 18K times each, and 2.12% of those impressions turn into an interaction. That is about 5.61% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.4 posts a day over the last 30 days, though only 40% 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 22 posts sampled, 82% carry an image or video, 9% are part of a thread and 82% link out. The account's strongest tracked post pulled 12K interactions, about 31x its own typical post.
Measured over 22 original posts from a 30-day window, last computed on October 14, 2026.
Compared with accounts its own size
elvis's engagement rate beats 63% 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 64% 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 0.12%, elvis sits above the 25th percentile of the 205,261 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 | 82% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 82% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 82% 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.
- 82% 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 745 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 16, 202631x their median
Nearly half a year of silence. We spent it studying one problem: how far RL can scale. MiMo-V2.6 is in the middle of its RL run right now. Three things we scaled: compute (~2B tokens per step, 1568 prompts × 16 rollouts, fully async), environments and harnesses (multi-task agentic RL, mixed across multiple harnesses in one run), and grader compute (agentic in-group credit assignment, with test-case and rubric-based rewards). We'll open-source the details piece by piece over the coming weeks. Streaming the run: https://t.co/ZSxahzJRju
- Sep 15, 202611x their median
I was not expecting things to go this way, but I think MCPs are better than CLIs for most integrations. The models have gotten much better at tool calling, we can defer tools & MCP is now stateless. If you need to compose/filter data, add params like query to your MCP tools.
- Sep 2, 202611x their median
Gemini 3.8 Flash on DeepSWE 1.1, scores 73.7%! https://t.co/JW7fhVy4He
- Sep 11, 20269.8x their median
OK. It's time to rethink this. I've spend the last several weeks working on a harness that tightly constrains the agents to work the way that I want them to work. I set up all kinds of gates, and tests, and tools, and protocols, and ... And while I was heads-down getting that to work, the agents got a LOT better. So much so that when I came up for air, the need for my harness was obviated. Indeed, the need for _any_ but the most liberal of harnesses may be obviated. Just how good these things have gotten blows me away. I have had long debates with grok and codex about the structure of systems -- as if they were senior engineers. They often disagree with me and have their own perspectives. I have, more than once, found myself agreeing with their views. I have not given up on constraints and tooling. Unit testing is still important. So is CRAP and Mutation testing. These tools still find bugs and offer useful constraints, though they can leave scars. However, the agents have gotten so good that I can now give one a very significant task with a few guidelines and it will faithfully implement it. I can walk away for 40 minutes and when I return it will be done. CRAP will be satisfied, Coverage will be high, and Mutation testing complete. The architecture will be clean, and the code will be very good. The end result may not behave perfectly, but it's so close that a couple of tweaks usually puts it into place. What does this mean going forward? I'm not sure. But I'm beginning to think that harnesses should not treat agents as components within a software design.
- Sep 14, 20268.8x their median
Since I stopped using my harness, my token consumption has fallen by a huge factor. That harness was massively inefficient.
- Sep 3, 20267.7x their median
wrote down some of the design thinking behind Grok Bot. persistent roles, clear state, scoped context, coordinated teams — an interface designed to move you from operating AI to delegating work. https://t.co/37On6hzsyl https://t.co/SDDYqjVhmi
- Sep 2, 20266.6x their median
A year ago the question was which model. Now it's which harness. Pi, Exo, Claude Code, Codex, DeepSeek Harness and 4 others. Same model, same tasks, same runtime. 360 runs, 2 billion tokens. Pass rates: 50% to 67%. Cost per pass: $1.05 to $18.34. Introducing FrontierHarness Eval. 🧵
- Oct 13, 20266.0x their median
introducing XERJ Corpus Hub, reference memory for AI agents. give your agent a corpus for your task. watch it become superintelligent. > finds the answers in indexed expert knowledge > applies them to your code, research, and projects > works with any model. free and OSS forever/Apache 2 install in one prompt: >Install XERJ (docs: https://t.co/IuphAL9cVj), index this project’s sources, docs, and logs, and connect https://t.co/BanCJJul0x as reference memory hub
- Sep 12, 20265.3x their median
David Friedberg reveals frontier labs are taking novel insights from his chat history and packaging them as their own "I have had experiences where we've asked some fairly novel scientific questions, and it identifies it as a novel insight, 'oh, never thought about that, might be interesting,' blah blah blah..." "And the using a different account, asking the next model version, it's like, 'oh, you could do this'. And it actually just describes this exact thing that we had in our chat in the previous version." "These are a handful of anecdotal experiences. But I know the domain that we work in, and the novelty of this stuff. So I know that there isn't some new corpus of information out there that's training the new model." "So all I can say at that point is that my conversation or our analyses have been used for training. The truth is, it's actually a piece of IP, that's our organizational IP. We don't have any NDA or confidentiality protections, with them being a service provider back to us." "This is why I care a lot about open source, because I don't want them having my chat logs, because they can use them for training to create an IP that is now diffused to the rest of the market. I find this very unfair."
- Sep 15, 20264.3x their median
I hate to say it, but if you’re running more than 2 sub agents at time, you’re almost certainly burning tokens for 0 quality gain. Agents don’t trust each other enough to avoid double-checking everyone’s homework.
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 388 interactions against 326K followers, an engagement rate of 0.12%. Measured over 22 original posts, its engagement rate beats 63% 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 18K times each, and 2.12% of those impressions turn into an interaction. That is about 5.61% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.4 posts a day over the last 30 days, though only 40% 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 22 posts sampled, 82% carry an image or video, 9% are part of a thread and 82% link out. The account's strongest tracked post pulled 12K interactions, about 31x its own typical post.
- What is elvis's engagement rate on X?
- elvis (@omarsar0) has an engagement rate of 0.12%, based on the median interactions across 22 original posts from the last 30 days against 326,048 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.12%, elvis sits above the 25th 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 @omarsar0 have real engagement?
- Its engagement rate beats 63% 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 @omarsar0 post?
- Most posts go out around 14:00 UTC, and Tuesday is its busiest day, at roughly 3.4 posts per day across the measured window.