elvis engagement report
@omarsar0 - 318K followers on X
Measured over 19 original posts from a 30-day window, last computed on September 4, 2026.
Engagement
A typical post picks up 322 interactions against 318K followers, an engagement rate of 0.102%. Measured over 19 original posts, its engagement rate beats 62% of 6,874 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 29K times each, and 1.10% of those impressions turn into an interaction. That is about 9.15% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 19 posts sampled, 74% carry an image or video, 21% are part of a thread and 42% link out. The account's strongest tracked post pulled 4.1K interactions, about 13x its own typical post.
Measured over 19 original posts from a 30-day window, last computed on September 4, 2026.
Compared with accounts its own size
elvis's engagement rate beats 62% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 7 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 49% 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 0.102%, elvis sits above the 50th percentile of the 66,691 accounts in this comparison. That places it in the above the median band, which runs 0.1% to 0.499%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 118.6% |
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 15:00 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 | 74% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 42% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 74% 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.
- 42% 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 551 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 2, 202613x their median
Gemini 3.8 Flash on DeepSWE 1.1, scores 73.7%! https://t.co/JW7fhVy4He
- Sep 3, 20268.0x 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, 20267.7x 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. 🧵
- Sep 3, 20262.8x their median
The @bot team gave me 50 codes to hand out to my followers, each worth $200. That's a month free of the $200/month plan, or $200 in credits. Just comment with your best/most fun use cases or things you’d like to try in Grok Bot. Grok Bot changed how I work with agents. I stopped overthinking. I hand a task to a bot, and it manages most of the work from there. My favorite bot so far: My orchestrator bot runs my higher-level agent team. It assigns work across specialized bots and keeps everything organized, so I run many tasks in parallel instead of babysitting one at a time.
- Sep 3, 20262.3x their median
Banger paper from Google DeepMind and colleagues. (bookmark it) A model reads its entire KV cache on every generated token, even though it ends up attending to a tiny slice of it. In other words, if you ask about one detail from a 1M-token conversation the global attention layers re-read all of it, per token. The usual fix is to guess the relevant tokens first with cheap proxy scores, which still costs O(N) every step. Declarative Attention asks the model instead. The model declares where it needs to look, inside its own chain-of-thought. In this way, generation splits into three modes: global reads the full context, focus reads one specific region, and local reads only recent output. The inference engine parses those declarations the same way it parses tool calls and skips most of the cache read. On zero-shot on off-the-shelf weights across 15 long-context tasks, attended tokens during decoding drop 52.0% on Gemma-4-31B and 31.1% on Qwen-3.6-27B. Paper: https://t.co/caC2iKjXGD Chat with Paper: https://t.co/6TIllXY5nQ
- Sep 2, 20261.9x their median
Remarkable result for Exo, best price / performance. Exo's design philosophy is to expose the full harness code to the model for self improvement to be maximally bitter lesson aligned. Not just the prompt, but the entire running code and logs with ability to upgrade dynamically. https://t.co/jhLCbSZ1eY
- Sep 3, 20261.8x their median
Today we’re turning your Claude subscription into a full AI app builder. Connect Claude, ChatGPT or Cursor to Zite MCP and ask for an app. It goes live with its own database, logins for your whole team, roles for who sees what. Zero Zite credits. https://t.co/ubbGdbOX2h
- Sep 2, 20261.7x their median
Banger paper from ByteDance Seed. If you are curious about self-evolving agent harnesses, this one is worth your time. (bookmark it) The proposes method, HarnessDev, stops scoring a model on the tasks it completes and scores it on the harness it builds. The agent starts from a weak but runnable seed plus a handful of cases, then builds a full execution system. A second stage hands that harness back and asks it to improve on downstream feedback. Both stages are scored on capability and on execution-token cost, so there is awareness of efficiency and spend. The experiments include six creator LLMs, four domains, 2,207 held-out downstream instances. The result splits by domain. Generated harnesses stay well behind mature human-engineered references on code and on search and research, while matching or beating them on writing and machine-learning experimentation. Evolution produces gains, but they are unstable and transfer only partially to held-out tasks, and they depend heavily on which model runs the harness. Paper: https://t.co/nbXaLrXU3o Chat with Paper: https://t.co/ithbgySYDU
- Sep 3, 2026
We got 46% fewer errors than the single best LLM across the 16 most used benchmarks (TerminalBench, LiveCodeBench, etc). Here's how that's possible and what each model can achieve when used optimally (every benchmarks misses the majority of model capabilities) 👇 Interactive Site: https://t.co/6ZqljIhKBh Academic Paper: https://t.co/AX7QEMtEF5
- Sep 3, 2026
Massive paper from Meta. I like this one because it shows the use of agent harnesses for production-grade recommender systems. Details below: This is one of the more convincing agent deployments I've seen. It runs against a live production recommender serving billions of people and reports A/B results. Sustaining a recommender is continual optimization work. Content shifts, user behavior shifts, upstream models shift, and the choices governing retrieval, ranking and serving have to be revisited. Human engineers test those changes through online experiments, which is slow enough that parts of the system go unrevised. In CORAL, each cycle the agent observes operating signals, reasons over a memory of past decisions and their measured outcomes, and invokes tools including a numerical optimizer that keeps every change inside a fixed operating budget. The policy improves in context from its own prior actions, with no parameter updates. Across two large social platforms, the same harness improves engagement at no additional serving cost on one and reduces serving cost without degrading engagement on the other. Performance improves as the loop iterates. The guardrail design carries as much weight as the agent. A bounded change budget makes this safe to run against production. Paper: https://t.co/G46EgVuPMR Chat with Paper: https://t.co/KlYFT8dAFD
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.
Buy or sell X accounts - escrow-protected
PlayerSells is an escrow marketplace for X accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 322 interactions against 318K followers, an engagement rate of 0.102%. Measured over 19 original posts, its engagement rate beats 62% of 6,874 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 29K times each, and 1.10% of those impressions turn into an interaction. That is about 9.15% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 15:00 UTC, and Thursday is the busiest day of the week. Of the 19 posts sampled, 74% carry an image or video, 21% are part of a thread and 42% link out. The account's strongest tracked post pulled 4.1K interactions, about 13x its own typical post.
- What is elvis's engagement rate on X?
- elvis (@omarsar0) has an engagement rate of 0.102%, based on the median interactions across 19 original posts from the last 30 days against 318,405 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.102%, elvis sits above the 50th percentile of the 66,691 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 62% of the tracked X accounts closest to it in follower count (6,874 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 15:00 UTC, and Thursday is its busiest day, at roughly 1.97 posts per day across the measured window.