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Charly Wargnier ♨️ engagement report

@DataChaz - 183K followers on X

Measured over 18 original posts from a 30-day window, last computed on October 16, 2026.

Engagement

Middle of its size range
Per follower
0.039%
of 183K followers
Per impression
0.569%
13K views on a typical post
Reach
6.89%
of its followers see a post
Typical post
72
interactions (median)
Saved
0.187%
24 bookmarks on a typical post
Posting rate
3.07/day
active 20% of days
Peak time
08:00 UTC
Thursday

A typical post picks up 72 interactions against 183K followers, an engagement rate of 0.039%. Measured over 18 original posts, its engagement rate beats 43% 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 13K times each, and 0.569% of those impressions turn into an interaction. That is about 6.88% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.1 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 08:00 UTC, and Thursday is the busiest day of the week. Of the 18 posts sampled, 100% carry an image or video, 33% are part of a thread and 17% link out. The account's strongest tracked post pulled 25K interactions, about 346x its own typical post.

Measured over 18 original posts from a 30-day window, last computed on October 16, 2026.

Compared with accounts its own size

Charly Wargnier ♨️'s engagement rate beats 43% 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 29% 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 0.039%, Charly Wargnier ♨️ sits above the 25th percentile of the 205,048 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.678%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 205,048 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,894 times apart and a linear axis would flatten everything below the median into a single point.
Show the percentile table
Engagement rate percentiles
PercentileEngagement rate
10th percentile0.004%
25th percentile0.024%
50th percentile0.143%
75th percentile0.678%
90th percentile2.55%
99th percentile82.4%

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 08:00 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.

Engagement by hour posted, UTCTwenty-four bars, one per UTC hour. Each bar shows how posts published in that hour compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest hour: 08:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 08:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
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 UTC0%116K
22:00 UTC-2%100K
23:00 UTC-1%90K
Engagement by day of weekSeven bars, one per weekday, Sunday first. Each bar shows how posts published on that day compare with their own authors' median engagement. Bars above the centre line ran higher than the median, bars below ran lower. A marker flags Busiest day: Thursday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Thursday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%393K
Monday+1%483K
Tuesday-2%520K
Wednesday-3%472K
Thursday-2%430K
Friday-3%447K
Saturday+2%393K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

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.

This account's posting mix compared with catalog-wide effects
FormatThis accountCatalog effect95% intervalAccounts behind it
Image or video100% of posts+111%+108% to +115%34K
Outbound link17% of posts-41%-42% to -40%32K
Typical length-+15%+14% to +16%32K
  • 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.
  • 17% 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 547 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 15, 2026346x their median

    I'm never ever leaving LinkedIn 🤣 https://t.co/9FUkLX2Zkw

    24K9312281161.9M viewsView on X
  • Oct 15, 2026116x their median

    Linus Torvalds says, rules prohibiting AI usage are “UTTERLY STUPID” and “idiotic”. For the Linux kernel, Torvalds says, “we’re not going to make stupid rules that say ‘you can’t use tools for X’”. https://t.co/CGfqABRVyg

    7.2K779157234443K viewsView on X
  • Oct 15, 202637x their median

    this guy created Linux without Opus 5.5 https://t.co/ekMcjVPerj

    2.4K103752037K viewsView on X
  • Oct 14, 202621x their median

    Introducing Expand Fetch, a new way to scrape and crawl the web. Your agent has never seen the web. No wonder it gets basic questions wrong. Traditional Markdown APIs lose critical details. So we trained two System one models to change that: Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts. X-Ray, a JSON model that uncovers the hidden data behind every page. Every fetch also comes with a Wayback Machine. Every line links back to its source, so you can verify exactly what your agent saw. Three products. One API. On our benchmark of 1,094 questions across 326 challenging web pages, agents using Expand got 74% right. The next best API got 55%. Available today at $1 per 1,000 fetches. Try it at https://t.co/x4SDYsUX25

    1.2K16716449412K viewsView on X
  • Aug 25, 202615x their median

    true story https://t.co/tdwfA0WJ8T

    9221173118146K viewsView on X
  • Sep 16, 202614x their median

    Introducing iHermes A personal AI assistant powered by Hermes Agent, with GBrain for memory. You talk to it in iMessage. We're huge fans of Hermes. We've been using it to build internal systems and automate work, and we wanted more people to get that kind of value without having to set up the whole thing themselves. Start with one text. Connect the apps you use, hand off a task, and let it work through the steps. It keeps useful context from your earlier work and follows up proactively. Our goal is to make iHermes the most transparent and secure personal assistant in iMessage, built around the person using it. Your assistant should work for you.

    817399725163K viewsView on X
  • Oct 13, 20263.6x their median

    We launched 1M+ cloud agents. Now we’re sharing how you can build them for your company. We spent a year battle-testing flexible components for diverse use cases and security needs. Unreal Agent is one of those components. Today, we’re discussing the broader stack 🧵: https://t.co/Tt5kigdl3Q

    19315361587K viewsView on X
  • Oct 14, 20263.2x their median

    This fully autonomous demo shows a Bagel Labs foundation model controlling a robot to make coffee. We previously released WorldDiT, our lightweight architecture for robot foundation models. At the time of release, it was on the Pareto frontier for model size and performance. We have since been refining this architecture to handle longer horizon, more useful tasks fully autonomously. Making coffee means getting a whole sequence of actions right. Each step depends on the result of the last, so small mistakes can build up as the task continues. Getting this to work with a small model is especially challenging. This demo is an example of what the model can do today. If you are deploying robots or have existing hardware you want to make autonomous, we would like to partner with you. https://t.co/VYPldhBA2e

    12746471160K viewsView on X
  • Sep 15, 20262.3x their median

    Introducing Astra for Customer Retention & Growth. Just describe your goal. NoimosAI autonomously creates and delivers personalized campaigns that keep customers engaged and coming back. Maximize customer lifetime value. All on a single platform. https://t.co/zJiI3hY5UY

    11018231557K viewsView on X
  • Oct 15, 20262.3x their median

    Coding agents are entering the physical world. Zero-shot is only the starting point. What if we gave them practice—and let the next agent inherit what they learned? EmbodiedEvo: one Playground → reusable Skills & Lessons → unseen layouts. No model-weight updates. 45.08 average score. 38.95% success. #1 on the RoboDojo leaderboard. Project: https://t.co/oIQmpkqcel Paper: https://t.co/2YgblnSKoI Github:https://t.co/wh2aO43CT3

    74252241322K viewsView on X

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 72 interactions against 183K followers, an engagement rate of 0.039%. Measured over 18 original posts, its engagement rate beats 43% 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 13K times each, and 0.569% of those impressions turn into an interaction. That is about 6.88% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.1 posts a day over the last 30 days, though only 20% of days saw any activity at all. Most posts go out around 08:00 UTC, and Thursday is the busiest day of the week. Of the 18 posts sampled, 100% carry an image or video, 33% are part of a thread and 17% link out. The account's strongest tracked post pulled 25K interactions, about 346x its own typical post.

What is Charly Wargnier ♨️'s engagement rate on X?
Charly Wargnier ♨️ (@DataChaz) has an engagement rate of 0.039%, based on the median interactions across 18 original posts from the last 30 days against 182,599 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.039%, Charly Wargnier ♨️ sits above the 25th 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 @DataChaz have real engagement?
Its engagement rate beats 43% 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 @DataChaz post?
Most posts go out around 08:00 UTC, and Thursday is its busiest day, at roughly 3.07 posts per day across the measured window.

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