Kylie engagement report
@KylieHopkinsX - 253K followers on X
Measured over 28 original posts from a 30-day window, last computed on September 25, 2026.
Engagement
A typical post picks up 306 interactions against 253K followers, an engagement rate of 0.12%. Measured over 28 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 81K times each, and 0.38% of those impressions turn into an interaction. That is about 31.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.3 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 28 posts sampled, 96% carry an image or video, 39% are part of a thread and 36% link out. The account's strongest tracked post pulled 641 interactions, about 2.1x its own typical post. Recurring topics include #aivideo, #capcutpc, #dreamina.
Measured over 28 original posts from a 30-day window, last computed on September 25, 2026. Recurring tags: #aivideo, #capcutpc, #dreamina.
Compared with accounts its own size
Kylie'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 21% 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%, Kylie 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 | 96% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 36% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 96% 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.
- 36% 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 649 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
- Aug 31, 20262.1x their median
One thing I like about this: it doesn’t expect students to sit through a 45-minute lesson and somehow remember everything. Gauth has 200+ free high school math courses with quick quizzes built in along the way. A lot more to try at https://t.co/0yo4g5anmN.course https://t.co/X21jdzpt2d
- Sep 18, 20261.6x their median
🚨 AI might soon predict the outcome of an experiment BEFORE you run it. I just came across JEPA Anything..... Its a research project exploring a unified world model for science. Instead of only answering questions, it tries to learn how scientific systems change when you intervene. It can learn anything like 1️⃣ Biology 2️⃣ Molecules 3️⃣ Physics 4️⃣ Clinical data etc... The crazy part is what happens next… 👀 You give it a state + intervention → it tries to predict the resulting state. This could completely change how researchers choose experiments. JEPA Anything is powered by @PhAILabs #PhAILabs #ScienceIDE #AI #tech
- Sep 6, 20261.5x their median
Bigger models are not the only path to useful medical AI. Efficiency matters too. Sante is designed as an MoE model with only 5.1B active parameters per token, while targeting demanding healthcare tasks. The model’s active footprint is part of the story. Here is why 👇 https://t.co/fxx3kDloF0
- Sep 11, 2026
🧙♂️✨ From a Blender white model to a cinematic magical world, GPT-6 Astra planned the workflow. PixVerse brought it to life. @PixVerse Try it on PixVerse, or in ChatGPT with the PixVerse Plugin. Prompt ↓ Create a 12-second, single-take white-model animation in Blender based on the attached reference image https://t.co/oEKq6hDALF, then use PixVerse with Seedance 2.5 to transform the exported animation into a cinematic, Witcher-3-inspired wizard workshop sequence. In Blender, use the reference image to guide the room layout, architecture, furniture, and props. Build an editable low-poly scene with clean meshes and UVs, using simple white or light-gray materials. Include stone walls, wooden shelves, an arched window, a fireplace, books, an astronomical instrument, and an alchemy table filled with potion bottles. Keep the silhouettes readable and the room proportions believable. Animate a low first-person viewpoint with exactly one right hand holding one potion. Begin with the bottle resting near the lower-right edge while the character looks around the workshop and toward the window. Raise the potion naturally, pause to examine it, shift focus onto the bottle, then lower it while turning toward the fireplace and glowing reagents. Leave a small gap between the palm and bottle while maintaining believable fingertip contact. Use smooth changes in speed, gentle curved head movements, subtle breathing, and slight arm lag. Check the complete action at normal playback for clipping, awkward grip, abrupt turns, and excessive shaking. Keep the Blender fireplace simple: use logs, low embers, and flickering light. Avoid solid cones or flame-shaped meshes, which can be interpreted as glowing rocks. Prepare a separate fireplace detail from the reference image to guide the final appearance. Export the complete white-model MP4. Then generate a 12-second video with PixVerse, explicitly referencing the Blender-exported white-model video as the camera, action, and spatial reference, the attached image as the scene appearance reference, and the fireplace detail as the fire reference. Preserve the continuous shot, low viewpoint, single-hand potion action, room layout, and timing. Enrich the scene with weathered stone, carved wood, worn leather, iron fittings, and realistic glass. Combine cool window light and drifting dust with warm fireplace illumination and softly glowing magical liquids. Create natural wood fire with thin, irregular flames curling between charred logs, glowing embers, occasional sparks, and subtle smoke. Keep the hand anatomy, bottle shape, fire movement, and focus transitions consistent throughout. Review the full generated video, especially the hand action and fireplace. Deliver the white-model MP4, the PixVerse-rendered MP4, and the editable Blender project. After acceptance, retain the latest approved versions and move older versions to recoverable storage.
- Sep 11, 2026
A blank shape went in… and a whole dessert collection came out 🍪 This was a fun workflow to watch. Starting from a basic Blender setup, @MeshyAI helped create different pastry white models, and GPT 6 Astra turned them into a complete 3D collection. The coolest part is how easy it becomes to test ideas: Want a different flavor? A different style? A completely new asset? Instead of restarting everything, I can just explore more possibilities much faster. 😝 Thanks to @MeshyAI for the collaboration! Excited to keep exploring AI + 3D. 🙌 #Astra #MeshyAI #ImageTo3D #GenerativeAI
- Sep 24, 2026
I followed Apsara mainly for Qwen4, but what unexpectedly caught my attention through the updates my friends and colleagues shared from the venue were the giant tech tote bags everywhere. Qwenbook, NVIDIA, Alibaba, Qoder, Meoo and more. They were carrying them all over the venue, and apparently some were even walking back and forth just to collect a few more. 👇
- Sep 8, 2026
Textbooks weren’t cutting it, so we made learning visual. Turn complex topics into interactive maps, study guides, and step-by-step explanations with Gauth. See it. Understand it. Remember it. Build your own visual course at https://t.co/fvsvWK7cMj https://t.co/O5wav234EX
- Aug 25, 2026
🚨 This SuperComputer builds your company with AI Always on computer Build complex software Use both open source and closed source AI Free backend including database, payment and auth Here’s how it works ↓
- Sep 7, 2026
For anyone who loves creating, this is one to watch. 👀🎥 CapCut World is coming to Los Angeles in October 2026, connecting creators and the creative community around AI-powered creativity, video creation, and the tools that help bring ideas to life. #CapCutWorld #CapCutDidThat https://t.co/Dq7HV10DCh
- Aug 20, 2026
This is what AI video creation should feel like. CapCut App + Seedance 2.5 gives creators a simple way to access a powerful AI video model without adding another complicated tool to the workflow. Easy to use. Powerful to create with. Affordable to experiment with. Try Seedance 2.5 starting from $0.06/sec. https://t.co/X151LthRMf #CapCut #CapCutApp #Seedance25 #CapCutai #CapCutDidThat
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 306 interactions against 253K followers, an engagement rate of 0.12%. Measured over 28 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 81K times each, and 0.38% of those impressions turn into an interaction. That is about 31.8% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.3 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 16:00 UTC, and Thursday is the busiest day of the week. Of the 28 posts sampled, 96% carry an image or video, 39% are part of a thread and 36% link out. The account's strongest tracked post pulled 641 interactions, about 2.1x its own typical post. Recurring topics include #aivideo, #capcutpc, #dreamina.
- What is Kylie's engagement rate on X?
- Kylie (@KylieHopkinsX) has an engagement rate of 0.12%, based on the median interactions across 28 original posts from the last 30 days against 253,367 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.12%, Kylie 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 @KylieHopkinsX 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 @KylieHopkinsX post?
- Most posts go out around 16:00 UTC, and Thursday is its busiest day, at roughly 3.33 posts per day across the measured window.