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Bilgin Ibryam engagement report

@bibryam - 85K followers on X

Measured over 44 original posts from a 30-day window, last computed on September 2, 2026.

Engagement

Middle of its size range
Per follower
0.049%
of 85K followers
Per impression
1.22%
3.4K views on a typical post
Reach
4.03%
of its followers see a post
Typical post
42
interactions (median)
Saved
1.19%
40 bookmarks on a typical post
Posting rate
1.87/day
active 53% of days
Peak time
09:00 UTC
Sunday

A typical post picks up 42 interactions against 85K followers, an engagement rate of 0.049%. Measured over 44 original posts, its engagement rate beats 37% 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 3.4K times each, and 1.22% of those impressions turn into an interaction. That is about 4.01% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.9 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 09:00 UTC, and Sunday is the busiest day of the week. Of the 44 posts sampled, 45% carry an image or video and 91% link out. The account's strongest tracked post pulled 1.2K interactions, about 29x its own typical post. Recurring topics include #devoxx, #kubernetes.

Measured over 44 original posts from a 30-day window, last computed on September 2, 2026. Recurring tags: #devoxx, #kubernetes.

Compared with accounts its own size

Bilgin Ibryam's engagement rate beats 37% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 5 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 0.049%, Bilgin Ibryam sits above the 25th percentile of the 66,128 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.

p100.002%
p250.016%
p50 (median)0.1%
p750.499%
p902.09%
p99119.9%
Engagement rate as a share of followers, across the 66,128 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 57,111 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 percentile0.002%
25th percentile0.016%
50th percentile0.1%
75th percentile0.499%
90th percentile2.09%
99th percentile119.9%

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 09:00 UTC, and Sunday 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: 09:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 09:00 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: Sunday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Sunday
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 video45% of posts+111%+108% to +115%34K
Outbound link91% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 45% 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.
  • 91% 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 235 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

  • Jun 24, 202429x their median

    Java is dead Kubernetes is dead DevOps is dead And now: Serverless is dead... In this context, 'dead' means: mature, stable, well understood by the industry. It's not the hot topic for marketing, devrel, or conferences anymore.

    995152391788K viewsView on X
  • Aug 29, 20268.7x their median

    https://t.co/C7uN2Vdu03

    316500018K viewsView on X
  • Aug 23, 20267.2x their median

    🎯 Just Use Postgres! by @denismagda This has to be one of the coolest titles for a book: The practical takeaway: before adding a specialty database, check what Postgres already handles. A real fast guide on: • JSON and full-text search • AI/RAG, time series, geospatial data, and queues https://t.co/nyRi0rslCK via @ManningBooks

    266286111K viewsView on X
  • Aug 30, 20264.5x their median

    Oracle: Agent memory is a database problem. Turso: Treat agent state like a filesystem, but implement it as a database TroveFiles: Filesystem-as-memory Mem0: Your AI Agent’s Memory Is Just a File? That’s the Problem. Neo4j: Memory should be a context graph Vercel: Agent memory is a state problem, not a memory problem ...

    1461025638K viewsView on X
  • Aug 30, 20264.5x their median

    There's no reason for software to be slow anymore https://t.co/BHNBedtkdc

    17890027K viewsView on X
  • Aug 16, 20264.0x their median

    https://t.co/hSlBRCjbYE

    143205014K viewsView on X
  • Aug 30, 20263.9x their median

    GitHub reviewed 2,500+ agents.md files. Five patterns stood out: • Put commands early • Show code, not prose • Name the exact stack • Set clear boundaries • Cover tests, structure, style, and Git An agent needs an operating manual, not a personality. https://t.co/GShsUymmkO 👆 Use it as a review checklist, not another generic prompt template.

    138225012K viewsView on X
  • Aug 16, 20263.9x their median

    Good system design starts before the architecture diagram. Donne Martin’s System Design Primer: 1. define use cases and constraints 2. sketch the high-level design 3. design core components 4. scale around bottlenecks https://t.co/D6P9JIdS8d https://t.co/61XkCCe9h9

    13724117.4K viewsView on X
  • Aug 23, 20263.5x their median

    Git at any scale - @cursor_ai 👏 A write‑ahead‑log first Git storage system on S3 gives fully consistent, horizontally scalable push and clone performance, solving Spokes’ replication and consistency limitations. Enables up to 120 pushes/s on S3 Standard and 300+ pushes/s on S3 Express One Zone . https://t.co/9MxdqybPU4

    131133011K viewsView on X
  • Aug 29, 20263.0x their median

    Taking AI agents to production takes more than deployment: • governed data and memory • evaluation before release • security and guardrails • observability after launch Google maps the lifecycle from development to production. https://t.co/Hqq3qxbWOt https://t.co/88RoCXndxA

    10220405.8K 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.

Recurring topics

#devoxx#kubernetes

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 42 interactions against 85K followers, an engagement rate of 0.049%. Measured over 44 original posts, its engagement rate beats 37% 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 3.4K times each, and 1.22% of those impressions turn into an interaction. That is about 4.01% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.9 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 09:00 UTC, and Sunday is the busiest day of the week. Of the 44 posts sampled, 45% carry an image or video and 91% link out. The account's strongest tracked post pulled 1.2K interactions, about 29x its own typical post. Recurring topics include #devoxx, #kubernetes.

What is Bilgin Ibryam's engagement rate on X?
Bilgin Ibryam (@bibryam) has an engagement rate of 0.049%, based on the median interactions across 44 original posts from the last 30 days against 84,590 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.049%, Bilgin Ibryam sits above the 25th percentile of the 66,128 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 @bibryam have real engagement?
Its engagement rate beats 37% 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 @bibryam post?
Most posts go out around 09:00 UTC, and Sunday is its busiest day, at roughly 1.87 posts per day across the measured window.

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