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IBM Developer engagement report

@IBMDeveloper - 122K followers on X

Measured over 5 original posts from a 30-day window, last computed on October 5, 2026.

Engagement

Per follower
0.016%
of 122K followers
Per impression
0.523%
3.6K views on a typical post
Reach
2.98%
of its followers see a post
Typical post
19
interactions (median)
Saved
0.083%
3 bookmarks on a typical post
Posting rate
0.4/day
active 30% of days
Peak time
14:00 UTC
Monday

Early reading. We have captured 5 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.

A typical post picks up 19 interactions against 122K followers, an engagement rate of 0.016%. Posts are seen about 3.6K times each, and 0.523% of those impressions turn into an interaction. That is about 2.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 5 posts sampled, 100% carry an image or video, 20% are part of a thread and 60% link out. The account's strongest tracked post pulled 60 interactions, about 3.2x its own typical post. Recurring topics include #java, #podcast. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

Measured over 5 original posts from a 30-day window, last computed on October 5, 2026. Recurring tags: #java, #podcast.

Where this sits in the catalog

At 0.016%, IBM Developer sits above the 10th percentile of the 156,969 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.022%.

p100.003%
p250.022%
p50 (median)0.128%
p750.604%
p902.32%
p9983.4%
Engagement rate as a share of followers, across the 156,969 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 26,068 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 percentile0.003%
25th percentile0.022%
50th percentile0.128%
75th percentile0.604%
90th percentile2.32%
99th percentile83.4%

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 Monday 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: 14:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 14:00 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: Monday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Monday
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 link60% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 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.
  • 60% 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 209 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 9, 20263.2x their median

    Build AI agents and MCP tools in watsonx Orchestrate using IBM Bob. 🏗️ In this walkthrough, Ahmed Azraq shows how to build an MCP server with Bob, from designing to implementation. 🎥: https://t.co/cQN9fN7frD https://t.co/KCtYfT9ZvH

    475627.8K viewsView on X
  • Jun 23, 20262.4x their median

    AI agents are easy to demo. Production is a different problem. Enter CUGA. 🦉 An open-source agent harness that lets you focus on building instead of plumbing: https://t.co/5jRzs87mOV https://t.co/2RkUjdyllT

    336518.6K viewsView on X
  • Sep 9, 20262.0x their median

    Consider this your printed reminder: 💻 IBM TechXchange 2026 🗓️ October 26–29 📍 Atlanta, GA See you there. 👀 https://t.co/sYqRPWuzFt

    255713.8K viewsView on X
  • Aug 7, 20261.9x their median

    Native QSYS connectivity, built-in @IBM i skills and agentic workflows, all within your VS Code-family workspace. Premium Package for i brings IBM Bob directly to your IBM i system. No export loops. No friction. Explore what’s possible: https://t.co/EmIy7Hpz68 https://t.co/vBmdnA8by3

    266323.6K viewsView on X
  • Sep 21, 20261.8x their median

    Starting next week, builders of every background will be putting IBM Bob to work as they design and deploy real solutions. Are you ready to build with them? Check out the challenge themes and get started: https://t.co/I92DhkII0a https://t.co/92Mo1AC2cb

    226334.7K viewsView on X
  • Aug 27, 2026

    Give @IBM Bob a structured markdown file and clear instructions, and it can follow a repeatable workflow tailored to your needs. In this tutorial, @alexsotob shows how to create your first skill, refine it over time and adapt it to your own use cases: https://t.co/1JdQCIA6ym https://t.co/K82REra43L

    197203.2K viewsView on X
  • Aug 18, 2026

    One click. 🖱️ That’s all it takes to start building AI agents in watsonx Orchestrate using Bob. Watch Ahmed Azraq go from BRD to five specialized agents in this tutorial. https://t.co/gRHsVRbfLm

    197203.3K viewsView on X
  • Aug 17, 2026

    CPU vs. GPU: Which handles large-scale analytics better? Watch @gethackteam and William Hill put GPU-accelerated Presto to work on a 179-million-row dataset, then learn how to configure the environment yourself: https://t.co/KauTXo50gV https://t.co/WxcLhRrudu

    212012.6K viewsView on X
  • Jul 28, 2026

    Vibe coding gets you moving fast, but you eventually hit a wall of code you no longer fully trust or understand. The fix? Using spec-driven development to balance human ownership, agent autonomy, and project trust. @nheidloff shows you how to do it with @IBM Bob. ⤵️

    173303.1K viewsView on X
  • Sep 4, 2026

    Coding agents can turn a vague idea into working software fast. But what happens when no one has defined what “correct” means? Markus Eisele explores how much specification agentic development actually needs: https://t.co/X6APdp4XRv https://t.co/yKXK2lkVxq

    175002.5K 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

#java#podcast

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 19 interactions against 122K followers, an engagement rate of 0.016%. Posts are seen about 3.6K times each, and 0.523% of those impressions turn into an interaction. That is about 2.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.4 posts a day over the last 30 days, though only 30% of days saw any activity at all. Most posts go out around 14:00 UTC, and Monday is the busiest day of the week. Of the 5 posts sampled, 100% carry an image or video, 20% are part of a thread and 60% link out. The account's strongest tracked post pulled 60 interactions, about 3.2x its own typical post. Recurring topics include #java, #podcast. Only 5 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.

What is IBM Developer's engagement rate on X?
IBM Developer (@IBMDeveloper) has an engagement rate of 0.016%, based on the median interactions across 5 original posts from the last 30 days against 121,790 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.016%, IBM Developer sits above the 10th percentile of the 156,969 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 @IBMDeveloper have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @IBMDeveloper post?
Most posts go out around 14:00 UTC, and Monday is its busiest day, at roughly 0.4 posts per day across the measured window.

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