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Vlad Mihalcea engagement report

@vlad_mihalcea - 93K followers on X

Measured over 33 original posts from a 30-day window, last computed on August 26, 2026.

Engagement

Middle of its size range
Per follower
0.034%
of 93K followers
Per impression
0.992%
3.2K views on a typical post
Reach
3.45%
of its followers see a post
Typical post
32
interactions (median)
Saved
0.558%
18 bookmarks on a typical post
Posting rate
1.3/day
active 53% of days
Peak time
13:00 UTC
Tuesday

A typical post picks up 32 interactions against 93K followers, an engagement rate of 0.034%. Measured over 33 original posts, its engagement rate beats 33% of 3,848 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 3.2K times each, and 0.992% of those impressions turn into an interaction. That is about 3.45% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 33 posts sampled, 36% carry an image or video and 73% link out. The account's strongest tracked post pulled 195 interactions, about 6.1x its own typical post. Recurring topics include #java, #javazone, #smalltalk.

Measured over 33 original posts from a 30-day window, last computed on August 26, 2026. Recurring tags: #java, #javazone, #smalltalk.

Compared with accounts its own size

Vlad Mihalcea's engagement rate beats 33% of the tracked X accounts closest to it in follower count (3,848 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 45% 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 0.034%, Vlad Mihalcea sits above the 25th percentile of the 37,214 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.081%.

p100.002%
p250.012%
p50 (median)0.081%
p750.436%
p902.10%
p99160.2%
Engagement rate as a share of followers, across the 37,214 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 106,785 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 percentile0.012%
50th percentile0.081%
75th percentile0.436%
90th percentile2.10%
99th percentile160.2%

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 13:00 UTC, and Tuesday 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: 13:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 6%Busiest hour: 13:00 UTC
Show engagement by hour posted, utc as a table
Engagement by hour posted, UTC
Hour (UTC)Vs author medianPosts
00:00 UTC-1%51K
01:00 UTC-2%52K
02:00 UTC-3%51K
03:00 UTC-4%54K
04:00 UTC-6%44K
05:00 UTC-4%42K
06:00 UTC-4%49K
07:00 UTC-5%53K
08:00 UTC-4%61K
09:00 UTC-3%71K
10:00 UTC-2%73K
11:00 UTC-3%80K
12:00 UTC-2%88K
13:00 UTC-2%96K
14:00 UTC-4%99K
15:00 UTC-2%103K
16:00 UTC-3%100K
17:00 UTC-2%92K
18:00 UTC-1%86K
19:00 UTC-2%81K
20:00 UTC-1%75K
21:00 UTC-1%67K
22:00 UTC-1%58K
23:00 UTC-1%52K
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: Tuesday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 5%Busiest day: Tuesday
Show engagement by day of week as a table
Engagement by day of week
DayVs author medianPosts
Sunday+5%234K
Monday0%294K
Tuesday-3%288K
Wednesday-1%253K
Thursday-2%247K
Friday-3%256K
Saturday+3%230K
See what moves engagement across the whole catalogWhat counts as a good engagement rate at this size

Best tweets

  • Aug 10, 20266.1x their median

    Pro tip: If the LLM generates code that you cannot understand, it means you need to upskill. Use AI to explain what was generated and provide more resources to help you keep up with the model. Being able to review the code that AI generates will become a mandatory requirement for software engineers.

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  • Aug 18, 20264.5x their median

    In 2026, Claude Code has reached the point where it can generate reasonable code. Lately, I have had to wear the Product Owner, Business Analyst, Software Architect, and QA Engineer hats, rather than the Software Developer or Platform Engineer ones.

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  • Aug 24, 20263.0x their median

    Claude Code tip: If you are using Claude Code and have lots of projects where you saved sessions, keep in mind that, by default, Claude will remove the ones you haven't used over the past 30 days. To prevent this issue, set the cleanupPeriodDays property in ~.claude/settings.json to a much higher value. Imagine the horror when I opened a project that I hadn't used in more than a month, only to see that my precious sessions were gone. šŸ˜€ Luckily, Windows had a backup of that folder, and I could recover some of them, but don't rely on that. Better to keep them for longer, especially if those are side projects.

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  • Aug 24, 20262.4x their median

    How to detect the Hibernate N+1 query problem during testing https://t.co/pBh7baRUFP

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  • Aug 14, 20262.2x their median

    Never in my life have I been "absolutely right" so often as I've been told by AI in 2026

    624503.8K viewsView on X
  • Apr 14, 20262.0x their median

    🤩 10 months of video editing, 42 lessons, and 389 minutes of video material šŸš€ I'm proud of how my High-Performance Spring Persistence video course turned out. https://t.co/V8iptQnoM7 https://t.co/DwkLYVmWSl

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  • Aug 21, 20261.9x their median

    Unit and integration tests protect your application against hallucinations whether those come from an AI agent or a human

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  • Aug 18, 20261.6x their median

    14 High-Performance Java Persistence Tips https://t.co/XlOlbsrJLc

    493004.1K viewsView on X
  • Aug 21, 2026

    Spring Boot performance tuning https://t.co/1Am7K4lilM

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  • Aug 16, 2026

    How to store date, time, and timestamps in UTC time zone with JDBC and Hibernate https://t.co/3FoPw4gB3Z https://t.co/YWOEdA0gVR

    356013.8K 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

#java#javazone#smalltalk

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 32 interactions against 93K followers, an engagement rate of 0.034%. Measured over 33 original posts, its engagement rate beats 33% of 3,848 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 3.2K times each, and 0.992% of those impressions turn into an interaction. That is about 3.45% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 13:00 UTC, and Tuesday is the busiest day of the week. Of the 33 posts sampled, 36% carry an image or video and 73% link out. The account's strongest tracked post pulled 195 interactions, about 6.1x its own typical post. Recurring topics include #java, #javazone, #smalltalk.

What is Vlad Mihalcea's engagement rate on X?
Vlad Mihalcea (@vlad_mihalcea) has an engagement rate of 0.034%, based on the median interactions across 33 original posts from the last 30 days against 93,443 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.034%, Vlad Mihalcea sits above the 25th percentile of the 37,214 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 @vlad_mihalcea have real engagement?
Its engagement rate beats 33% of the tracked X accounts closest to it in follower count (3,848 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 @vlad_mihalcea post?
Most posts go out around 13:00 UTC, and Tuesday is its busiest day, at roughly 1.3 posts per day across the measured window.

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