Uncle Bob Martin engagement report
@unclebobmartin - 287K followers on X
Measured over 28 original posts from a 30-day window, last computed on October 29, 2026.
Engagement
A typical post picks up 492 interactions against 287K followers, an engagement rate of 0.172%. Measured over 28 original posts, its engagement rate beats 69% of 21,624 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 30K times each, and 1.62% of those impressions turn into an interaction. That is about 10.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.4 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Saturday is the busiest day of the week. Of the 28 posts sampled, 46% link out. The account's strongest tracked post pulled 2.8K interactions, about 5.7x its own typical post.
Measured over 28 original posts from a 30-day window, last computed on October 29, 2026.
Compared with accounts its own size
Uncle Bob Martin's engagement rate beats 69% of the tracked X accounts closest to it in follower count (21,624 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 56% 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 0.172%, Uncle Bob Martin sits above the 50th percentile of the 217,973 accounts in this comparison. That places it in the above the median band, which runs 0.149% to 0.705%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.149% |
| 75th percentile | 0.705% |
| 90th percentile | 2.63% |
| 99th percentile | 82.5% |
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 17:00 UTC, and Saturday 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 | 0% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 46% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | no effect | -2% to -1% | 42K |
- 0% 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.
- 46% 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 191 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
- Oct 25, 20265.7x their median
I composed this skill and have had pretty good luck with it. My grokbots use it when writing code. https://t.co/uUG29G2KPz
- Sep 7, 20265.5x their median
For the past several months I've been working on this hypothesis that agents need to be constrained by a harness of deterministic tools that force them to follow a strict process. Gherkin, Code, Unit Tests, Crap, Mutation Tests, etc. But while I've been hammering away on that, the models have gotten astronomically better. I'm starting to think that I'm over constraining them. Perhaps vastly over constraining them.
- Sep 1, 20264.5x their median
Have you noticed that when you call businesses an AI sometimes answers? I called a plumber recently and “Rudy“ answered. Rudy was so friendly and effusive that I immediately suspected that it was an AI. So I asked it, point blank, are you an AI? Rudy answered “yes I’m your helpful AI assistant”. Then it and I had an extremely efficient, very pleasant, conversation about what I needed. It knew all the facts, had the schedule right there, I got an appointment confirmed within 90 seconds. I was not put on hold, I was not subjected to someone who could not speak the language, I was not subjected to someone who is so new to their job that they didn’t know how to find the keys on the keyboard. It was a remarkably good experience. Soon, everything is going to be like that. No more “Press one for…”.
- Oct 14, 20264.2x their median
I'm a newcomer to @poteto's plist; But it seems to me that there is some very significant wisdom encoded in there. I suspect you could teach a master class on the principles.
- Oct 16, 20263.8x their median
Practical? No. Extravagant? Utterly! Beautiful? Absolutely! Will I buy one? No chance. Did I cheer? Enthusiastically!
- Sep 18, 20263.8x their median
Given that agents still need high level supervision. And given that the human review of code is a bottleneck. And given that the models have gotten good enough that strict deterministic harnesses are suboptimal. Then how should we supervise their output. My current solution is to use an interrogation tool coupled with an agent. The tool displays the structure of the system in a UML-like diagram, color coded with crap and mutation scores. The diagram is dynamic, allowing the user to click on element and drill down through the architecture -- all the way to code. The user can detect structures that need improvement and tell the associated agent to make the appropriate changes. These changes can range from high level architectural changes, to the lowest level code refactorings. The associated agent will make those changes and cause them to be redisplayed. The tool I'm using is custom made for me, but you can point an agent at it and use it as a template to build your own custom tool. It's a work in progress, so don't be surprised if it changes a lot over the next few weeks. But then you probably won't care because by then you'll be deep in customizing it for yourself. And anyway, you can always point an agent back at mine, or someone else's, and integrate improvements into your own. https://t.co/tu0Dsc9Vta.
- Oct 12, 20263.7x their median
This is just so strange. I'm talking to grokbot on my ipad, while it manipulates files on my mac, trying to adjust a voice/video sync problem induced by my use of a bluetooth microphone. The two of "us" are debugging the idiosyncrasies of Camtasia to get it to work. I mean, It's like talking to a very capable and thoughtful assistant. Wierd.
- Oct 25, 20263.6x their median
I am transitioning away from using CLI agents for development. On my last project I transitioned to using grokbots.
- Sep 2, 20263.4x their median
The trans founder of the Elementary OS Linux distribution is now claiming that Omarchy Linux funds are being raised for “ethnic cleansing”. https://t.co/kayBuL4FDs
- Oct 26, 20263.3x their median
If, five years ago, any one had told me that I would be able to build significant software systems while driving in my car in a few years, I would have written them off as being overly optimistic by several orders of magnitude. And that makes we wonder what the next five years will bring. I think we are in for a radical reshuffling of priorities that the powers that be are unprepared for. I suspect it will overtake them before they can muster a response.
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.
Buy or sell Twitter (X) accounts - escrow-protected
PlayerSells is an escrow marketplace for Twitter (X) accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 492 interactions against 287K followers, an engagement rate of 0.172%. Measured over 28 original posts, its engagement rate beats 69% of 21,624 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 30K times each, and 1.62% of those impressions turn into an interaction. That is about 10.5% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.4 post a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Saturday is the busiest day of the week. Of the 28 posts sampled, 46% link out. The account's strongest tracked post pulled 2.8K interactions, about 5.7x its own typical post.
- What is Uncle Bob Martin's engagement rate on X?
- Uncle Bob Martin (@unclebobmartin) has an engagement rate of 0.172%, based on the median interactions across 28 original posts from the last 30 days against 287,462 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.172%, Uncle Bob Martin sits above the 50th percentile of the 217,973 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 @unclebobmartin have real engagement?
- Its engagement rate beats 69% of the tracked X accounts closest to it in follower count (21,624 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 @unclebobmartin post?
- Most posts go out around 17:00 UTC, and Saturday is its busiest day, at roughly 1.4 posts per day across the measured window.