Python Programming engagement report
@PythonPr - 223K followers on X
Measured over 108 original posts from a 30-day window, last computed on October 3, 2026.
Engagement
A typical post picks up 148 interactions against 223K followers, an engagement rate of 0.067%. Measured over 108 original posts, its engagement rate beats 52% 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 8.4K times each, and 1.76% of those impressions turn into an interaction. That is about 3.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.3 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 23:00 UTC, and Tuesday is the busiest day of the week. Of the 108 posts sampled, 88% carry an image or video. The account's strongest tracked post pulled 14K interactions, about 94x its own typical post.
Measured over 108 original posts from a 30-day window, last computed on October 3, 2026.
Compared with accounts its own size
Python Programming's engagement rate beats 52% 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 59% 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 0.067%, Python Programming sits above the 25th percentile of the 204,491 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 | 0.677% |
| 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 23:00 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.
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 | 88% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 0% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | -3% | -3% to -2% | 54K |
- 88% 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.
- 0% 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 91 characters, which falls in the 80 - 180 characters band. Across the catalog, posts of 80 to 180 characters run 3% below 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
- Sep 30, 202694x their median
Who said coding needs a keyboard? ๐ฎโ๐จ https://t.co/HEEu3vCn5f
- Sep 7, 202631x their median
Python ๐ฅ๐ฅ๐ฅ https://t.co/vDHSzd2bZN
- Sep 28, 202625x their median
POV: youโre a vibecoder in 2026.....๐๐ป๐๐ป https://t.co/TyymhjV7Kq
- May 31, 202612x their median
Python๐ฅ๐ฅ๐ฅ https://t.co/GLKmYJktTv
- Sep 6, 20269.3x their median
https://t.co/fRGfAzkFQe
- Sep 8, 20265.8x their median
Name a programming language more goated than Python in the AI era ๐ค https://t.co/kjscjKqxDS
- Sep 29, 20265.2x their median
This math sits underneath nearly every AI model being trained right now. Gradient. Jacobian. Hessian. Three words that look intimidating at first. But they are really just three ways of measuring change. (Throughout, assume the functions are smooth enough to differentiate.) ๐ญ./ ๐๐ฟ๐ฎ๐ฑ๐ถ๐ฒ๐ป๐ โf Takes a scalar function: f : โโฟ โ โ Returns a vector of n first-order partial derivatives (written as a column here). It answers: "Which direction makes f increase fastest?" That is why gradients are central to optimization. Gradient descent steps in the opposite direction, because the gradient points uphill. Backpropagation is how we compute gradients efficiently during training. ๐ฎ./ ๐๐ฎ๐ฐ๐ผ๐ฏ๐ถ๐ฎ๐ป J_F Takes a vector-valued function: F : โโฟ โ โแต Returns an m ร n matrix of first-order partial derivatives. It answers: "How does each output change with each input?" The Jacobian is the local linear map of F: ฮF โ J_F(x) ฮx for small ฮx It shows up in: โ sensitivity analysis and local linearization โ change of variables (through its determinant, when m = n) โ automatic differentiation: โข forward-mode AD computes Jacobian-vector products โข reverse-mode AD (backprop) computes vector-Jacobian products When m = 1, the Jacobian is just the gradient written as a row. ๐ฏ./ ๐๐ฒ๐๐๐ถ๐ฎ๐ป H_f Takes a scalar function: f : โโฟ โ โ Returns an n ร n matrix of second-order partial derivatives. It answers: "How does the gradient itself change?" That is why the Hessian captures the local curvature of f. When the second partial derivatives are continuous, the Hessian is symmetric. At a critical point (where โf = 0): โ positive definite Hessian โ strict local minimum โ negative definite Hessian โ strict local maximum โ indefinite Hessian โ saddle point โ semidefinite Hessian โ inconclusive It powers Newton-type and other second-order optimization methods, and uncertainty approximations such as the Laplace approximation. ๐ง๐ต๐ฒ ๐ฐ๐น๐ฒ๐ฎ๐ป ๐บ๐ฒ๐ป๐๐ฎ๐น ๐บ๐ผ๐ฑ๐ฒ๐น Gradient = first derivatives of one output โ tells you direction Jacobian = first derivatives of many outputs โ tells you sensitivity Hessian = second derivatives of one output โ tells you curvature And they connect: โf = (J_f)แต for scalar f (row vs. column convention) H_f = the Jacobian of โf Same idea: measure change. Different object: direction, sensitivity, curvature. Once this clicks, optimization stops looking like a pile of formulas. It starts looking like a map of the problem.
- Sep 7, 20265.1x their median
Types of databases, developers & engineers use https://t.co/a9wIALOs86
- Sep 9, 20264.4x their median
SQL to Pythonโ๏ธ https://t.co/D2ZB5Ht83s
- Sep 30, 20264.2x their median
Senior Developers ๐ซฃ https://t.co/BRZPDyTxIB
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.
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Reading these numbers
A typical post picks up 148 interactions against 223K followers, an engagement rate of 0.067%. Measured over 108 original posts, its engagement rate beats 52% 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 8.4K times each, and 1.76% of those impressions turn into an interaction. That is about 3.77% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 4.3 posts a day over the last 30 days, with activity on roughly 47% of days. Most posts go out around 23:00 UTC, and Tuesday is the busiest day of the week. Of the 108 posts sampled, 88% carry an image or video. The account's strongest tracked post pulled 14K interactions, about 94x its own typical post.
- What is Python Programming's engagement rate on X?
- Python Programming (@PythonPr) has an engagement rate of 0.067%, based on the median interactions across 108 original posts from the last 30 days against 222,540 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.067%, Python Programming sits above the 25th percentile of the 204,491 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 @PythonPr have real engagement?
- Its engagement rate beats 52% 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 @PythonPr post?
- Most posts go out around 23:00 UTC, and Tuesday is its busiest day, at roughly 4.27 posts per day across the measured window.