Back to @python_spaces's profile

Python Space engagement report

@python_spaces - 104K followers on X

Measured over 6 original posts from a 30-day window, last computed on October 14, 2026.

Engagement

Per follower
0.044%
of 104K followers
Per impression
0.795%
5.8K views on a typical post
Reach
5.54%
of its followers see a post
Typical post
46
interactions (median)
Saved
0.596%
34 bookmarks on a typical post
Posting rate
1.57/day
active 50% of days
Peak time
17:00 UTC
Friday

Early reading. We have captured 6 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 46 interactions against 104K followers, an engagement rate of 0.044%. Posts are seen about 5.8K times each, and 0.795% of those impressions turn into an interaction. That is about 5.54% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Friday is the busiest day of the week. Of the 6 posts sampled, 50% carry an image or video, 33% are part of a thread and 33% link out. The account's strongest tracked post pulled 346 interactions, about 7.5x its own typical post. Only 6 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 6 original posts from a 30-day window, last computed on October 14, 2026.

Where this sits in the catalog

At 0.044%, Python Space sits above the 25th percentile of the 212,270 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.

p100.004%
p250.025%
p50 (median)0.146%
p750.695%
p902.61%
p9983.1%
Engagement rate as a share of followers, across the 212,270 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,455 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 percentile0.004%
25th percentile0.025%
50th percentile0.146%
75th percentile0.695%
90th percentile2.61%
99th percentile83.1%

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 Friday 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: 17:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 17:00 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: Friday.
SunMonTueWedThuFriSat
Above the authors' own mediansBelowScale: plus or minus 111%Busiest day: Friday
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 video50% of posts+111%+108% to +115%34K
Outbound link33% of posts-41%-42% to -40%32K
Typical length-no effect-2% to -1%42K
  • 50% 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.
  • 33% 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 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

  • Sep 25, 20267.5x their median

    Tested Qoder this week. Dropped a docs link into the task screen. Asked it to generate an architecture diagram. The agent read the docs, planned the structure, and built the diagram without me touching a single tool. https://t.co/xH0uBeXq8M

    118921315145K viewsView on X
  • Sep 25, 20266.5x their median

    https://t.co/VVDiN1rvRU

    224351919108K viewsView on X
  • Oct 5, 20261.7x their median

    Everyone is talking about Jev right now. Devs are already building wild things with it. Mario bots, drones, on-chain traders. Here are 10 open source projects worth copying if you have Jev API access 🧵👇 https://t.co/dWLGRqniVk

    6212317.3K viewsView on X
  • Oct 10, 2026

    Overnight agent runs rarely go wrong because of the coding. They go wrong because nothing checks the work until a human wakes up. This is the loop that fixes that. The agent writes the code, calls the TestSprite CLI to run Integration Tests against the running app, reads the failing step and fix prompt that come back, patches the code, and runs the tests again. No human in the loop until the flows pass. The animation is the cute version. The real version leaves a video replay of every run for you to check over breakfast.

    84111.8K viewsView on X
  • Oct 11, 2026

    What are the current problems with Local AI? @grok

    60114.2K viewsView on X
  • Oct 3, 2026

    Immediate openings at https://t.co/Xs57hX4cmL Bringing on 50 Staff-level Software Engineers this month. You’ll partner with leading frontier labs on evaluations and dataset creation. $200–$300/hr Fully remote It’s demanding, detail-heavy work definitely not glamorous but the compensation reflects it. Apply: https://t.co/xMzK8yvH1r

    42002.9K 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.

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 46 interactions against 104K followers, an engagement rate of 0.044%. Posts are seen about 5.8K times each, and 0.795% of those impressions turn into an interaction. That is about 5.54% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.6 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 17:00 UTC, and Friday is the busiest day of the week. Of the 6 posts sampled, 50% carry an image or video, 33% are part of a thread and 33% link out. The account's strongest tracked post pulled 346 interactions, about 7.5x its own typical post. Only 6 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 Python Space's engagement rate on X?
Python Space (@python_spaces) has an engagement rate of 0.044%, based on the median interactions across 6 original posts from the last 30 days against 104,348 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.044%, Python Space sits above the 25th percentile of the 212,270 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 @python_spaces have real engagement?
There is not yet enough sample to rank this account against others of its size.
When does @python_spaces post?
Most posts go out around 17:00 UTC, and Friday is its busiest day, at roughly 1.57 posts per day across the measured window.

Keep going