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Peter Steinberger 🦞 engagement report

@steipete - 610K followers on X

Measured over 20 original posts from a 30-day window, last computed on October 11, 2026.

Engagement

Top quarter for its size
Per follower
0.132%
of 610K followers
Per impression
0.453%
175K views on a typical post
Reach
29.1%
of its followers see a post
Typical post
792
interactions (median)
Saved
0.117%
205 bookmarks on a typical post
Posting rate
3.5/day
active 53% of days
Peak time
19:00 UTC
Friday

A typical post picks up 792 interactions against 610K followers, an engagement rate of 0.132%. Measured over 20 original posts, its engagement rate beats 75% of 20,182 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 175K times each, and 0.453% of those impressions turn into an interaction. That is about 28.7% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.5 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 19:00 UTC, and Friday is the busiest day of the week. Of the 20 posts sampled, 55% carry an image or video, 20% are part of a thread and 30% link out. The account's strongest tracked post pulled 52K interactions, about 63x its own typical post.

Measured over 20 original posts from a 30-day window, last computed on October 11, 2026.

Compared with accounts its own size

Peter Steinberger 🦞's engagement rate beats 75% of the tracked X accounts closest to it in follower count (20,182 accounts, accounts of similar size (decile 10 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 29% 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 0.132%, Peter Steinberger 🦞 sits above the 25th percentile of the 204,313 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.

p100.004%
p250.024%
p50 (median)0.143%
p750.677%
p902.55%
p9982.4%
Engagement rate as a share of followers, across the 204,313 accounts we have scanned enough to measure. The axis is logarithmic, because the top and bottom of this population are about 22,878 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 percentile0.024%
50th percentile0.143%
75th percentile0.677%
90th percentile2.55%
99th percentile82.4%

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 19:00 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: 19:00 UTC.
0003060912151821
Above the authors' own mediansBelowScale: plus or minus 111%Busiest hour: 19:00 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 video55% of posts+111%+108% to +115%34K
Outbound link30% of posts-41%-42% to -40%32K
Typical length--3%-3% to -2%54K
  • 55% 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.
  • 30% 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 142 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

  • Feb 15, 202663x their median

    I'm joining @OpenAI to bring agents to everyone. @OpenClaw is becoming a foundation: open, independent, and just getting started.🦞 https://t.co/XOc7X4jOxq

    42K3.8K4.2K1.9K6.6M viewsView on X
  • Aug 16, 202622x their median

    Here is how to enable a 1M-token context window in Codex for GPT-5.6 Sol. Even though we have tuned the context limit in Codex to be set optimally when it comes to performance and cost, this is a common ask, so here it is documented. A larger context window lets Codex retain more code, tool output, and conversation history before summarizing older material. You need a model that supports it. And GPT-5.6 Sol, for example, has a documented 1,050,000-token window. Open ~/.codex/config.toml and add or update these settings at the top level, before any [section] headers: ``` model = "gpt-5.6-sol" model_context_window = 1000000 model_auto_compact_token_limit = 900000 ``` The first setting selects the model. The second tells Codex to use a one-million-token context budget. The third starts automatic history compaction around 900,000 tokens, leaving some headroom. Restart Codex client and start a new session after saving. To try the configuration for a single CLI session without changing your defaults: ``` codex -m gpt-5.6-sol \ -c model_context_window=1000000 \ -c model_auto_compact_token_limit=900000 ``` Have fun, but also know that we tuned the default carefully!

    15K1.2K1.0K5313.8M viewsView on X
  • Sep 3, 202612x their median

    I think the best way to describe OpenAI's culture is as a mega startup. It's hard to believe unless you see it from the inside. Extreme ownership, care and pace.

    7.8K1681.6K691.6M viewsView on X
  • Oct 3, 202612x their median

    Do I know anyone at Google who could help? We're now over a week in review limbo for OpenClaw's Android app.

    8.9K136217911.7M viewsView on X
  • Aug 19, 20269.5x their median

    512GB RAM Studios. Apple was good to us. 🦞 https://t.co/NyvtNH6lRa

    7.0K9035068858K viewsView on X
  • Aug 24, 20268.4x their median

    My full interview with Tibo (@thsottiaux) 0:45 Tibo's Lessons from Google DeepMind 4:22 Building OpenAI’s Relentless Culture 7:23 Astra & Next Gen Models 11:18 How Fast AI Changes Developer Workflows 14:27 ChatGPT & Codex Merging 20:25 OpenAI vs. Anthropic 23:37 Why OpenAI Keeps Resetting Limits 30:25 Recursive Self-Improvement 32:00 Dangers That Caused "The Pause" 34:13 Will Ultra Fast Become the Default? 43:20 Why Everyone Needs to Try AI

    5.7K4792922132.5M viewsView on X
  • Jul 2, 20207.0x their median

    GPT3 writing code. A compiler from natural language to code. People don't understand β€” this will change absolutely everything. We're decoupling human horsepower from code production. The intellectual equivalent of the discovery of the engine. https://t.co/QGJbQRBdQv https://t.co/CJIaRK8j0M

    4.6K609139411View on X
  • Aug 21, 20265.2x their median

    This is a great real-world example of AI-assisted coding: Linus Torvalds just fixed a nasty Linux kernel GPU bug with substantial help from AI. The bug caused part of Intel Xe GPU compression metadata storage to be incorrectly exposed as usable VRAM, resulting in corrupted page tables and black screens. The eventual fix was basically a one-liner: round_up() β†’ round_down(). Finding it was the hard part: 24 debugging patches and 18 kernel boots. Linus says AI did much of the grunt work, repeatedly adding debug code and analyzing the results as he narrowed down the problem. The funniest part: the AI repeatedly told him the problem was "impossible and unsolvable" and suggested giving up. Linus kept pushing it until they found the bug. And then he let the AI write the commit message. πŸ˜„

    3.7K3138855256K viewsView on X
  • Oct 1, 20263.4x their median

    "Today, we’re releasing two Cloudflare-trained decision models, Clef and Clef-flash" https://t.co/E8cYq4w7Fz Never seen an idea spreading so fast.

    2.4K17611827196K viewsView on X
  • Sep 9, 20263.2x their median

    This is brilliant. https://t.co/39dr9faUMC

    2.1K16015977498K 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.

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Reading these numbers

A typical post picks up 792 interactions against 610K followers, an engagement rate of 0.132%. Measured over 20 original posts, its engagement rate beats 75% of 20,182 tracked accounts of a similar size. Comparing inside a size band matters here: engagement rate falls as accounts grow, so a raw rate would mostly just re-measure the follower count. Posts are seen about 175K times each, and 0.453% of those impressions turn into an interaction. That is about 28.7% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 3.5 posts a day over the last 30 days, with activity on roughly 53% of days. Most posts go out around 19:00 UTC, and Friday is the busiest day of the week. Of the 20 posts sampled, 55% carry an image or video, 20% are part of a thread and 30% link out. The account's strongest tracked post pulled 52K interactions, about 63x its own typical post.

What is Peter Steinberger 🦞's engagement rate on X?
Peter Steinberger 🦞 (@steipete) has an engagement rate of 0.132%, based on the median interactions across 20 original posts from the last 30 days against 610,485 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
Is that a good engagement rate?
At 0.132%, Peter Steinberger 🦞 sits above the 25th percentile of the 204,313 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 @steipete have real engagement?
Its engagement rate beats 75% of the tracked X accounts closest to it in follower count (20,182 accounts), which puts it in the top quarter for its size 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 @steipete post?
Most posts go out around 19:00 UTC, and Friday is its busiest day, at roughly 3.5 posts per day across the measured window.

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