Peter Steinberger π¦ engagement report
@steipete - 610K followers on X
Measured over 20 original posts from a 30-day window, last computed on October 11, 2026.
Engagement
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 π¦'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 π¦ 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%.
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 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.
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 | 55% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 30% 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.π¦ https://t.co/XOc7X4jOxq
- 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!
- 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.
- 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.
- Aug 19, 20269.5x their median
512GB RAM Studios. Apple was good to us. π¦ https://t.co/NyvtNH6lRa
- Aug 24, 20268.4x their median
My full interview with Tibo (@thsottiaux) 0:45 Tibo's Lessons from Google DeepMind 4:22 Building OpenAIβs 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
- 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
- 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. π
- Oct 1, 20263.4x their median
"Today, weβre releasing two Cloudflare-trained decision models, Clef and Clef-flash" https://t.co/E8cYq4w7Fz Never seen an idea spreading so fast.
- Sep 9, 20263.2x their median
This is brilliant. https://t.co/39dr9faUMC
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 π¦'s engagement rate on X?
- Peter Steinberger π¦ (@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 π¦ 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.