Peter Steinberger 🦞 engagement report
@steipete - 581K followers on X
Measured over 15 original posts from a 30-day window, last computed on August 26, 2026.
Engagement
A typical post picks up 947 interactions against 581K followers, an engagement rate of 0.165%. Measured over 15 original posts, its engagement rate beats 78% of 3,791 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 130K times each, and 0.729% of those impressions turn into an interaction. That is about 22.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.5 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 18:00 UTC, and Sunday is the busiest day of the week. Of the 15 posts sampled, 60% carry an image or video, 7% are part of a thread and 27% link out. The account's strongest tracked post pulled 52K interactions, about 55x its own typical post.
Measured over 15 original posts from a 30-day window, last computed on August 26, 2026.
Compared with accounts its own size
Peter Steinberger 🦞's engagement rate beats 78% of the tracked X accounts closest to it in follower count (3,791 accounts, accounts of similar size (decile 8 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 44% 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 0.165%, Peter Steinberger 🦞 sits above the 50th percentile of the 36,654 accounts in this comparison. That places it in the above the median band, which runs 0.08% to 0.433%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.433% |
| 90th percentile | 2.10% |
| 99th percentile | 160.5% |
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 18:00 UTC, and Sunday 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% | 51K |
| 01:00 UTC | -2% | 52K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 52K |
| 08:00 UTC | -4% | 61K |
| 09:00 UTC | -3% | 70K |
| 10:00 UTC | -2% | 72K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 94K |
| 14:00 UTC | -4% | 97K |
| 15:00 UTC | -2% | 101K |
| 16:00 UTC | -3% | 98K |
| 17:00 UTC | -2% | 91K |
| 18:00 UTC | -1% | 85K |
| 19:00 UTC | -1% | 80K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -1% | 57K |
| 23:00 UTC | -2% | 52K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 231K |
| Monday | 0% | 288K |
| Tuesday | -2% | 278K |
| Wednesday | -1% | 251K |
| Thursday | -1% | 245K |
| Friday | -3% | 253K |
| Saturday | +3% | 227K |
Best tweets
- Feb 15, 202655x 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
- Aug 16, 202618x 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!
- Aug 19, 20267.9x their median
512GB RAM Studios. Apple was good to us. 🦞 https://t.co/NyvtNH6lRa
- Aug 24, 20266.9x 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
- Aug 21, 20264.4x 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() → 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. 😄
- Aug 21, 20262.6x their median
This week we read research from a team of academics that ran a software task across 7 agents and 5 models. They found that in domains with a mature CLI ecosystem, agents without MCP baked in completed the task just as reliably and were 5-28x cheaper. Full arXiv paper below https://t.co/DOtkMeqpoC
- Aug 23, 20262.2x their median
cli is nice, having UI visualizations and your team where you work is nicer. https://t.co/OFmkkkq6l5
- May 19, 20251.7x their median
OpenAI's Codex animation is cute https://t.co/TNpFZepIE2
- Aug 22, 20261.5x their median
We need the world's best architects to design the datacenter exteriors and make them beautiful
- Aug 15, 2026
Added a short instruction to our shared AGENTS MD file to upload videos to each PR that changes UI state. https://t.co/BjpJL1qFSR https://t.co/09wZoz7X0J
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 947 interactions against 581K followers, an engagement rate of 0.165%. Measured over 15 original posts, its engagement rate beats 78% of 3,791 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 130K times each, and 0.729% of those impressions turn into an interaction. That is about 22.4% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.5 posts a day over the last 30 days, with activity on roughly 50% of days. Most posts go out around 18:00 UTC, and Sunday is the busiest day of the week. Of the 15 posts sampled, 60% carry an image or video, 7% are part of a thread and 27% link out. The account's strongest tracked post pulled 52K interactions, about 55x its own typical post.
- What is Peter Steinberger 🦞's engagement rate on X?
- Peter Steinberger 🦞 (@steipete) has an engagement rate of 0.165%, based on the median interactions across 15 original posts from the last 30 days against 580,555 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.165%, Peter Steinberger 🦞 sits above the 50th percentile of the 36,654 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 78% of the tracked X accounts closest to it in follower count (3,791 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 18:00 UTC, and Sunday is its busiest day, at roughly 2.47 posts per day across the measured window.