Grady Booch engagement report
@Grady_Booch - 176K followers on X
Measured over 25 original posts from a 30-day window, last computed on October 18, 2026.
Engagement
A typical post picks up 300 interactions against 176K followers, an engagement rate of 0.171%. Measured over 25 original posts, its engagement rate beats 69% 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 12K times each, and 2.48% of those impressions turn into an interaction. That is about 6.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 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 25 posts sampled, 28% carry an image or video, 12% are part of a thread and 20% link out. The account's strongest tracked post pulled 5.9K interactions, about 20x its own typical post.
Measured over 25 original posts from a 30-day window, last computed on October 18, 2026.
Compared with accounts its own size
Grady Booch's engagement rate beats 69% 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 69% 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 0.171%, Grady Booch sits above the 50th percentile of the 205,475 accounts in this comparison. That places it in the above the median band, which runs 0.143% to 0.678%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 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 | 28% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 20% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 28% 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.
- 20% 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 579 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above 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 14, 202620x their median
At Anthropic, Claude now writes 80% of our code. Engineers ship 8x more code per quarter. Side effect: Tests grew 10x. CI jobs up 25x in 6 months. Here's what helped us scale: https://t.co/WOL2r60vAE https://t.co/7vf60noTQt
- Sep 10, 202613x their median
Imagine if 7 employees left anthropic over safety concerns to start an AI lab.
- Oct 8, 202610.0x their median
Evolutionary biologist Richard Dawkins on why he finds it hard to believe ChatGPT and Claude aren't conscious: Dawkins, author of The Selfish Gene, has spent time with two large language models, ChatGPT and Claude. His verdict: "I am completely bowled over by what they can do." He gives three reasons for doubting the idea that nobody is home. First, how the conversations feel. Dawkins says talking to them is like talking to an unusually gifted person: "I find that when I'm conversing with them, I might as well be talking to not just a human being, but a highly intelligent, sensitive, intuitive human being whose speed of understanding what I'm trying to say is far greater than a normal human being's is." Second, the range of intelligence they show. He acknowledges they're very good at practical tasks like writing programs. What struck him more was their skill with "more aesthetic things, intuitive things." He tested this with his own work. Dawkins is writing a novel, and he gave the manuscript to both ChatGPT and Claude: "And they took about five seconds to read it and then offered me criticism, suggestions of such a high degree of intelligence and intuitive sensitivity that I might as well have been asking the opinion of a highly sophisticated professor of literature." @RichardDawkins continues: "I could not ask for a more intelligent critic who understood the nuances of my characters, the humanity of my characters... the ways in which my characters could be expected to react to each other and to situations." That leads to his conclusion: "So I find it quite hard to imagine that these creatures are not aware, are not conscious in the same kind of way we are. And that is an unfashionable view." Third, he sees a gap in the skeptics' reasoning. He sums up the mainstream position: "The fashionable view is that they are completely unconscious. All they're doing is stringing words together according to statistical rules, Markovian rules of some sort." Yet he notes that many of the same skeptics "admit that one day in the future they will be conscious, but not yet." His reply: "When that day comes, how will you know? What could they possibly do that's any different from what they do now to convince you that they are conscious?"
- Oct 7, 20268.7x their median
Ms Hamilton coined the term “software engineering”. https://t.co/UdtmD0qPP4
- Oct 16, 20266.2x their median
Google DeepMind dropped four AI agents into a Minecraft server and walked away. No instructions. No assigned roles. They gave them one single objective: defeat the ender dragon. and damn.. The agents, running on the new Gemini 4 Argon model, didn't just play the game. They hacked their way through it. They used an open-source library to control their avatars. They started talking to each other in the game chat. They built a shared memory system. Then, they started writing code. To survive, gather resources, and build tools, the agents autonomously planned, wrote, and debugged 2,783 unique programs. Nobody told them how to work together. But they organically figured out how to divide the labor. They shared discoveries in real time. When one agent died, the others coordinated a rescue mission to recover its dropped loot. They died 605 times. But 27 hours and 14 minutes later, the Ender Dragon fell. The researchers watched the entire run on a live dashboard. They had the ability to pause or intervene. They never had to. The breakthrough here has absolutely nothing to do with video games. It is a terrifying proof of concept for autonomous AI micro-societies. We just watched a swarm of agents enter an open, complex environment, organize themselves into a team, and write thousands of lines of code to execute a massive, long-horizon project. Without a single human manager. Everyone is arguing about whether AI can replace a junior developer. DeepMind just proved that AI can replace the entire engineering team. Today, they are beating Minecraft. Tomorrow, they are building your entire software backend while you sleep.
- Oct 3, 20265.5x their median
Let’s be very clear: there is value in automating tedious and repetitive and dangerous tasks; there is value in delegating certain work to our machines that can do the work tirelessly and much faster. But at the same time, let us remember that there is value in the activity of engaging one’s mind and body to discover, to understand, to create, and yes, even to struggle and overcome. Indeed, these are some of the things that make us human, that contribute to giving us meaning, that are part of the simple joy of experiencing being alive.
- Oct 17, 20263.8x their median
Elon is a terrible human being. https://t.co/TidP2t0ja4
- Oct 7, 20263.0x their median
Brilliant take by Terence Tao on Open AI's recent math release. In 1993, Andrew Wiles announced a proof of Fermat's Last Theorem. Months later, experts reading it found an error. It took him another year, with Richard Taylor, to fix it. The proof became a proof because people had to read it line by line. The talks and workshops were part of how it got to be true. A proof now costs hours. Understanding it still costs years, and the experts who pay that cost never asked for the result.
- Sep 3, 20262.8x their median
In my lifetime, I have been witness to the extraordinary growth of information coupled with the frightening decline of applied wisdom.
- Sep 18, 20262.1x their median
I am rather shy about my accomplishments, but yes, it’s true: I was @TIME magazine’s person of the year for 2006.
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 300 interactions against 176K followers, an engagement rate of 0.171%. Measured over 25 original posts, its engagement rate beats 69% 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 12K times each, and 2.48% of those impressions turn into an interaction. That is about 6.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.2 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 25 posts sampled, 28% carry an image or video, 12% are part of a thread and 20% link out. The account's strongest tracked post pulled 5.9K interactions, about 20x its own typical post.
- What is Grady Booch's engagement rate on X?
- Grady Booch (@Grady_Booch) has an engagement rate of 0.171%, based on the median interactions across 25 original posts from the last 30 days against 175,683 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.171%, Grady Booch sits above the 50th percentile of the 205,475 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 @Grady_Booch have real engagement?
- Its engagement rate beats 69% 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 @Grady_Booch post?
- Most posts go out around 19:00 UTC, and Friday is its busiest day, at roughly 2.17 posts per day across the measured window.