Jeff Bullas engagement report
@jeffbullas - 535K followers on X
Measured over 52 original posts from a 30-day window, last computed on August 27, 2026.
Engagement
A typical post picks up 6 interactions against 535K followers, an engagement rate of 0.001%. Measured over 52 original posts, its engagement rate beats 11% of 3,899 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.8K times each, and 0.365% of those impressions turn into an interaction. That is about 0.333% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 17:00 UTC, and Thursday is the busiest day of the week. The account's strongest tracked post pulled 85 interactions, about 14x its own typical post.
Measured over 52 original posts from a 30-day window, last computed on August 27, 2026.
Compared with accounts its own size
Jeff Bullas's engagement rate beats 11% of the tracked X accounts closest to it in follower count (3,899 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 28% 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 0.001%, Jeff Bullas sits below the 10th percentile of the 37,856 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.012%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.081% |
| 75th percentile | 0.439% |
| 90th percentile | 2.10% |
| 99th percentile | 155.6% |
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 Thursday 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% | 53K |
| 01:00 UTC | -2% | 53K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 55K |
| 04:00 UTC | -6% | 44K |
| 05:00 UTC | -4% | 43K |
| 06:00 UTC | -4% | 50K |
| 07:00 UTC | -5% | 54K |
| 08:00 UTC | -4% | 63K |
| 09:00 UTC | -3% | 72K |
| 10:00 UTC | -2% | 75K |
| 11:00 UTC | -3% | 81K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 98K |
| 14:00 UTC | -3% | 101K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 102K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 88K |
| 19:00 UTC | -2% | 83K |
| 20:00 UTC | -1% | 77K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 53K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 238K |
| Monday | 0% | 302K |
| Tuesday | -3% | 302K |
| Wednesday | -1% | 257K |
| Thursday | -2% | 250K |
| Friday | -3% | 259K |
| Saturday | +3% | 233K |
Best tweets
- Aug 25, 202614x their median
Here is a trap I think more of us will fall into. You ask ChatGPT a hard question. Then Claude. Then Gemini. They all give roughly the same answer. You think: “Three AIs agree. It must be right.” Not necessarily. A 2026 study found that AI advice can exert social influence much like human advice. And several AI systems may share similar training data, assumptions and patterns. So three answers are not automatically three independent votes. For important decisions, I am starting to use AI differently. Instead of asking three machines: “What should I do?” I ask these 4 questions: • What Is The Strongest Reason This Advice Is Wrong? • Which Assumption Is Doing Most Of The Work? • What Evidence Would Reverse Your Recommendation? • What Would A Smart Person Who Disagrees Say? AI gives us abundant advice. But the scarce skill is knowing when agreement is evidence and when it is merely repetition. Source: Scientific Reports, 2026
- Aug 24, 202612x their median
One thing keeps bothering me as I think about humans competing with intelligent machines. AI has advantages we cannot match. It can read faster. Remember more. Work longer. Compare millions of possibilities. Operate at a scale no human can approach. But there is one strange thing missing. It doesn't need anything to matter. It doesn't wake up wanting to build a company. It doesn't worry about its children. It doesn't feel the sting of failure. It doesn't fall in love with a problem and spend ten years trying to solve it. Humans have limited intelligence, limited time and limited energy. But those limits force us to choose what matters. Perhaps that is not merely a weakness. Meaning may be one of the reasons human judgment remains valuable in a world of almost unlimited intelligence.
- Aug 19, 20266.8x their median
AI has made starting incredibly cheap. > Starting an article. > Starting a podcast. > Starting a business. > Starting a book. You can generate the name, plan, research, first draft and marketing ideas before lunch. But when starting becomes almost free, another human trait becomes more valuable: Finishing. The internet may soon be filled with millions of impressive beginnings. The scarce person could be the one who chooses something worthwhile and stays with it long enough to make it real. AI reduces the cost of starting. It doesn't remove the need for commitment.
- Aug 25, 20265.2x their median
AI has solved one problem for me and created another. I can now do far more than I could two years ago. That sounds entirely positive. Until every new capability creates another possibility. > Another article. > Another business idea. > Another tool. > Another experiment. > Another project I could start. AI gives us more capacity. But capacity without limits easily becomes complexity. So I am testing a very simple rule: For every new project I start, one project must stop. Not because the new idea is bad. But because my attention is finite. That may be one of the strange lessons of abundant intelligence: The machine can keep generating possibilities forever. Humans still need the discipline to choose what not to do. AI can expand your opportunities. Do not let it expand your life beyond your ability to live it.
- Aug 26, 20265.0x their median
Something strange has been happening on my website. At times, around 90% of the traffic is coming from desktop devices rather than phones or tablets. That made me wonder: Are all these visitors even human? The analytics alone cannot answer that. But then I came across a much bigger number. Cloudflare says automated agents and bots now generate more than half of all web requests. That does not mean half of my website visitors are AI agents. But it tells us something important. The web now has another major population: Machines. For years I designed websites for humans: > Make the headline clear. > Make the page easy to read. > Build trust. > Get the click. Now I’m asking another set of questions: • Can A Machine Understand What I Sell? • Can It Verify My Claims? • Can It Compare My Product? • Can It Work Out Whether I Am Trustworthy? The web was built for humans. Machines are moving in. We may soon need to design for both.
- Aug 20, 20264.0x their median
One of history’s earliest warnings about AI was written 2,400 years before AI existed. In Plato’s Phaedrus, Socrates tells a story about the invention of writing. A king warns that it may weaken memory and give people the appearance of wisdom without its reality. Every powerful thinking tool solves one problem and creates another. AI can give us answers. It cannot make us wise unless we still do the thinking.
- Aug 20, 20263.3x their median
AI can make you feel more capable and more confused at the same time. You can explore 20 careers, 50 ideas, 100 interests and a dozen versions of who you could become before breakfast. The problem is not lack of possibility. It is knowing what deserves your attention. I used to think identity came first, then purpose, then action. Now I think it is more circular. Identity → Purpose → Curiosity → Judgment → Focus → Writing → Action → Reflection → Identity We start with some sense of who we are. But we also discover ourselves by acting. And writing seems to be one of the bridges. When I write about an idea, I understand it better. I notice what I actually believe. Patterns become visible. My questions improve. My focus gets stronger. So perhaps AI shouldn’t tell us who we are. It should help us notice who we are becoming. That feels like a much better use of the machine.
- Aug 24, 20263.0x their median
For twenty years, almost every online business model depended on one tiny human action: The click. > Click the search result. > Click the ad. > Click the email. > Click the product. > Click through to the website. A huge part of the digital economy was built around persuading humans to move from one page to another. AI agents could compress that entire journey. Instead of opening ten tabs, you may eventually say: “Find the best option, compare them and tell me which one to buy.” The machine does the clicking. You get the decision. That raises a question that should worry anyone with an online business: What is a website worth when the human no longer needs to visit it? The web may not disappear. But the economics underneath it could change dramatically.
- Aug 19, 20263.0x their median
At 51, I applied for around 700 jobs. One word kept appearing: Overqualified. It sounded almost flattering. It wasn't. It meant decades of experience had become a cost rather than an asset. Now AI is changing the economics of knowledge work. Machines can increasingly produce drafts, retrieve information and perform routine analysis. That leaves some very human things harder to automate: • Judgment • Pattern Recognition • Context • Relationships • Knowing Which Problem Matters Those usually take time to acquire. Maybe AI won't make experience obsolete. Maybe AI is about to reprice it.
- Aug 26, 20262.8x their median
A robot may eventually be able to clean my home better and cheaper than a human. I still wouldn’t replace my cleaner. She has been coming to my home for about 15 years. I know her stories and struggles. She knows mine. And she is part of the family. That is why a 2026 study involving 2,309 people caught my attention. People trusted robots more for mechanical tasks than for work that required more human-like thinking or feeling. There was another clue: Human supervision restored some of that lost trust. That makes sense to me. Automation works best when the value is mainly in the task. But many jobs contain something else: • Trust • Care • Judgment • Conversation • Relationship We may discover that some apparently simple jobs were never just jobs. A machine can replace the task without replacing the human value around it. Source: Journal of Behavioral and Experimental Economics, 2026
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 6 interactions against 535K followers, an engagement rate of 0.001%. Measured over 52 original posts, its engagement rate beats 11% of 3,899 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 1.8K times each, and 0.365% of those impressions turn into an interaction. That is about 0.333% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.8 posts a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 17:00 UTC, and Thursday is the busiest day of the week. The account's strongest tracked post pulled 85 interactions, about 14x its own typical post.
- What is Jeff Bullas's engagement rate on X?
- Jeff Bullas (@jeffbullas) has an engagement rate of 0.001%, based on the median interactions across 52 original posts from the last 30 days against 534,898 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.001%, Jeff Bullas sits below the 10th percentile of the 37,856 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 @jeffbullas have real engagement?
- Its engagement rate beats 11% of the tracked X accounts closest to it in follower count (3,899 accounts), which puts it in the bottom 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 @jeffbullas post?
- Most posts go out around 17:00 UTC, and Thursday is its busiest day, at roughly 1.83 posts per day across the measured window.