Annie Duke engagement report
@AnnieDuke - 99K followers on X
Measured over 10 original posts from a 30-day window, last computed on August 20, 2026.
Engagement
A typical post picks up 18 interactions against 99K followers, an engagement rate of 0.018%. Measured over 10 original posts, its engagement rate beats 26% of 3,917 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 7.2K times each, and 0.243% of those impressions turn into an interaction. That is about 7.26% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.73 post a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 10 posts sampled, 60% carry an image or video and 70% link out. The account's strongest tracked post pulled 37K interactions, about 2076x its own typical post.
Measured over 10 original posts from a 30-day window, last computed on August 20, 2026. Recurring tag: #gcapodcast.
Compared with accounts its own size
Annie Duke's engagement rate beats 26% of the tracked X accounts closest to it in follower count (3,917 accounts, accounts of similar size (decile 6 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 14% 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 0.018%, Annie Duke sits above the 25th percentile of the 37,996 accounts in this comparison. That places it in the below the median band, which runs 0.012% to 0.081%.
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 | 154.7% |
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 13:00 UTC, and Wednesday 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% | 54K |
| 02:00 UTC | -3% | 52K |
| 03:00 UTC | -4% | 56K |
| 04:00 UTC | -6% | 45K |
| 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% | 82K |
| 12:00 UTC | -2% | 90K |
| 13:00 UTC | -2% | 99K |
| 14:00 UTC | -3% | 102K |
| 15:00 UTC | -2% | 105K |
| 16:00 UTC | -4% | 103K |
| 17:00 UTC | -3% | 95K |
| 18:00 UTC | -1% | 89K |
| 19:00 UTC | -2% | 84K |
| 20:00 UTC | -1% | 78K |
| 21:00 UTC | -1% | 69K |
| 22:00 UTC | -2% | 60K |
| 23:00 UTC | -2% | 54K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 239K |
| Monday | 0% | 303K |
| Tuesday | -3% | 305K |
| Wednesday | -1% | 259K |
| Thursday | -2% | 251K |
| Friday | -3% | 260K |
| Saturday | +3% | 234K |
Best tweets
- Apr 21, 20262076x their median
I’m in love with this sentence: “The best math you can learn is how to calculate the future cost of current decisions.”
- Apr 26, 202698x their median
Underrated life skill: knowing when to give up.
- Jul 22, 202619x their median
I don’t believe reality is a simulation, but you genuinely couldn’t script this timeline: • Two weeks ago: At @swyx’s AI Engineer World’s Fair in SF, I decide at the last minute to introduce my friend @uri_rolls onstage for his talk on cyber benchmarks for infrastructure penetration and access control (see below, amazing team). I say: “There is a future where cyber is alive and everyone is well protected and I’m pretty sure that future involves open-source models.” And later: “A big challenge is going to be speed: the speed of attack versus defense. When an intruder starts to enter, you have to see what’s happening and catch them.” • One week ago: @huggingface is hit by a sophisticated intrusion over the weekend. The traces look unlike anything we’ve seen before and suggest serious AI involvement, but we don’t yet know which model was used. The closed models we ask for help choke on their guardrails. We need to react fast, so we turn to @Zai_org’s GLM-5.2 to help us analyze the attack. • Earlier this week: @OpenAI reaches out, discloses what happened, and partners with us on the investigation. The intruder turns out to be exactly what we had discussed two weeks earlier: a fully autonomous agent, powered by an unreleased frontier model, attempting to gain access to part of our infrastructure. Sometimes the timeline we live in is genuinely vertigo-inducing.
- Mar 26, 202617x their median
Congrats to my friend @JuliaMinson, Professor of Public Policy at the @Kennedy_School of Government, on the publication of her new book: "How to Disagree Better." Julia is a decision scientist with research interests in conflict management, negotiations, and judgment and decision making. Her main line of research addresses the“psychology of disagreement”–How do people engage with opinions, values, and judgments that conflict with their own? She guest authored my Substack this week. Check it out and grab her book– a must read! https://t.co/baD5xEs7e7
- May 2, 202413x their median
Annie Duke (@AnnieDuke) is a decision-making expert, former professional poker player, special partner at @firstround, and the author of Thinking in Bets (a national bestseller) and Quit: The Power of Knowing When to Walk Away. She also co-founded @AllDecisionEd, a nonprofit whose mission is to improve lives by empowering students through decision skills education. In our conversation, Annie shares: 🔸 Her biggest lessons from the late Daniel Kahneman 🔸 How to use pre-mortems and “kill criteria” 🔸 The power of “mental time travel” 🔸 The relationship between money and happiness 🔸 The nominal group technique for better decisions 🔸 How @firstround improved their decision-making process using her techniques 🔸 A ton of tactical decision-making frameworks 🔸 Much more Listen now 👇 - YouTube: https://t.co/3NNXhgaHAG - Spotify: https://t.co/2wHtRIecww - Apple: https://t.co/oGXDhnY9mG Some key takeaways: 1. Use the “3Ds” framework to make better decisions for your org: a. Discover: Collect individual opinions independently before group discussion and then share these with everybody prior to the meeting. b. Discuss: Compare perspectives within the group, focusing on understanding points of disagreement rather than building consensus. c. Decide: Make decisions independently post-discussion to minimize group influence. 2. Incorporate the word “nevertheless” into discussions, especially when making decisions or addressing disagreements. It acknowledges the other person’s perspective while affirming your decision or stance. 3. Replace confrontational language like “I disagree” or “you’re wrong” with phrases like “I don’t understand” so everyone feels heard and valued. If people feel heard, they are more likely to feel like they contributed to the decision, even when they disagree. 4. Use pre-mortems to set kill criteria. Before starting a project, imagine failure and what early warning signs might have predicted it. Commit in advance to reassess or pivot if you notice those red flags later on. This makes it easier to walk away when sunk costs and overconfidence bias loom large. 5. Know that if you’re considering quitting, it’s likely overdue. Uncertainty, sunk costs, identity, and ownership effects conspire to make us persist with failing efforts longer than we should. We wait for incontrovertible proof to quit. If quitting crosses your mind, examine if you’d start the project today given what you now know.
- Jun 1, 20267.6x their median
I love this excerpt from my interview with @AnnieDuke, one of my favorite guests on the #RicherWiserHappier podcast. Here, she offers superb insights on how to avoid being irrational when we have a losing investment. https://t.co/XCqEgKTitC. I'm now sharing highlights like this on my new YouTube channel (@WilliamGreenMarketsandLife), including clips from my conversations with great investors like Ray Dalio, Howard Marks, Joel Greenblatt, Mohnish Pabrai, Tom Russo & many more. I hope you find it a helpful resource.
- Aug 12, 20266.9x their median
I still think the most "must read" for any prospective, aspiring, or developing trader or investor to ever read is "Thinking in Bets" by @AnnieDuke https://t.co/s93Q6lEGfV
- Jun 25, 20265.2x their median
I’m excited to add 3 of my favorite teachers/humans to Lenny’s List on @MavenHQ. Huge alpha in learning from this crew: 1. @Shreyas's Product Sense course: how to make the correct product decisions in the face of ambiguity 2. @clairevo and Zach Davis’s Executive AI Playbook course: how EPD leaders should redesign their operating model to enable AI transformation 3. @AnnieDuke's Decision Making course: how to avoid cognitive biases and groupthink, and make better decisions A few reasons why I continue to recommend @MavenHQ: 1. Learn from people who've done the work: Instructors have decades of experience and have shipped real products at scale. 2. Hands-on projects: Every course pushes you to ship something. 3. Fresh content: With the change of pace these days, live courses are the only way to avoid stale advice. Check all the courses out at https://t.co/RPaMHUcf6U and use code LENNYSLIST to get a whopping 15-30% off.
- Mar 18, 20263.7x their median
Experience is essential for learning but paradoxically, it can also get in the way. I call this the “paradox of experience.” The core issue here is that we process outcomes sequentially, treating each single result as if it reveals whether a decision was good or bad, even though a single outcome is just one realization of a probabilistic set. To simplify, I like to use my “Cognitive Chain Saw” metaphor. Before a decision, and looking into the future, we see a “tree of possibilities”. Think of the trunk as the present, and various branches representing different ways the future could unfold. After an outcome, once a specific future event occurs, our minds use a cognitive chain saw to hack off the branches that didn’t happen. We leave only the branch that occurred, making the past appear linear and inevitable, rather than probabilistic. The danger here is hindsight bias, causing us to believe that we “knew it all along” or “should have known”, simply because the other possibilities have vanished from our view. To solve the paradox of experience and counter the cognitive chain saw, we have to deliberately reassemble the tree. We must remind ourselves of the full range of potential futures that existed at the time of the decision. Only then can we fairly evaluate the quality of the process rather than the luck of the outcome. Still overwhelmed by the possibilities? I teach a live, online cohort-based @MavenHQ course on how to make smarter, faster, and more confident choices, including how to: - Strengthen analytical reasoning and strategic thinking in complex or ambiguous situations. - Use probabilistic thinking to make sound decisions even when outcomes are unclear. - Enhance collaboration and group reasoning using structured input and feedback techniques. My next Maven cohort starts April 20th. Join us with this offer: https://t.co/Z9pKjoIGZA
- Aug 6, 20262.7x their median
"How lucky are you?" ;) I recently had the fun chance to sit down with @Markmanson and talk about decision making, navigating uncertainty, and maximizing "human" luck in our day-to-day decision making. I think you'll enjoy this conversation a lot. I certainly did. https://t.co/WjmotOPt0d
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.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 18 interactions against 99K followers, an engagement rate of 0.018%. Measured over 10 original posts, its engagement rate beats 26% of 3,917 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 7.2K times each, and 0.243% of those impressions turn into an interaction. That is about 7.26% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.73 post a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 10 posts sampled, 60% carry an image or video and 70% link out. The account's strongest tracked post pulled 37K interactions, about 2076x its own typical post.
- What is Annie Duke's engagement rate on X?
- Annie Duke (@AnnieDuke) has an engagement rate of 0.018%, based on the median interactions across 10 original posts from the last 30 days against 99,215 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.018%, Annie Duke sits above the 25th percentile of the 37,996 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 @AnnieDuke have real engagement?
- Its engagement rate beats 26% of the tracked X accounts closest to it in follower count (3,917 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 @AnnieDuke post?
- Most posts go out around 13:00 UTC, and Wednesday is its busiest day, at roughly 0.73 posts per day across the measured window.