Kirk Borne engagement report
@KirkDBorne - 495K followers on X
Measured over 57 original posts from a 30-day window, last computed on October 21, 2026.
Engagement
A typical post picks up 20 interactions against 495K followers, an engagement rate of 0.004%. Measured over 57 original posts, its engagement rate beats 23% of 20,444 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 1.13% of those impressions turn into an interaction. That is about 0.358% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 04:00 UTC, and Wednesday is the busiest day of the week. Of the 57 posts sampled, 98% carry an image or video and 95% link out. The account's strongest tracked post pulled 16K interactions, about 807x its own typical post. Recurring topics include #datascience, #ai, #mathematics.
Measured over 57 original posts from a 30-day window, last computed on October 21, 2026. Recurring tags: #datascience, #ai, #mathematics.
Compared with accounts its own size
Kirk Borne's engagement rate beats 23% of the tracked X accounts closest to it in follower count (20,444 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 51% 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 0.004%, Kirk Borne sits above the 10th percentile of the 204,491 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.024%.
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 04:00 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% | 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 | 98% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 95% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 98% 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.
- 95% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 362 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 5, 2026807x their median
🇺🇸 US dollar has lost 97% of its purchasing power since the Federal Reserve was created. A $3 item in 1913 would cost $100 today. https://t.co/W7PjDWEffq
- Nov 29, 2022676x their median
My textbook with Kosuke Imai (@Harvard) is finally out! Assuming no prior knowledge of statistics or coding and only minimal knowledge of math, DSS teaches the fundamentals of survey research, predictive models, and causal inference + how to analyze real-world data with R. 🧵.. https://t.co/xwrgKu9QdJ
- Sep 13, 2026106x their median
STANFORD'S COMPLETE LLM COURSE IS FREE 📌 If you're serious about Generative AI, LLM Engineering, or Agentic AI, this is a great course to save. COURSE OUTLINE 1. Transformers https://t.co/6fTkogs23m 2. Transformer-Based Models & Tricks https://t.co/vt4sQNSPXc 3. Large Language Models https://t.co/pkRKICNbTL 4. LLM Training https://t.co/YJTrZHjmmt 5. LLM Tuning https://t.co/y1X4DtOy64 6. LLM Reasoning https://t.co/nZHE9JZfn7 7. Agentic LLMs https://t.co/c6vljsVEs4 8. LLM Evaluation https://t.co/gj6ZMTQUwz 9. Recap & Current Trends https://t.co/xTcaZ2OUOz OFFICIAL COURSE SYLLABUS https://t.co/g65ZKC2GJB The official Stanford syllabus confirms all 9 lectures and their topics, including RAG, tool calling, agents, LLM evaluation, RLHF, LoRA, reasoning, and current trends. If you already know the basics, pay extra attention to: Lecture 7 → Agentic LLMs Lecture 8 → LLM Evaluation
- Feb 13, 202670x their median
If I need to pick ONLY FIVE courses to learn AI & ML from scratch, I'll pick: ❯ CS221 - Artificial Intelligence ❯ CS229 - Machine Learning ❯ CS230 - DL ❯ CS234 - RL ❯ CS336 - LLM These courses could cost $100K. But Stanford is offering for FREE on YouTube: https://t.co/sEyFQP2XDW
- Sep 4, 202657x their median
Mathematics for Trading -- 151 Strategies (download 361-page PDF): https://t.co/OJ1X9QH2JB https://t.co/2bN5jGs0xT
- Feb 28, 202645x their median
Meet Kip Thorne. Born in 1940 in Logan, Utah, he grew up in an academic family fascinated by ideas, patterns, and the invisible forces of gravity and spacetime. He arrived at Caltech as a freshman in 1958 completely overwhelmed, no calculus, slow reader, thinking slower than his peers. He stumbled badly for a year and a half. It could have ended there. Instead, he found his own way, earned his degree, completed his PhD at Princeton under John Archibald Wheeler, and returned to Caltech as a professor. There he devoted his life to the wildest frontiers of Einstein’s general relativity: black holes, wormholes, and gravitational waves. For more than forty years, Thorne championed what most scientists called impossible detecting the faint ripples in spacetime caused by cosmic collisions. He became the driving force behind LIGO, the massive laser interferometer built to hear them. Then in September 2015, after decades of persistence, LIGO succeeded capturing the first direct gravitational waves from two black holes colliding a billion light-years away. In 2017 he shared the Nobel Prize in Physics for this landmark breakthrough. He also served as scientific consultant and executive producer for Christopher Nolan’s Interstellar, making the film’s black hole one of the most accurate depictions ever shown on screen. Kip Thorne spent his career chasing signals most thought he’d never live to hear. He persisted where others gave up. And still, he opened an entirely new window on the universe the era of gravitational-wave astronomy we live in today.
- Sep 19, 202635x their median
[Download 585-page PDF eBook] Game Theory: https://t.co/8NySL3GFmu ————— #GameTheory #Gamification #Mathematics #Statistics #Probability https://t.co/8QBFW96ZfB
- Sep 5, 202628x their median
"Statistics and Machine Learning in Python" by Edouard Duchesnay, Tommy Löfstedt, and Younes Feki (2025) is now in @ChapterPal's library. The book is hands-on first, with plenty of code snippets and illustrations. Table of contents: Python ecosystem for data science * Introduction to Python language * Overview of Python ecosystem for data-science * Development Environment * Package & Environments Dependency Management * Development with Integrated Development * Environment (IDE) and JupyterLab Python language * Import libraries * Basic operations * Data types * Execution control statements * List comprehensions, iterators, etc. * Functions * Regular Expression * System programming * Scripts and argument parsing * Networking * Object Oriented Programming (OOP) * Style guide for Python programming * Documenting * Modules and packages * Unit testing * Exercises Data Manipulation and Visualization * Numpy: Arrays and Matrices * Pandas: data manipulation Numerical Methods in Python * Numerical Differentiation * Numerical Integration * Time Series * Optimization (Minimization) by Gradient Descent Statistics * Univariate Statistics * Hands-On: Brain volumes study * Linear Mixed Models * Multivariate Statistics * Resampling and Monte Carlo Methods Machine Learning Overview * Introduction * Overfitting and Regularization Unsupervised Learning * Linear Dimensionality Reduction and Feature Extraction * Manifold learning: non-linear dimension reduction * Clustering Supervised Learning * Linear Models for Regression *Linear Models for Classification * Non-Linear Kernel Methods and Support Vector Machines (SVM) * Non-Linear Ensemble Learning Resampling Methods and Model Evaluation * Out-of-sample Validation for Model Selection and Evaluation * Random Permutations: sample the null distribution * Bootstrapping * Parallel Computation * Hands-On: Validation of Supervised Classification Pipelines Deep Learning: Introduction * Backpropagation * Multilayer Perceptron (MLP) Deep Learning for Computer Vision * Convolutional Neural Networks (CNNs) * Pretraining and Transfer Learning Natural Language Processing * Bag-of-Words Models Mathematical Details and Demonstrations * Negative Log-Likelihood (NLL) for Binary Classification with Sigmoid Activation Read the book with an AI tutor: https://t.co/avnhwoIT6K
- Sep 5, 202619x their median
📊📈Bayesian Data Analysis — download free 677-page PDF Statistics eBook at https://t.co/bFe911a8To +++++ 🌟Do it in Python🌟using this Practical Guide to Probabilistic Modeling: https://t.co/kjpaqEY2eY 🚀✨ ——— #DataScience #DataScientist #MachineLearning #ML https://t.co/50G5e5hbiS
- May 12, 202622x their median
https://t.co/7naHTlcbaI
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 20 interactions against 495K followers, an engagement rate of 0.004%. Measured over 57 original posts, its engagement rate beats 23% of 20,444 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 1.13% of those impressions turn into an interaction. That is about 0.358% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 10% of days saw any activity at all. Most posts go out around 04:00 UTC, and Wednesday is the busiest day of the week. Of the 57 posts sampled, 98% carry an image or video and 95% link out. The account's strongest tracked post pulled 16K interactions, about 807x its own typical post. Recurring topics include #datascience, #ai, #mathematics.
- What is Kirk Borne's engagement rate on X?
- Kirk Borne (@KirkDBorne) has an engagement rate of 0.004%, based on the median interactions across 57 original posts from the last 30 days against 495,219 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.004%, Kirk Borne sits above the 10th percentile of the 204,491 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 @KirkDBorne have real engagement?
- Its engagement rate beats 23% of the tracked X accounts closest to it in follower count (20,444 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 @KirkDBorne post?
- Most posts go out around 04:00 UTC, and Wednesday is its busiest day, at roughly 2 posts per day across the measured window.