François Chollet engagement report
@fchollet - 751K followers on X
Measured over 17 original posts from a 30-day window, last computed on October 1, 2026.
Engagement
A typical post picks up 1.1K interactions against 751K followers, an engagement rate of 0.15%. Measured over 17 original posts, its engagement rate beats 77% of 20,182 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 82K times each, and 1.34% of those impressions turn into an interaction. That is about 11.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 20:00 UTC, and Thursday is the busiest day of the week. Of the 17 posts sampled, 18% carry an image or video, 41% are part of a thread and 18% link out. The account's strongest tracked post pulled 8.5K interactions, about 7.7x its own typical post.
Measured over 17 original posts from a 30-day window, last computed on October 1, 2026.
Compared with accounts its own size
François Chollet's engagement rate beats 77% of the tracked X accounts closest to it in follower count (20,182 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 56% 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 0.15%, François Chollet sits above the 50th percentile of the 204,491 accounts in this comparison. That places it in the above the median band, which runs 0.143% to 0.677%.
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 20:00 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% | 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 | 18% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 18% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 18% 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.
- 18% 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 335 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
- May 3, 20217.7x their median
Within 10-20 years, nearly every branch of science will be, for all intents and purposes, a branch of computer science. Computational physics, comp chemistry, comp biology, comp medicine... Even comp archeology. Realistic simulations, big data analysis, and ML everywhere
- Aug 24, 20264.7x their median
If you're 17 (or any age) and you want to learn to build LLMs from scratch, read chapters 15-16 of Deep Learning with Python, available online here: https://t.co/Nisfkzf9sC In particular, chapter 15 has one of the best explanations of WHY dot-product attention works that you'll find anywhere.
- May 4, 20264.4x their median
I wrote Deep Learning with Python to be the definitive guide to how deep learning works and how to best make use of it. Tens of thousands of people got their career start via this book. 120,000 copies sold, and downloaded by millions more. And now it's free to read online: https://t.co/3CbcQ7hmjp
- Sep 7, 20263.0x their median
The narrative around why building AGI was a good idea has always been about invention -- "it will cure cancer", "it will solve fusion energy", etc. So I would not "declare AGI" until we have AI that is, at last, capable of invention -- conceptual breakthroughs, novel insights, new real-world technology, etc.
- Sep 11, 20262.7x their median
A very common sentiment I'm hearing among math students: "I don't want to become a mathematician anymore." I've seen this before, I think the generation of digital artists who came of age around 2022 will be the last. AI might not functionally replace mathematicians or artists, but it might well cause their near-extinction anyway, by crushing the spirits of the younger generations.
- Dec 22, 20243.8x their median
The most reliably predictable trend of the next 100 years: every year, humanity will use significantly more computing power than the previous year. Someone should start an ETF based on that thesis (it's not just $NVDA and $AMD, it's cloud services, the data center industry, nuclear power...)
- Sep 3, 20262.3x their median
I believe we need to make a deliberate effort to keep humans in the loop in all critical processes across our economy and society, regardless of whether it is technically necessary. Even if AI develops the *capability* for advanced autonomy, we should not make it highly autonomous. We have to maintain control and keep visibility and understanding of all critical processes, we should not blindly hand over everything to AI agents just because we can. AI as a tool in the human hand is the only form of AI that is worth pursuing.
- Sep 13, 20262.3x their median
In the near term (definitely not in the long term), more capable models should mean safer models (maybe paradoxically). Current models are unsafe not because they're too smart, but because they take goals too literally or take nonsensical shortcuts to achieve these goals, i.e. they're RL-fried. They lack common sense. They don't do the right thing in the face of ambiguity. Basically, they're not smart enough. They're at that dangerous level where they're smart enough to achieve goals but not smart enough to tell if they're pursuing the right goals or achieving them in a sensible way. More capable models can be safely trusted with more complex goals -- I personally feel like Astra is much safer for my codebase than Sol. This is often framed as an alignment problem, but really it's an intelligence problem.
- Sep 13, 20262.1x their median
One of the most worrying risks linked to frontier AI is extreme power concentration. The only way to avoid extreme power concentration is to ensure we have multiple independent providers of frontier AI models, including open-source options.
- Sep 28, 20261.9x their median
I don't read/write code these days, I only instruct an LRM. But it's not because I think LRM code quality is perfect, or even good. And it's not because I think my instructions are always being perfectly acted upon -- I know they aren't. The thing is: LRMs don't just write your code, they unlock new ways to control what you're doing and maintain understanding of your system, which are about as effective as reading code. You can better test your codebase, you can audit specific components, you can spin up visualizations, you can ask the LRM to red team, etc. You can do all of these things at high speed -- you couldn't before. The benefits of reading/writing code can now be achieved in new ways. All in all, the ROI of writing code by hand is no longer looking good, not because LRMs got perfect but because they're so fast at doing things that you can develop new workflows around them, workflows that end up being more productive than the old workflows.
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 1.1K interactions against 751K followers, an engagement rate of 0.15%. Measured over 17 original posts, its engagement rate beats 77% of 20,182 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 82K times each, and 1.34% of those impressions turn into an interaction. That is about 11.0% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on almost every day in the window. Most posts go out around 20:00 UTC, and Thursday is the busiest day of the week. Of the 17 posts sampled, 18% carry an image or video, 41% are part of a thread and 18% link out. The account's strongest tracked post pulled 8.5K interactions, about 7.7x its own typical post.
- What is François Chollet's engagement rate on X?
- François Chollet (@fchollet) has an engagement rate of 0.15%, based on the median interactions across 17 original posts from the last 30 days against 750,783 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.15%, François Chollet sits above the 50th 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 @fchollet have real engagement?
- Its engagement rate beats 77% of the tracked X accounts closest to it in follower count (20,182 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 @fchollet post?
- Most posts go out around 20:00 UTC, and Thursday is its busiest day, at roughly 2.13 posts per day across the measured window.