François Chollet engagement report
@fchollet - 721K followers on X
Measured over 12 original posts from a 30-day window, last computed on September 1, 2026.
Engagement
A typical post picks up 646 interactions against 721K followers, an engagement rate of 0.09%. Measured over 12 original posts, its engagement rate beats 69% of 3,739 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 46K times each, and 1.39% of those impressions turn into an interaction. That is about 6.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 13:00 UTC, and Monday is the busiest day of the week. Of the 12 posts sampled, 17% carry an image or video, 33% are part of a thread and 25% link out. The account's strongest tracked post pulled 5.2K interactions, about 8.0x its own typical post.
Measured over 12 original posts from a 30-day window, last computed on September 1, 2026. Recurring tag: #sundayharangue.
Compared with accounts its own size
François Chollet's engagement rate beats 69% of the tracked X accounts closest to it in follower count (3,739 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 61% 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 0.09%, François Chollet sits above the 50th percentile of the 36,261 accounts in this comparison. That places it in the above the median band, which runs 0.08% to 0.431%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.431% |
| 90th percentile | 2.10% |
| 99th percentile | 160.7% |
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 Monday 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% | 50K |
| 01:00 UTC | -2% | 51K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 53K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 51K |
| 08:00 UTC | -4% | 60K |
| 09:00 UTC | -3% | 69K |
| 10:00 UTC | -2% | 71K |
| 11:00 UTC | -3% | 78K |
| 12:00 UTC | -2% | 86K |
| 13:00 UTC | -2% | 93K |
| 14:00 UTC | -3% | 96K |
| 15:00 UTC | -2% | 100K |
| 16:00 UTC | -3% | 97K |
| 17:00 UTC | -2% | 90K |
| 18:00 UTC | -1% | 84K |
| 19:00 UTC | -2% | 79K |
| 20:00 UTC | -1% | 74K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -2% | 57K |
| 23:00 UTC | -2% | 51K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 229K |
| Monday | 0% | 284K |
| Tuesday | -2% | 273K |
| Wednesday | -1% | 250K |
| Thursday | -2% | 243K |
| Friday | -3% | 251K |
| Saturday | +3% | 226K |
Best tweets
- Aug 24, 20268.0x 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, 20267.2x 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
- 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...)
- Apr 5, 20262.5x their median
Science went from the initial observation of radioactivity to a working atom bomb over 47 years via only about 9 distinct key experiments -- extremely few data points -- and symbolic models concise enough they would fit on a single page. This is what extreme generalization looks like, and it powered entirely by symbolic compression. Turn a handful of data points (deliberately collected) into a tractable plan to completely reshape reality, by reverse-engineering the causal symbolic rules behind the data.
- Jul 2, 20262.5x their median
Eventually, much of AI will converge towards intuition-guided symbolic world modeling, i.e. deep learning-guided program synthesis. It is inevitable. Symbolic modeling lets a system construct a compact, reusable, highly generalizable mental model of a problem space using minimal data.
- Dec 6, 20241.6x their median
Today we're announcing the winners of ARC Prize 2024. We're also publishing an extensive technical report on what we learned from the competition (link in the next tweet). The state-of-the-art went from 33% to 55.5%, the largest single-year increase we've seen since 2020. The benchmark remains unbeaten, but we're happy to see that research progress on the key bottleneck to AGIs (in particular on-the-fly adaptation to novel tasks) has been reignited in 2024 -- in part thanks to ARC Prize. In particular the competition has popularized Test Time Training (TTT), originally pioneered for ARC-AGI by Jack Cole last year. I believe TTT represents the largest jump in LLM generalization capabilities since the initial findings regarding in-context-learning circa 2019-2020. ARC Prize has also led to a considerable surge of research interest towards program synthesis. Competition winners: 🥇 the ARChitects (Daniel Franzen, Jan Disselhoff) 🥈 @guille_bar 🥉 alijs (Agnis Liukis) Paper Award winners: 🥇 "Combining Induction and Transduction For Abstract Reasoning" by @xu3kev et al. 🥈 "The Surprising Effectiveness of Test-Time Training for Abstract Reasoning" by @akyurekekin et al. 🥉 "Searching Latent Program Spaces" by @ClementBonnet16 & @MattVMacfarlane ARC-AGI-Pub Leaderboard (solutions using commercial APIs): 🥇 @jeremyberman 🥈 @ellisk_kellis & @akyurekekin 🥉 @ryangreenblatt
- Aug 23, 2026
An increasing fraction of social media consists of slop influencers using AI to make posts and bots replying to them. An echo of an echo of an echo
- Aug 10, 2026
Coding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks off.
- Aug 10, 2026
Builders respect builders. The loudest, most toxic haters are almost always the ones who have never built a thing -- the Nobody McPoasters.
- Aug 20, 2026
The conjecture is wrong, here's an AI-generated counter example https://t.co/HTsV8JvaSj
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 646 interactions against 721K followers, an engagement rate of 0.09%. Measured over 12 original posts, its engagement rate beats 69% of 3,739 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 46K times each, and 1.39% of those impressions turn into an interaction. That is about 6.44% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.7 posts a day over the last 30 days, with activity on roughly 57% of days. Most posts go out around 13:00 UTC, and Monday is the busiest day of the week. Of the 12 posts sampled, 17% carry an image or video, 33% are part of a thread and 25% link out. The account's strongest tracked post pulled 5.2K interactions, about 8.0x its own typical post.
- What is François Chollet's engagement rate on X?
- François Chollet (@fchollet) has an engagement rate of 0.09%, based on the median interactions across 12 original posts from the last 30 days against 721,073 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.09%, François Chollet sits above the 50th percentile of the 36,261 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 69% of the tracked X accounts closest to it in follower count (3,739 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 @fchollet post?
- Most posts go out around 13:00 UTC, and Monday is its busiest day, at roughly 1.73 posts per day across the measured window.