clem 🤗 engagement report
@ClementDelangue - 642K followers on X
Measured over 15 original posts from a 30-day window, last computed on August 26, 2026.
Engagement
A typical post picks up 711 interactions against 642K followers, an engagement rate of 0.114%. Measured over 15 original posts, its engagement rate beats 73% of 3,809 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 58K times each, and 1.22% of those impressions turn into an interaction. That is about 9.08% 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 roughly 53% of days. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 15 posts sampled, 93% carry an image or video, 20% are part of a thread and 27% link out. The account's strongest tracked post pulled 1.9K interactions, about 2.6x its own typical post.
Measured over 15 original posts from a 30-day window, last computed on August 26, 2026.
Compared with accounts its own size
clem 🤗's engagement rate beats 73% of the tracked X accounts closest to it in follower count (3,809 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 58% 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 0.114%, clem 🤗 sits above the 50th percentile of the 36,852 accounts in this comparison. That places it in the above the median band, which runs 0.08% to 0.435%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.012% |
| 50th percentile | 0.08% |
| 75th percentile | 0.435% |
| 90th percentile | 2.10% |
| 99th percentile | 159.8% |
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% | 51K |
| 01:00 UTC | -2% | 52K |
| 02:00 UTC | -3% | 50K |
| 03:00 UTC | -4% | 54K |
| 04:00 UTC | -6% | 43K |
| 05:00 UTC | -4% | 42K |
| 06:00 UTC | -4% | 48K |
| 07:00 UTC | -5% | 52K |
| 08:00 UTC | -4% | 61K |
| 09:00 UTC | -3% | 70K |
| 10:00 UTC | -2% | 72K |
| 11:00 UTC | -3% | 79K |
| 12:00 UTC | -2% | 87K |
| 13:00 UTC | -3% | 95K |
| 14:00 UTC | -4% | 98K |
| 15:00 UTC | -2% | 101K |
| 16:00 UTC | -4% | 99K |
| 17:00 UTC | -2% | 91K |
| 18:00 UTC | -1% | 85K |
| 19:00 UTC | -1% | 80K |
| 20:00 UTC | -1% | 75K |
| 21:00 UTC | -1% | 66K |
| 22:00 UTC | -2% | 58K |
| 23:00 UTC | -2% | 52K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +4% | 232K |
| Monday | 0% | 290K |
| Tuesday | -2% | 281K |
| Wednesday | -1% | 252K |
| Thursday | -2% | 245K |
| Friday | -3% | 254K |
| Saturday | +3% | 228K |
Best tweets
- Aug 12, 20262.6x their median
Let's go! https://t.co/BAJlYIJqDQ https://t.co/FYTH5MrTBG
- Aug 10, 20262.6x their median
Meta is back! well done @finkd @alexandr_wang! https://t.co/gWvkuDeHzF
- Aug 25, 20262.5x their median
Had a lot of fun chatting again with my twin brother @dylan522p We went through lab economics over the next few years - the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone). And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities. One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry - the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. 0:00:00 – Two labs will soon control most of the world’s compute 0:07:01 – $6 billion in fab capex enables $1t+ of end revenue 0:13:08 – Compute prices will rise if the labs outbid everyone 0:18:22 – Which layer will capture most of the surplus? 0:25:40 – Will datacenter regulation slow down AI? 0:29:43 – Labs are shifting compute from inference to R&D 0:33:27 – China gets less than 10% of new compute, but its labs need less 0:48:48 – Will AI cause a sovereign debt crisis? 1:07:52 – Will the world's future workforce belong to a few companies?
- Aug 5, 20262.4x their median
Feels like Google could have been the dominating force in AI by open-sourcing the frontier with Gemini, Veo, and Nano Banana. Instead, they kept them behind APIs for a few billion dollars in revenue. Maybe there's still time?
- Apr 2, 20262.1x their median
Hot take: Git was the wrong abstraction for 90% of ML data. Checkpoints, optimizer states, training logs, agent traces - none of this needs version control. It needs fast, cheap, mutable storage. So we built Buckets. S3-like storage on the @huggingface Hub with Xet dedup and zero egress. Train in a bucket. Publish to a repo. One platform. 🤗🤗🤗
- Aug 25, 20261.9x their median
Who's excited? https://t.co/jiKFk0vwJn https://t.co/tfK3kqGmxk
- Aug 19, 2026
2018: HF is building a chatbot for teens OpenAI is building Open AI 2026: HF is building Open AI OpenAI is building a chatbot for teens https://t.co/phyCqUHgV4
- Aug 5, 2026
Some people are surprised that APIs (aka what Anthropic, OpenAI, and others provide) are treated differently than open weights in the new AI model framework. I'm not surprised at all, and it's actually very good policy. Let me explain: Model weights, APIs, and apps are three very different layers of the stack. Treating them the same would be a recipe for bad regulation. Think about how we handle cars. We don't regulate steel, we crash-test cars. Nobody asks a steel mill to guarantee that nothing dangerous will ever be built with its steel. Obligations sit with the carmaker and rules of the road with the driver, because that's where risk becomes real and where someone can actually act on it. Model weights are the steel of AI. They're raw research output, closer to science than product: no user, no interface, no deployment. They don't do anything on their own. And because everything else is built on top of them, this is the layer where regulation does the most damage. Restrict weights and you slow down all progress downstream, and you prevent countless positive use cases from ever emerging: the lab fine-tuning an open model for rare diseases, the startup serving a language big providers ignore, the safety researchers who can only audit models because the weights are open. You don't reduce risk, you just kill open source and concentrate power in a few big labs. APIs are the middle layer, the parts and engine suppliers of AI: a commercial service where a provider serves a model at scale. Here you have a business relationship, terms of service, the ability to monitor for abuse. It makes sense to expect transparency, security standards, and accountability from providers at this layer, because they can actually enforce things. Apps are the car on the road: where AI meets the real world. A medical assistant, a hiring tool, a companion for kids, a financial advisor. This is where concrete harm can happen, and conveniently, it's where we already have decades of regulation. Health, finance, employment, consumer protection. An AI hiring tool should comply with employment law whether it's powered by an open model, an API, or a spreadsheet. The principle is simple: regulate at the layer where risk actually materializes and where actors can act on it. Push obligations to the deployment layer, keep the research layer open. We don't regulate steel, we crash-test cars. Well done @realDonaldTrump @DavidSacks @mkratsios47!
- Aug 22, 2026
NVIDIA built its own coding harness to optimize CUDA GPU kernels and achieved a 100% score on ARC-AGI-3’s 25 public games, solving all 183 levels. With agents, we'll move from a world where it's quite hard to run, optimize, post-train your own AI models and kernels to a world where virtually everybody can do it. 100 million AI builders when?
- Aug 6, 2026
Fortunately AI agents don't just cyberattack us. They also use us more than ever for what we're actually built for: the storage and collaboration layer for AI 😅 New record: almost 4 PB of private & public training datasets, models, and agent traces added to Hugging Face last week.
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 711 interactions against 642K followers, an engagement rate of 0.114%. Measured over 15 original posts, its engagement rate beats 73% of 3,809 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 58K times each, and 1.22% of those impressions turn into an interaction. That is about 9.08% 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 roughly 53% of days. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 15 posts sampled, 93% carry an image or video, 20% are part of a thread and 27% link out. The account's strongest tracked post pulled 1.9K interactions, about 2.6x its own typical post.
- What is clem 🤗's engagement rate on X?
- clem 🤗 (@ClementDelangue) has an engagement rate of 0.114%, based on the median interactions across 15 original posts from the last 30 days against 642,332 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.114%, clem 🤗 sits above the 50th percentile of the 36,852 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 @ClementDelangue have real engagement?
- Its engagement rate beats 73% of the tracked X accounts closest to it in follower count (3,809 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 @ClementDelangue post?
- Most posts go out around 13:00 UTC, and Wednesday is its busiest day, at roughly 2.13 posts per day across the measured window.