AI telegram bot. — @aigram
😁1

Channel
@huggingface
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
254subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002648335111 |
|---|---|
| Type | Channel |
| Username | @huggingface |
| Created | Between 1 March 2025 and 31 July 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/huggingface |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Aug 2026, 02:03 | 254 | no change |
| 6 Aug 2026, 14:38 | 254 | first reading |
20 posts held, back to 11 February 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 10 May 2026. An engagement rate over an empty window would be a number about nothing.
Lifetime counters from Telegram’s own channel header, read 7 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
8 reactions across 7 posts, in 6 distinct kinds. The most used accounts for 25.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 2 | 25.0% | |
| 😁 | 2 | 25.0% | |
| ⚡ | 1 | 12.5% | |
| 👀 | 1 | 12.5% | |
| 👏 | 1 | 12.5% | |
| 🥰 | 1 | 12.5% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 9 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 8reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 11 February 2026 to 10 May 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
AI telegram bot. — @aigram
😁1
Hugging Face (Twitter) RT @evijit: Today, @evaluatingevals is introducing Every Eval Ever, a unified, open data format and public dataset for AI evaluation results.
Hugging Face (Twitter) RT @Cohere_Labs: Introducing ✨Tiny Aya✨, a family of massively multilingual small language models built to run where people actually are. Tiny Aya delivers strong multilingual performance in 70+ global languages in a 3.35B parameter model, efficient enough to run locally, even on a phone.
Hugging Face (Twitter) RT @Cohere_Labs: Very special to work with our @huggingface friends to bring Tiny Aya, the most capable multilingual open-weight model at its scale to the world! 🚀 Big thanks to @ngxson for the huge help merging the changes into llama.cpp.
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Hugging Face (Twitter) RT @AdinaYakup: Happy Spring Festival🧧🐎 Here’s to another year of building and sharing! 新的一年,继续开源同行 🤗
🥰1
Hugging Face (Twitter) RT @NielsRogge: Dots.ocr is known for being among the SOTA for OCR Looks like the new 1.5 model is SOTA on OlmOCRBench! https://huggingface.co/rednote-hilab/dots.ocr-1.5
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Hugging Face (Twitter) RT @Alibaba_Qwen: 🚀 Qwen3.5-397B-A17B is here: The first open-weight model in the Qwen3.5 series. 🖼️Native multimodal. Trained for real-world agents. ✨Powered by hybrid linear attention + sparse MoE and large-scale RL environment scaling. ⚡8.6x–19.0x decoding throughput vs Qwen3-Max 🌍201 languages & dialects 📜Apache2.0 licensed 🔗Dive in: GitHub: github.com/QwenLM/Qwen3.5 Chat: chat.qwen.ai A…
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Hugging Face (Twitter) RT @RisingSayak: Will be there at the @OfficialINDIAai Impact Summit in Delhi from 17-19. Will also present ReflectionFlow at the symposium on the 18th. Kinda surprised there’s no real discussion about open science and open source, given the stellar speakers. Anyway, looking forward to it!
⚡1👍1
Hugging Face (Twitter) RT @j_dekoninck: Introducing QED-Nano: a 4B model for mathematical proof writing, competitive with larger models like GPT-OSS-120B. We open-source our entire pipeline, including data, code, and a blog post, hoping that the community can build on these artifacts to create more specialized models.
Hugging Face (Twitter) RT @_lewtun: We trained a tiny 4B model to reason for millions of tokens through IMO-level problems. Heaps excited to share our new blog post covering the full pipeline, from distilling the 🐳 to augmenting RL with a reasoning cache that unlocks extreme inference-time scaling for theorem proving. https://huggingface.co/spaces/lm-provers/qed-nano-blogpost
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Hugging Face (Twitter) RT @RisingSayak: An ambitious project today 🔥 We got an agent to write custom kernels that actually work for a given model, hardware instruction set, and other relevant model-dependent constraints. Benchmarks are our rewards here 🤪 We got these kernels to work with Diffusers and `torch.compile` and they delivered ACTUAL SPEEDUP without messing up the quality 🏆 Despite the competitive lands…
Hugging Face (Twitter) 💚💚💚 https://twitter.com/NVIDIAAIDev/status/2022023402047799389#m
Showing the 12 most recent of 20 posts we hold for @huggingface. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank — 116,752 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
@huggingface named 12 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 7 August 2026 — this entry's latest reading, not the date you are reading this.
“Hugging Face” (@huggingface), 254 subscribers as measured 7 August 2026. Telegram Register, tgregister.com/channel/huggingface.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.