Telegram RegisterThe public register of Telegram

Channel

ML Underhood

@MLunderhood

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

4,682subscribers

+23 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1002130338791
TypeChannel
Username@MLunderhood
CreatedBetween 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/MLunderhood

Growth

4,6594,6824,670.56 August 2026 — 4,659 subscribers6 August 2026 — 4,661 subscribers10 August 2026 — 4,668 subscribers13 August 2026 — 4,682 subscribers6 August 202613 August 2026
4 measurements spanning 7 days, net +23. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 4,656–4,685 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 03:574,682+14
10 Aug 2026, 07:174,668+7
6 Aug 2026, 22:004,661+2
6 Aug 2026, 07:324,659first reading

Engagement

8 posts held, back to 10 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 7 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
35.8%
avg views ÷ 4,682 subscribers
Avg views / post
1,670
6 posts measured
Reaction rate
2.01%
reactions ÷ views · ER floor
Posts in window
6
of 8 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 11 August 2026
Posts held8 (10 July 202611 August 2026)
Views total10,047
Reactions total202
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 05:37 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

349 reactions across 8 posts, in 12 distinct kinds. The most used accounts for 37.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥13237.8%
11232.1%
👍3710.6%
❤‍🔥308.60%
👏102.87%
🥰82.29%
🎉61.72%
😍61.72%
🏆30.86%
🍾20.573%
💯20.573%
🤩10.287%

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 8 of the 8 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 349reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 8 most recent posts we hold, published 10 July 2026 to 11 August 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.

Recent posts

11 Aug 2026, 10:23 UTC647 views27 reactionsread 12 August 2026

Posted without readable text

11👍7😍6❤‍🔥2🔥1

7 Aug 2026, 12:33 UTC≈1,420 views45 reactionsread 12 August 2026
Photo

Выложили препринт второй части Gryphon о рекомендациях в Яндекс Музыке О первой статье Gryphon мы уже рассказывали. Теперь вышла вторая работа, Gryphon-v2: One Model in Place of a Cascade, где мы попробовали заменить одной моделью весь рекомендательный каскад. В первой версии Gryphon уже объединял Semantic-ID generation и item-level scoring. Пользовательская история кодировалась один раз, а дальше модель генерирова

19🔥16👍7👏3

31 Jul 2026, 09:04 UTC≈1,660 views28 reactionsread 12 August 2026
Photo

Registers Matter for Pixel-Space Diffusion Transformers Сегодня разбираем свежую статью от команды Visual GenAI в Yandex Research на тему регистров в картиночных диффузионках. Немного контекста Изначально регистры придумали для визуальных трансформеров. В self-supervised-моделях вроде DINO обнаружили «артефакты»: некоторые патч-токены получали слишком высокие нормы, а CLS-токен начинал смотреть не на основной объе

🔥138👍5❤‍🔥1💯1

24 Jul 2026, 09:49 UTC≈2,000 views23 reactionsread 12 August 2026
Photo

Конференция за конференцией — мы на SIGIR 2026! В эти дни в Мельбурне проходит конференция по исследованиям и разработке информационного поиска. И мы уже там, чтобы посмотреть (и вам показать) интересное, а также представить собственную работу — Gated Bidirectional Linear Attention for Generative Retrieval. В рекомендательных системах generative retrieval обычно строится по схеме «энкодер–декодер». Энкодер обрабаты

🔥14❤‍🔥53👍1

23 Jul 2026, 09:31 UTC≈1,660 views44 reactionsread 12 August 2026
Photo

Scalable Keyword Spotting via Modular Network Expansion Сегодня рассказываем о статье от команды технологий голосового ввода Яндекса. Работу приняли на конференцию Interspeech 2026, которая пройдёт с 27 сентября по 1 октября в Сиднее. Авторы предложили новый способ обновлять набор голосовых команд в умных устройствах без забывания уже известных. Наш коллега Виктор Хаймоненко рассказал о методе подробнее. Чтобы рас

24❤‍🔥10🔥8👍2

14 Jul 2026, 10:18 UTC≈2,660 views35 reactionsread 12 August 2026
Photo

Рассказываем о новой unified-модели в Alice AI С недавних пор в Алисе AI и Шедевруме появилась обновлённая модель — Alice AI ART 2.0. Пользователи уже сейчас могут протестировать два базовых сценария её работы: Text-to-Image и Image-to-Image. Для Яндекса этот релиз — первый шаг к тому, чтобы получить unified-модель с едиными метриками и стеком, которая одинаково хорошо умеет и в T2I, и в I2I. Команда генеративных м

15🥰8👏6🔥3👍2💯1

10 Jul 2026, 14:55 UTC≈15,500 views26 reactionsread 12 August 2026
Photo

По следам воркшопов и постерных сессий Вчера мы анонсировали активности с участием наших исследователей на ICML 2026. Теперь делимся фото и впечатлениями спикеров о том, как это было. Дмитрий Еремеев, Yandex Research: Для меня это первая конференция — всё было в новинку, проходило очень насыщенно. Одну из наших статей, GraphPFN, мы представляли в формате постера на основной конференции, а ещё я выступал с докладом

❤‍🔥12🔥84👍2

10 Jul 2026, 10:30 UTC≈5,120 views121 reactionsread 12 August 2026
Photo

Получили Best Paper Award на воркшопе ICML 2026! Статья GraphPFN: A Prior-Data Fitted Graph Foundation Model получила статус лучшей работы на воркшопе Graph Foundation Models: A New Era for Graph Machine Learning 💫 Машинное обучение на графах сейчас проходит примерно тот же путь, который несколько лет назад проделали NLP и CV — от узкоспециализированных моделей к foundation models. Именно вокруг этого строилась про

🔥6928👍11🎉6🏆3🍾2👏1🤩1

Showing the 8 most recent of 8 posts we hold for @MLunderhood. 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

Citation-graph rank — 10,512 of 1,151,006entries 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.

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 9 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.

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.

Cite this entry

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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“ML Underhood” (@MLunderhood), 4,682 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/MLunderhood.

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.