Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 88% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
33 measurements spanning 42 days, net +211. 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 18,611–18,886 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
17 Sept 2026, 20:38
18,854
+10
15 Sept 2026, 19:38
18,844
-6
13 Sept 2026, 23:59
18,850
+13
12 Sept 2026, 09:38
18,837
+8
10 Sept 2026, 08:58
18,829
+9
7 Sept 2026, 06:37
18,820
+12
4 Sept 2026, 10:21
18,808
+8
2 Sept 2026, 23:48
18,800
+3
1 Sept 2026, 21:33
18,797
+14
1 Sept 2026, 00:19
18,783
-1
30 Aug 2026, 20:44
18,784
+1
29 Aug 2026, 22:55
18,783
-1
28 Aug 2026, 21:07
18,784
+3
27 Aug 2026, 22:23
18,781
+9
26 Aug 2026, 23:25
18,772
+8
26 Aug 2026, 02:56
18,764
+3
25 Aug 2026, 04:24
18,761
+1
24 Aug 2026, 03:15
18,760
+1
22 Aug 2026, 09:18
18,759
+4
20 Aug 2026, 21:44
18,755
first reading
Engagement
22 posts held, back to 30 July 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 55 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
15.4%
avg views ÷ 18,854 subscribers
Avg views / post
2,900
6 posts measured
Reaction rate
0.978%
reactions ÷ views · ER floor
Posts in window
6
of 22 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
Window
Rolling 30 days · latest post in window 2 September 2026
Posts held
22 (30 July 2026 – 2 September 2026)
Views total
17,380
Reactions total
170
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 02:11 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.
What this channel posts
Video runtime
23s
Average length
23s
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
600 reactions across 21 posts, in 15 distinct kinds. The most used accounts for 41.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
251
41.8%
🔥
162
27.0%
👍
45
7.50%
👏
43
7.17%
❤🔥
34
5.67%
👎
23
3.83%
🦄
13
2.17%
😱
11
1.83%
👀
6
1.00%
😁
4
0.667%
🥰
4
0.667%
✍
1
0.167%
🎉
1
0.167%
😢
1
0.167%
🤩
1
0.167%
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 21 of the 22 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 600 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 30 July 2026 to 2 September 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.
🧿 Одна модель вместо всего рекомендательного стека Яндекс Музыки
Привет, это Николай Савушкин, руководитель службы рекомендательных технологий Яндекс R&D. В августе мы опубликовали Sona Technical Report — результат года нашей работы над новой генеративной end-to-end-моделью рекомендаций.
Раньше все рекомендации в Музыке проходили через многоступенчатый каскад: отбором занимались больше 15 кандидатогенераторов, а за…
🎓 Как преподаватель математики стала руководителем ML-направления в ШАДе
Привет, это Валя Бронер. Я 11 лет преподавала студентам математику в Томском государственном университете, а затем перешла в бигтех. Последние три года я руковожу ML-направлением в ШАДе Яндекса и создаю курсы по машинному обучению.
Сегодня я расскажу, что мне нравится в преподавании, почему решила уйти из вуза в ШАД и как ML-разработчики помог…
📎 Найти баланс между ресёрчем и инженерией
Всем привет, это Никита Корягин, ML-инженер в команде алайнмента Alice AI. До того как устроиться в Яндекс, я довольно много занимался ресёрчем: экспериментировал с моделями, писал статьи, публиковался на конференциях. Такой формат мне всё ещё очень близок, но в какой-то момент я захотел получать больше практической отдачи от работы: увидеть, как идея доезжает до реальной м…
🍫 Как мы читаем даты изготовления с фотографий
На связи Роман Шинкаренко, я разработчик в службе AI-сервисов Лавки. В новом посте рубрики «Обучено Яндекс Лавкой» расскажу, как мы обрабатываем жалобы пользователей на качество товара в доставке. В процессе обсудим, как нам в этом помогает AI-агент и как мы научили его отвечать честно.
🅿️ Поддержка заводит тикет с тегом и фотографиями
Сотрудник техподдержки открывает…
⭐️ Продолжаем рассказывать про спикеров
19 сентября встретимся на Practical ML Conf — хардовой конференции по практическому применению ML в бизнесе. В программе будет буквально всё: доклады и keynotes, дискуссии, много нетворкинга и отдельная зона с сервисами Яндекса, где можно участвовать в активностях, узнавать про технологии и получать подарки.
Продолжаем знакомить вас со спикерами конференции. В прошлый раз рас…
🥹 Как добавить в умную колонку новые команды и ничего не сломать
Чтобы начать взаимодействовать с умной колонкой, не обязательно говорить активационное слово «Алиса». Вместо этого можно использовать быстрые команды — короткие фразы, с помощью которых можно управлять музыкой, громкостью или умным домом. Например, чтобы переключить трек, достаточно сказать «дальше», а чтобы убавить звук — «тише».
Такие команды делают…
📕 Делимся главными статьями ICRA 2026, генерируем и редактируем картинки в одной модели и внедряем AI в Лавку. Всё это и многое другое — в новом дайджесте Yandex for ML
🚕 Куда сходить
⚪️ 5 сентября — deep tech night. Уже поделились первыми спикерами и их докладами: обсудим переход к генеративным моделям в RecSys, Physical AI, инфраструктуру RL-обучения, современную архитектуру NPU и другие вызовы IT-индустрии в эпо…
🔎 Как мы объединили фичи картиночной Алисы
На связи команда генеративных моделей в компьютерном зрении Поисковых сервисов и ИИ. Вместе с коллегами мы делаем мультимодального ассистента «Алиса AI». У модели Alice AI ART, которая отвечает за визуальную генерацию, есть два базовых сценария:
⚪️ Text-to-Image (T2I) — генерация фото по текстовому описанию
⚪️ Image-to-Image (I2I) — редактирование по картинке с инструкцие…
🍫 AI’изация Лавки: что мы построили и куда идём дальше
Привет! Меня зовут Алёна Зайцева, я руковожу службой AI-сервисов Яндекс Лавки. Ниже расскажу, чем мы занимаемся и что удалось сделать за прошедшее полугодие, а в следующих постах рубрики «Обучено Яндекс Лавкой» мои ребята подробнее расскажут о строительстве платформы, «космолётов» и конкретных агентов.
🅿️ Над чем мы работаем
Наша задача — системно перестраиват…
📖 Обзор статей с ICRA 2026
С 1 по 5 июня в Вене прошла International Conference on Robotics and Automation (ICRA). Это главная международная конференция по робототехнике и автономным системам, и в этом году среди участников были ребята из команды Автономного транспорта Яндекса: Егор Волков, разработчик модели планирования движения, и Максим Спорышев, руководитель службы поведения и предсказания движения.
🔽 Собрали …
🔥 Делимся первыми спикерами на deep tech night
В карточках рассказываем, кто и на какие темы будет выступать на конференции 5 сентября. А чтобы получить доступ к эфиру, задать вопросы экспертам в прямом эфире и первыми посмотреть запись после события, зарегистрируйтесь на сайте.
🔳 Подробности о докладах и форма регистрации
Подписывайтесь:
💬 @Yandex4ML
📹 @YandexML
⚡️ Data Dojo возвращается — встреча ML-комьюнити в Питере
9 сентября в Санкт-Петербурге соберём тех, кто уже владеет основами ML и хочет двигаться дальше. Data Dojo — это живое общение с единомышленниками, разбор реальных кейсов от экспертов Яндекса и погружение в то, что происходит в ML прямо сейчас.
Если вы начинающий ML-специалист — это шанс войти в тусовку, познакомиться с трендами индустрии и получить карьерну…
🔥29👏24❤23👍2😁1
Showing the 12 most recent of 22 posts we hold for @yandexforml. 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.
Posts edited after publishing
@yandexforml edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
2 September 2026
Most recent edit
2 September 2026
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 7 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.
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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@yandexforml named 1 handle 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.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@yandex4ml named in 78 posts, 8 August 2026 – 3 September 2026
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Yandex for Developers @Yandex4Developers · 28,505 Telegram ranks this channel #5 of 94 here — alongside 93 others — read 9 September 2026
Яндекс Образование @Education_Yandex · 37,959 Telegram ranks this channel #8 of 96 here — alongside 95 others — read 31 August 2026
Young&&Yandex @Young_and_Yandex · 111,329 Telegram ranks this channel #9 of 92 here — alongside 91 others — read 14 August 2026
Яндекс нанимает | Вакансии для разработчиков @ya_jobs · 32,756 Telegram ranks this channel #10 of 96 here — alongside 95 others — read 5 September 2026
Machinelearning @ai_machinelearning_big_data · 280,660 Telegram ranks this channel #10 of 95 here — alongside 94 others — read 10 August 2026
Machine learning Interview @machinelearning_interview · 30,308 Telegram ranks this channel #13 of 98 here — alongside 97 others — read 7 September 2026
Stepik – онлайн-курсы @stepik_courses · 24,445 Telegram ranks this channel #14 of 93 here — alongside 92 others — read 16 September 2026
Data Secrets @data_secrets · 93,800 Telegram ranks this channel #19 of 98 here — alongside 97 others — read 17 August 2026
karpov.courses @KarpovCourses · 27,430 Telegram ranks this channel #20 of 96 here — alongside 95 others — read 10 September 2026
Время Валеры @cryptovalerii · 30,829 Telegram ranks this channel #21 of 94 here — alongside 93 others — read 6 September 2026
Код Желтый @kod_zheltyi · 33,061 Telegram ranks this channel #21 of 96 here — alongside 95 others — read 4 September 2026
XOR @xor_journal · 175,669 Telegram ranks this channel #22 of 93 here — alongside 92 others — read 12 August 2026
gonzo-обзоры ML статей @gonzo_ML · 24,323 Telegram ranks this channel #23 of 93 here — alongside 92 others — read 16 September 2026
Поступашки - ШАД, Стажировки и Магистратура @postypashki_old · 45,274 Telegram ranks this channel #27 of 90 here — alongside 89 others — read 28 August 2026
Искусственный интеллект. Высокие технологии @vistehno · 71,491 Telegram ranks this channel #27 of 94 here — alongside 93 others — read 20 August 2026
Data Science @datascienceiot · 42,642 Telegram ranks this channel #28 of 76 here — alongside 75 others — read 29 August 2026
эйай ньюз @ai_newz · 96,889 Telegram ranks this channel #30 of 94 here — alongside 93 others — read 16 August 2026
Sber AI @SberAIScience · 27,279 Telegram ranks this channel #34 of 95 here — alongside 94 others — read 10 September 2026
LLM под капотом @llm_under_hood · 29,146 Telegram ranks this channel #34 of 96 here — alongside 95 others — read 9 September 2026
Сиолошная @seeallochnaya · 79,594 Telegram ranks this channel #35 of 97 here — alongside 96 others — read 19 August 2026
VK Team @vkjobs · 25,707 Telegram ranks this channel #36 of 97 here — alongside 96 others — read 13 September 2026
Ozon Tech @ozon_tech · 29,730 Telegram ranks this channel #36 of 98 here — alongside 97 others — read 8 September 2026
Инжиниринг Данных @rockyourdata · 23,767 Telegram ranks this channel #48 of 93 here — alongside 92 others — read 17 September 2026
Python вопросы с собеседований @python_job_interview · 24,887 Telegram ranks this channel #48 of 90 here — alongside 89 others — read 15 September 2026
This channel appears in 34 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“Yandex for ML” (@yandexforml), 18,854 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/yandexforml.
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.