Education — 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 99% 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
31 measurements spanning 43 days, net -220. 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 15,579–15,865 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 31
Measured (UTC)
Subscribers
Change
18 Sept 2026, 21:38
15,612
-12
16 Sept 2026, 12:20
15,624
+3
14 Sept 2026, 13:57
15,621
-5
12 Sept 2026, 21:00
15,626
-23
10 Sept 2026, 21:41
15,649
-17
7 Sept 2026, 21:16
15,666
-6
4 Sept 2026, 21:18
15,672
-15
3 Sept 2026, 05:58
15,687
-7
1 Sept 2026, 21:56
15,694
-4
31 Aug 2026, 23:18
15,698
-1
30 Aug 2026, 20:35
15,699
-6
29 Aug 2026, 18:22
15,705
-5
28 Aug 2026, 18:54
15,710
-6
27 Aug 2026, 15:53
15,716
+5
26 Aug 2026, 12:34
15,711
+4
25 Aug 2026, 16:03
15,707
-11
24 Aug 2026, 17:14
15,718
-8
23 Aug 2026, 02:55
15,726
-6
21 Aug 2026, 13:06
15,732
-12
20 Aug 2026, 12:41
15,744
first reading
Engagement
105 posts held, back to 5 August 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 54 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
5.43%
avg views ÷ 15,612 subscribers
Avg views / post
848
38 posts measured
Reaction rate
2.61%
reactions ÷ views · ER floor
Posts in window
38
of 105 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. It is computed over the 36 of 38 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 3 September 2026
Posts held
105 (5 August 2026 – 3 September 2026)
Views total
32,207
Reactions total
796
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 10:20 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
36m 04s
Average length
1m 48s
Measured directly from 20 videos 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
2,191 reactions across 103 posts, in 24 distinct kinds. The most used accounts for 41.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
910
41.5%
🔥
480
21.9%
❤🔥
264
12.0%
👍
172
7.85%
⚡
92
4.20%
😁
85
3.88%
🎉
34
1.55%
😭
34
1.55%
👎
27
1.23%
✍
17
0.776%
😱
13
0.593%
💔
9
0.411%
💯
9
0.411%
👏
8
0.365%
🤝
8
0.365%
🗿
6
0.274%
😍
5
0.228%
👌
4
0.183%
🤣
4
0.183%
🤷♀
3
0.137%
4 further kinds
7
0.319%
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 103 of the 105 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 2,191 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 105 most recent posts we hold, published 5 August 2026 to 3 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.
Telegram Stars
Stars received
6
across the posts below
Posts paid on
4
of 105 we hold a reading for · 4%
Most on one post
3
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @animationschool_ru. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 105 most recent posts we hold for this entry, published 5 August 2026 to 3 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Поговорили с лауреатом премии «Икар», режиссером и аниматором Кариной Погореловой ✨
Она специализируется на короткометражных фильмах, сейчас работает на фрилансе и сотрудничает со студиями «Союзмультфильм», Petrick и другими. За ее плечами большой опыт: от создания собственного кино до позиции Lead 2D аниматора на «Докторе Динозавров» (студия 8XIII).
Из ее интервью вы узнаете:
🎨 в чем разница между коммерческой ин…
☁️ Что происходит с анимацией после того, как все уже нарисовано и анимировано?
Начинается композитинг 🔥
Именно здесь кадр собирается окончательно: настраиваются свет и цвет, глубина, атмосфера, эффекты — и отдельные элементы начинают работать как единая сцена
Мария Чиркова прошла курс «Композитинг анимации в Adobe After Effects» и собрала шоурил из работ, сделанных во время обучения. Показываем результат 😊
На ку…
☁️ Зачем повторять движение в склейках и как делать это правильно
Когда кадры сменяются очень быстро, зритель может не успеть сразу понять, что произошло. Повторение одного и того же движения помогает связать кадры между собой и сделать действие понятнее ☁️
☁️ Повторяйте ключевое движение: если действие продолжается после склейки, повтор движения помогает зрителю быстро сориентироваться
☁️ Сохраняйте знакомую трае…
«С чего начать сценарий?»
«Работает ли моя идея?»
«Получилась ли арка персонажа?»
Если у тебя тоже накопился список вопросов по сценаристике или режиссуре — мы поможем.
Дима Шрамко — сценарист, режиссер, основатель студии LETO и преподаватель курса «Сценарное мастерство». Он ответит на вопросы в прямом эфире и поможет решить твой затык.
Присылай вопросы заранее, все разберем на стриме 6 сентября в 17:00 мск — [фор…
КОНКУРС
В честь долгожданного выхода артбука по нашему проекту «MMA.Despite you» — небольшой конкурс!
Приз: разыгрываю 3 артбука!
Что нужно сделать: напишите описание суперспособности любого выдуманного бойца.
• Главное, распишите, как работает способность: что делает, как ей пользоваться, есть ли у нее особые условия — все, что посчитаете важным.
• Не забудьте подумать о том, как навык отражается на владельце в…
☁️ 26 и 27 сентября открываем двери школы: зовем всех, кто присматривается к анимации
Два дня будем заглядывать по ту сторону экрана и разбираться, как на самом деле устроен вход в профессию: что нужно уметь, с чего начинать и где чаще всего спотыкаются новички ☁️
⭐️В субботу — 3D: Maya, Blender, мультфильмы, игры и кино
⭐️В воскресенье — 2D: классическая анимация, перекладка, концепт-арт и композитинг
Сначала св…
Даже если мы давно не ходим в школу, каждую осень ждем примерно одного и того же: новых целей, свежих идей и мотивации учиться 🍂
Мы решили заглянуть немного вперед и узнать, что ждет нас этой осенью. Тыкай на любую карточку — там твой девиз на ближайшее время
Сохрани его и возвращайся, когда дождь и серость начнут побеждать мотивацию. И тогда этой осенью у тебя все получится! ☁️
Подписаться на Animation School
☀️ Вот оно какое, наше лето...
Обернулись назад, чтобы вспомнить, каким было наше лето. Оно пролетело быстро, но главное мы успели — встретиться с вами в летней столице и выпустить крутую анимацию 😊
Вот что точно останется с нами надолго:
⭐️Слет аниматоров — собрали больше полутора тысяч креативных людей, чтобы обнять коллег, обменяться энергией и вдохновением
⭐️Открыли новое направление в школе — самые внимательн…
🏆 Как сделать сильное тестовое в Spine?
Разбираемся вместе с Михаилом Гибертом — лидом 2D анимации и ментором курса «Аниматор в Spine — от основ до портфолио». Михаил рассказывает, на что обращают внимание студии, какие ошибки чаще всего допускают кандидаты и как собрать работу, которая действительно демонстрирует ваш уровень ☁️
⭐️Прокачивай навыки и собирайте сильное портфолио вместе с нами на курсе: https://clck.…
🔥 Есть победитель!
Поздравляем Александру Алейникову — ей достается артбук по «Аркейну» и скидка 20% на первый месяц курса «Знакомство с Moho: освоение софта и первые анимации». Завтра свяжемся с вами 🫶
А еще наш ментор Антон Прошин отдельно отметил Настю Бородихину и подготовит для нее подарок от себя ☁️
Спасибо за ваши работы! На этой неделе соберем всех и обязательно опубликуем
⭐️А если хочется прокачать анима…
Заглянем в гости к Антону Прошину — 2D аниматору и супервайзеру анимации на «Абдукции» 👀
Как выглядит рабочее место мультипликатора? Какую технику, мебель и гаджеты Антон использует каждый день, почему выбрал именно их и сколько всё это стоит?
Антон расскажет в новом видео на YouTube 🔥
⭐️А если хочется не только подсмотреть за рабочим процессом, но и самому освоить 2D анимацию в Moho — присоединяйся к курсу: https…
❤12🔥4👍2
Showing the 12 most recent of 105 posts we hold for @animationschool_ru. 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
@animationschool_ru 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
13 August 2026
Most recent edit
13 August 2026
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Внезапный опрос: выбираем лучшего «Призрака в доспехах»?
Фанатею от классики 95-го года59%
Готов(а) пересматривать «Невинность»22%
Ценю «Синдром одиночки» за раскрытие майора Кусанаги15%
Убеждаю всех, что «Синдром одиночки 2045» удалась4%
Люблю экранизацию с шикарной Йоханссон26%
Шарю за «Альтернативную архитектуру»7%
Выскажусь в комментах 😎11%
The shares total 144%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 105 most recent posts we hold, published 5 August 2026 to 3 September 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
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 1 registered channel — 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.
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.
CGSpeak @cgspeaks · 43,026 Telegram ranks this channel #58 of 72 here — alongside 71 others — read 28 August 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. 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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“Animation School: анимация на практике” (@animationschool_ru), 15,612 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/animationschool_ru.
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.