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 22 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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
4 measurements spanning 31 days, net +3. 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 368–374 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
19 Sept 2026, 04:57
373
+4
11 Sept 2026, 05:21
369
-1
19 Aug 2026, 15:29
370
no change
19 Aug 2026, 13:45
370
first reading
Engagement
16 posts held, back to 21 November 2025 — 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 page of 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 16 posts for this entry, the most recent from 21 December 2025. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
15s
Average length
15s
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
91 reactions across 11 posts, in 8 distinct kinds. The most used accounts for 37.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
34
37.4%
👍
26
28.6%
🔥
21
23.1%
😁
4
4.40%
🎄
3
3.30%
👏
1
1.10%
😱
1
1.10%
🤔
1
1.10%
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 13 of the 16 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 91 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 21 November 2025 to 21 December 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🎄 Вустер составляет киномарафон!
Пока вы наряжаете елку, я выбрал ТОП-5 фильмов, которые прокачают ваш английский до блеска гирлянды! И все они — про зиму, праздники и... таких милых созданий, как я! 😼
🎄«The Grinch» — учим слова про ворчание и плохое настроение (моё утро без корма!)
🎄«Home Alone» — осваиваем крики «А-а-а!» и «Мама!» на английском
🎄«Frozen» — запоминаем, как попросить «отпусти и забудь» (прошлые …
🍿 Паника перед живой речью? История Леры доказывает: кино лечит!
На картинках путь Леры. Между этими двумя точками — не только учебники, а кино и сериалы, которые помогли ей развить насмотренность и повысить мотивацию😼
P. S. Пока все пишут списки Деду Морозу, я готовлю для вас особенный новогодний сюрприз — подборку фильмов и мультиков для уровней A2-B1!🎄
➡️Теперь вопрос к вам: А какой фильм или сериал стал вашим …
😼 Вустер раскрывает секрет!
Знаете, как я готовлюсь к важным переговорам о двойной порции корма? По чёткому плану! Сегодня научу вас той же стратегии для устного экзамена. Ловите универсальную структуру, а также чек-лист устного ответа🎤
Да прибудет с вами удача🙏
The shares total 264%, 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.
Сдаю свою психику в химчистку
Новый год — лучшее время не только для новых целей, но и для того, чтобы наконец постирать и вывесить на солнце то, что отягощает. А что вы отправите в стирку и больше не заберете в этом году?
⏺Comparing myself → Сравнение себя с другими
⏺Avoiding my emotions → Избегание своих эмоций
⏺Dwelling on mistakes → Зацикленность на ошибках
Feeling bad for crying → Стыд за свои слезы
⏺People pl…
🐾 Внемлите, двуногие слуги! Сие Великий Кот Вустер снизошел до вашего уровня, дабы испытать ваш разум
Готовы ли вы пройти Испытание Усатого Соцсетевéда? Сможете ли отличить «лайк» от «взаимодействия», или ваши мозги уснули, как я на вашем ноутбуке?
Котоитоги: как мы пережили ноябрь и не сошли с ума (зато сошли с проторенной дорожки зубрёжки) 🐱
⏺Мы разобрались, почему песня живёт в голове rent-free
⏺Научились не гладить кота утюгом (iron ≠ pet!),
⏺Похоронили зубрёжку и перешли на ассоциации, эмоции и интервальные повторения и узнали котьи техники запоминания слов
⏺Разобрали дорожные идиомы
⏺И даже выяснили, почему cow превращается в beef
⏺А закончили месяц соцс…
The shares total 136%, 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.
Showing the 12 most recent of 16 posts we hold for @ive_oclock_with_Lugovaya. 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.
Polls
The 6 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.
The shares total 264%, 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.
The shares total 136%, 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 16 most recent posts we hold, published 21 November 2025 to 21 December 2025. 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.
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.
Черчилль: Английский с гарантией результата @immersion_club · 75,776 Telegram ranks this channel #24 of 86 here — alongside 85 others — read 19 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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“Английский с усами” (@ive_oclock_with_Lugovaya), 373 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/ive_oclock_with_Lugovaya.
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.