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 19 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
7 measurements spanning 37 days, net -18. 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 939–963 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Sept 2026, 22:21
942
-4
10 Sept 2026, 16:22
946
-8
31 Aug 2026, 20:18
954
-3
25 Aug 2026, 13:23
957
-2
19 Aug 2026, 11:08
959
-1
12 Aug 2026, 03:45
960
no change
12 Aug 2026, 03:31
960
first reading
Engagement
16 posts held, back to 22 June 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 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 11 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
1m 35s
Average length
48s
Measured directly from 2 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
110 reactions across 16 posts, in 4 distinct kinds. The most used accounts for 75.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
83
75.5%
🔥
23
20.9%
👍
2
1.82%
😍
2
1.82%
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 16 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 110 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 22 June 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.
Past Simple без нудних таблиць та зубріння! 🚀✨
Колеги, знаю, як важко дітям даються форми дієслів 🥲
Впевнена, ці інтерактивні розробки змусять учнів просити ще!
Що всередині набору:
🍦 Ice Cream Builder & Tic-Tac-Toe: збираємо морозиво з 3 форм irregular verbs та граємо в хрестики-нулики.
🪐 Space Quest: загартовуємо spelling правильних дієслів, розбираємо звуки /t/, /d/, /id/ з прибульцями та рятуємо слова з Чорної д…
І тут важливо діяти покроково: спочатку м'яко нагадати дієслова, а вже потім виводити їх у розмову ☀️
Ми спочатку тренуємо та відпрацьовуємо Past Simple (regular & irregular verbs) через інтерактивні завдання, і одразу після цього учні застосовують їх у розповідях про свої канікули🍉
🧳 Чітка система, жодного зазубрювання та повністю готовий матеріал, який заощадить ваш час на підготовку👩💻
Зазираємо всередину? ⬇️
Але зізнайтеся, після канікул завжди постає одна й та сама проблема… 😅
Діти приходять переповнені яскравими враженнями, емоціями, хочуть наперебій розповісти про своє літо…
…і раптово забувають геть усі дієслова в Past Simple! 🙈💭
Замість нормальної розповіді ми чуємо щось типу: «I go to the beach… I see a dolphin…»
Щоб на першому уроці не влаштовувати дітям нудний допит, тримайте вже готове колесо фортуни☀️
Всередині - 12 легких, кумедних і дуже дитячих запитань про літо, які розганяють розмову за 2 хвилини 🔝
Крутимо колесо, відповідаємо на кумедні питання про літо, сміємося та вмикаємося в урок без стресу🍦🍉
Все вже зібрано, оформлено й ready to use!
📲 Клікайте та зберігайте собі: link
Як замовити їжу та не розгубитися?🍔
Готовий урок "At the Restaurant"!
Практичне занурення в тему замовлення їжі, ресторанного етикету та розмовної мови. Матеріал побудований так, щоб крок за кроком вивести учнів у вільне говоріння без нудної теорії.
Що чекає всередині :
🍟Discussion: живий Warm-up для розігріву та обговорення ресторанного досвіду.
🍟Vocabulary & Matching: вивчаємо та закріплюємо важливу лексику (Appet…
Monster Body Parts 👻
Шукаєте яскравий Warm-up або розмовну гру для 1–4 класів? Вчити частини тіла з кольоровими монстриками — це завжди 100% залучення!
Що відпрацьовуємо на уроці:
✨ Vocabulary & Counting: частини тіла + числівники (three eyes, four arms).
✨ Speaking & Practice: один учень описує монстрика («He has got 4 big arms and a blue tail!»), а інші вгадують або малюють його.
✨ Grammar: непомітно відпрацьовує…
Warm-up після літа: розмовне колесо Wordwall ☀️🎡
Ідеальна вправа для перших уроків вересня, яка виконує одразу 3 завдання:
🍒Знімає мовний бар'єр після перерви
🍒Допомагає дізнатися, як пройшли канікули
🍒Повторює вживання Past Simple та правильних/неправильних дієслів
3 варіанти гри на уроці:
🍉Teacher to Student: Вчитель крутить колесо та ставить питання учню (Did you read any books?)
🍉Student to Student: Учні працюю…
A Trip to Montenegro 🇲🇪🧳
Практичний інтерактивний урок, який відправить ваших учнів у віртуальну подорож та закохає у граматику!
🎥 Дивіться відео-огляд вище, щоб зазирнути всередину та оцінити дизайн і наповнення☀️
📌 Для кого: рівень A2–B1⏳ Тривалість: 45–60 хв 💻 Формат: Canva
💸 Вартість: 99 грн 📩 Придбати урок: @julia_demchenko44
🔥4
Signed Julia
Showing the 12 most recent of 16 posts we hold for @bunnyland_english. 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 2 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.
Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is 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 22 June 2026 to 11 August 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.
Mentions
Named by 2 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.
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.
“Bunnyland🐰🇬🇧” (@bunnyland_english), 942 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/bunnyland_english.
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.
