Local community — 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 23 September 2026 and assigned it the closest of 31 fixed categories, at 52% 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
6 measurements spanning 40 days, net +12. 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 280–297 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
18 Sept 2026, 17:56
295
+2
9 Sept 2026, 23:56
293
+2
25 Aug 2026, 22:28
291
+5
18 Aug 2026, 07:56
286
+4
10 Aug 2026, 13:12
282
-1
9 Aug 2026, 21:45
283
first reading
Engagement
17 posts held, back to 27 May 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 17 posts for this entry, the most recent from 9 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
5m 01s
Average length
33s
Measured directly from 9 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
212 reactions across 16 posts, in 16 distinct kinds. The most used accounts for 21.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
45
21.2%
custom 5402432687812126781
22
10.4%
custom 4945344920798889360
18
8.49%
custom 5262598529863397825
18
8.49%
🔥
18
8.49%
🤩
15
7.08%
custom 4944982021832181080
13
6.13%
custom 5893170776265200786
12
5.66%
custom 5447579318148429606
11
5.19%
custom 5352843278880359118
10
4.72%
😍
9
4.25%
❤🔥
8
3.77%
🥰
4
1.89%
🍓
3
1.42%
🎉
3
1.42%
👍
3
1.42%
Custom emoji. 7 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them are Telegram’s.
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 17 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 212 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 17 most recent posts we hold, published 27 May 2026 to 9 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.
🌺 1 августа в нашей кофейне прошёл очередной мастер-класс по точечной росписи для взрослых.
Гости освоили базовые приёмы и нарисовали красивые узоры на тарелочках.
☕️ Мастер-класс прошёл в творческой атмосфере за чашечкой ароматного кофе и вкусной выпечкой.
❤️ Благодарим всех за участие и ждём вас на наших следующих творческих встречах!
🌺Наш мастер-класс по точечной росписи, который прошёл 25 июля, собрал вместе и детей, и взрослых. Было очень интересно, весело и вкусно! Этот день подарил всем нам море положительных эмоций!
Спасибо всем, кто был с нами! Ваши улыбки — лучшая награда! Ждём вас снова!
☕️ Что ты закажешь сегодня?
❤️Сделай скриншот и получи 15% скидку на выбранный напиток при заказе.
❕ Акция со скидкой за скриншот действует каждое воскресенье.
🌺 1 августа в нашей кофейне пройдёт мастер-класс по точечной росписи для взрослых.
Идеальный повод отдохнуть, выпить чашку вкусного кофе и создать подарок, который будет долго радовать вас.
Ждём в гости!
🌸 25 июля в нашей кофейне прошёл мастер-класс по точечной росписи. Улыбки детей и родителей, звонкий смех, вкусная выпечка, ароматный кофе и невероятное творчество! Было очень весело, душевно, радостно и уютно. Спасибо всем, кто разделил этот день с нами!
☀️25 июля в нашей кофейне состоится мастер-класс по точечной росписи для детей от 6 до 14 лет!
Мы не только интересно проведём время, но и создадим своими руками уникальный подарок!
Ждём вас!
The shares total 105%, 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.
Вчера, 20 июня, в нашей кофейне было особенно оживлённо!🔥
Мы объединили два источника энергии: бодрящий кофе☕️ и спортивный дух🚴.
Благодарим всех участников и ждём вас снова в гости!🥐
#laruche_coffeeride
Showing the 12 most recent of 17 posts we hold for @la_ruche_viennoserie. 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 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.
The shares total 105%, 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 17 most recent posts we hold, published 27 May 2026 to 9 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.
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 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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“la ruche | Венская выпечка | Энгельс” (@la_ruche_viennoserie), 295 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/la_ruche_viennoserie.
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.
