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 12 September 2026 and assigned it the closest of 31 fixed categories, at 55% 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 the other topics.
Growth
26 measurements spanning 99 days, net -180. 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 5,794–6,028 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 26
Measured (UTC)
Subscribers
Change
13 Nov 2026, 02:30
5,821
-5
8 Nov 2026, 09:12
5,826
-3
5 Nov 2026, 13:36
5,829
-6
2 Nov 2026, 08:26
5,835
-3
30 Oct 2026, 06:01
5,838
-1
26 Oct 2026, 22:25
5,839
-2
24 Oct 2026, 04:20
5,841
-7
21 Oct 2026, 03:45
5,848
-4
17 Oct 2026, 20:27
5,852
-7
14 Oct 2026, 22:58
5,859
-9
11 Oct 2026, 08:37
5,868
-15
5 Oct 2026, 01:57
5,883
-10
25 Sept 2026, 23:00
5,893
-19
12 Sept 2026, 18:16
5,912
-8
8 Sept 2026, 07:58
5,920
-10
3 Sept 2026, 02:05
5,930
-6
30 Aug 2026, 21:02
5,936
-7
28 Aug 2026, 06:56
5,943
-11
25 Aug 2026, 05:06
5,954
-9
22 Aug 2026, 10:49
5,963
first reading
Engagement
32 posts held, back to 16 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 10 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.06%
avg views ÷ 5,821 subscribers
Avg views / post
353
6 posts measured
Reaction rate
1.96%
reactions ÷ views · ER floor
Posts in window
6
of 32 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 5 of 6 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 November 2026
Posts held
32 (16 June 2026 – 3 November 2026)
Views total
2,118
Reactions total
33
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
4 Nov 2026, 16:40 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.
Reaction mix
251 reactions across 30 posts, in 21 distinct kinds. The most used accounts for 44.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
112
44.6%
🔥
41
16.3%
👍
17
6.77%
👀
13
5.18%
🤝
10
3.98%
🥰
9
3.59%
😍
7
2.79%
😱
7
2.79%
👨💻
6
2.39%
🤨
6
2.39%
💯
5
1.99%
👌
4
1.59%
✍
2
0.797%
🌚
2
0.797%
😁
2
0.797%
😢
2
0.797%
🤔
2
0.797%
🍾
1
0.398%
👏
1
0.398%
🙏
1
0.398%
1 further kind
1
0.398%
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 31 of the 32 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 268 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 32 most recent posts we hold, published 16 June 2026 to 3 November 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.
Recent posts
Telegram’s own ‘edited’ marker: 1 of 6 posts held from the last 30 days (16.7%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
📈 Что скрывал советский рынок? Подпольных миллионеров, «левые» бренды и…
Помните, как мечтали о джинсах Montana? А как в то время смотрели на кроссовки с тремя полосками?😍
Советское общество грезило импортными вещами, и некоторые нашли, как на этом заработать.
Разобрали анатомию черного рынка СССР.
Читайте статью, чтобы узнать:
— почему кустарные доработки многими ценились больше, чем советский бренд 😝?
— где п…
🤖 А разговоров-то было… Когда уже AI нас всех заменит?
Искусственный интеллект заметно изменил сферу защиты интеллектуальной собственности.
Агенты уже сейчас берут на себя много процессов:
🔣 патентный поиск. Раньше целый отдел патентных поверенных запирался на пару недель в кабинетах. AI «изучает» нужные объемы данных в сотни раз быстрее.
🔣 мониторинг объектов ИС. Если вы создавали мониторинг в Онлайн Патенте, зна…
🎨 Как защитить продукт от копирования?
Уникальный дизайн в большинстве ниш — это:
1️⃣— конкурентное преимущество;
2️⃣— актив, который может приносить бизнесу прибыль.
Но только в случае, если вы грамотно защитили свой продукт от копирования и можете отстаивать права на него.
📢 28 октября приглашаем на вебинар, где разберем:
➡️ как запатентовать дизайн продукта и получить исключительные права на него
➡️ чем патен…
🚨 Маркетплейсы смогут блокировать карточки без жалоб правообладателей
С 1 марта 2027 года площадки начнут автоматически проверять, законно ли селлеры используют названия.
«Под прицелом» окажутся не только продавцы подделок и бренды-копии.
Проблемы могут возникнуть и у тех, кто придумал название самостоятельно и не мониторит его на наличие товарных знаков.
Например, если существует товарный знак «Зима», то нарушени…
🤫 Запатентованный секрет Майкла Джексона
Помните знаменитый наклон из Smooth Criminal, когда певец подается вперед, нарушая все законы гравитации?
За этим трюком стоит реальное запатентованное изобретение. И один из авторов — сам Майкл Джексон.
В 1993 году он вместе с двумя соавторами получил патент на специальную систему, которая позволяла выполнять этот наклон прямо на сцене.
На иллюстрациях — чертежи из патент…
📅 Выход на зарубежный рынок: где и как защитить свой товар?
Российский патент не гарантирует защиту изобретения за рубежом. А при патентовании в других странах важно учитывать местные правила и особенности процедур.
Зато есть три вопроса, которые стоит решить еще до начала экспорта. И о них мы поговорим на вебинаре 16 октября в 10:45 Мск.
❓ Как грамотно защитить разработку в нужных странах? Есть особенности местн…
🧠 Как сохранить креативные идеи внутри компании?
Два факта, которые важно знать:
➡️ по умолчанию авторские права принадлежат не компании, а создателю.
➡️ один неверно оформленный документ может лишить бизнес разработки, на которую потрачены время, деньги и человеческий ресурс.
Нематериальные продукты скопировать еще проще, чем физические. Поэтому грамотное управление правами на них критично.
14 октября в 9:30 Мс…
❓Нобелевскую премию получает ученый. А кому достается патент?
После мини-квиза вы знаете, для чего ученые могут использовать светочувствительные белки и какие перспективы у подобных разработок.
Но есть еще один интересный вопрос: кому принадлежат права на изобретения нобелевских лауреатов? Ведь автор патента и его правообладатель — не всегда одно и то же лицо.
Мы изучили патенты лауреатов 2026 года и выяснили, как…
🏅 Нобелевская премия — 2026: угадаете, за что наградили ученых?
«Лауреаты заложили основу новой эры в нейробиологии», — написали об этом открытии на сайте комитета.
Но для чего практически могут пригодиться их разработки?
Мы подготовили квиз, чтобы немного рассказать вам об их работах.
Разбираться в нейробиологии необязательно — достаточно любопытства и немного интуиции)
Три вопроса. Попробуете? 👇
❤1
Showing the 12 most recent of 32 posts we hold for @onlinepatent. 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 3 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.
Пользуюсь и мониторингом, и поиском. Спасибо за них🔥18%
Впервые слышу… Конечно нужна инструкция!24%
Мне неактуально12%
Shares as published, totalling 101%. 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 32 most recent posts we hold, published 16 June 2026 to 3 November 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
Republishes
Channels on the register whose posts this channel has forwarded.
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 13 November 2026 — this
entry's latest reading, not the date you are reading this.
“Онлайн Патент — товарные знаки и патенты” (@onlinepatent), 5,821 subscribers as measured 13 November 2026. Telegram Register, tgregister.com/channel/onlinepatent.
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.