9 measurements spanning 7 days, net -15. 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 47,382–47,414 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 23:34
47,395
+2
11 Aug 2026, 22:42
47,393
+7
10 Aug 2026, 23:46
47,386
-9
9 Aug 2026, 22:32
47,395
+4
9 Aug 2026, 02:01
47,391
-2
7 Aug 2026, 23:35
47,393
-12
6 Aug 2026, 23:14
47,405
-5
6 Aug 2026, 01:46
47,410
no change
5 Aug 2026, 23:41
47,410
first reading
Engagement
22 posts held, back to 5 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 18 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
9.03%
avg views ÷ 47,395 subscribers
Avg views / post
4,280
8 posts measured
Reaction rate
1.25%
reactions ÷ views · ER floor
Posts in window
8
of 22 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.
What these figures were computed from
Window
Rolling 30 days · latest post in window 10 August 2026
Posts held
22 (5 June 2026 – 10 August 2026)
Views total
34,230
Reactions total
428
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
13 Aug 2026, 08:29 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
Photos
62
Videos
5
Links
282
Lifetime counters from Telegram’s own channel header, read 13 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
1,199 reactions across 21 posts, in 2 distinct kinds. The most used accounts for 93.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
1,119
93.3%
👎
80
6.67%
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 22 of the 22 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 1,274reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 5 June 2026 to 10 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.
Telegram Stars
Stars received
8
across the posts below
Posts paid on
5
of 22 we hold a reading for · 23%
Most on one post
3
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ThinkCritical. 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 22 most recent posts we hold for this entry, published 5 June 2026 to 10 August 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.
ТОЧНОСТЬ ФОРМУЛИРОВОК
Наш умозаключающий механизм, которым мы пользуемся в повседневной жизни, не приспособлен к сложной среде, где высказывание радикально меняется при малозаметном изменении формулировки. Ведь, если подумать, в первобытной среде нет важной разницы между высказываниями «большинство убийц — дикие звери» и «большинство диких зверей — убийцы». Неточность тут есть, но она не слишком важна. Наша статисти…
ЭФФЕКТ ПРИВЛЕКАТЕЛЬНОСТИ ГРУППЫ
или как скрывать недостатки внешности
Когда мы видим группу людей, наш мозг усредняет их черты лица, не давая заметить мелкие недостатки внешности. Поэтому каждый человек кажется из-за этого более привлекательным. Однако, группы также могут быть более или менее привлекательными. Группа из 3-х блондинок и 3-х брюнеток будет восприниматься привлекательнее, чем группа только из 6-ти блон…
На сегодня самый актуальный и показательный для большинства населения в любой стране не тот IQ, который intelligence quotient [коэффициент интеллекта], а IgQ/IrQ — ignorance quotient [коэффициент невежества] / irrationality quotient [коэффициент иррациональности]. Вот что надо измерять и учитывать в первую очередь.
Евгений Волков, проект «Корни»
РАССУЖДЕНИЯ ПОД ВЛИЯНИЕМ ЭМОЦИЙ
Наши рассуждения могут становиться алогичными под влиянием эмоций. Это верно для представителей всех слоев общества, даже для судей Верховного суда США. Когда судья Уильям Дуглас начинал работать в Верховном суде, председатель Верховного суда Чарльз Эванс дал ему следующий совет: «Вы должны помнить одну вещь. На конституционном уровне, на котором мы работаем, девяносто процентов всех …
ФРЕЙМИНГ В РЕКЛАМЕ
Все вы наверняка слышали фразы типа «Четверо стоматологов из пяти рекомендуют зубную пасту Colgate». И это правда. Рекламное агентство, стоящее за этим существующим на протяжении многих лет слоганом, хочет донести до вас мысль, что стоматологи предпочитают Colgate всем другим брендам. Но это не так.
Комитет рекламных стандартов Великобритании изучил утверждение слогана и счел его нечестным. Выясн…
ПРОКЛЯТИЕ ЗНАНИЯ
или почему мы не понимаем намеки
Более информированным людям чрезвычайно сложно [если вообще возможно] рассматривать какую-либо проблему с точки зрения менее информированных людей.
Примеры:
• намёки сложно понять не потому что собеседник туп или глуп, а просто автор намека не понимает, как его можно не понять;
• тяжело объяснить клиенту, почему «ремонт простой неисправности такой долгий и дорогой»;…
КЛАССЫ УТВЕРЖДЕНИЙ
Там, где аристотелевская логика признает лишь два класса утверждений «истинное» и «ложное», — пост-копенгагенистская наука склонна признавать четыре: «истинное», «ложное», «неопределенное» [пока еще непроверяемое] и «бессмысленное» [в принципе непроверяемое].
Роберт Уилсон, из книги «Квантовая психология»
ДАЙТЕ ПРАВИЛЬНЫЙ ОТВЕТ
или насколько мы подвержены когнитивным искажениям
Вы подбрасываете монету 50 раз и каждый раз записываете результат, а ваш приятель просто пишет последовательность орлов и решек длиной в 50 букв. Выберите, какую последовательность получил вы:
a) РРРООРООРРРРРОООРРОООРРРРРОООООООРОООРОООРРОООООРР
b) РОРОРОРОРРРООРОРОРРОРООРООРРОРОРООРООРРРОРОРОРОРОР
Узнать правильный ответ:
telegra.ph/Nasko…
КРИТИКА В КРИТИЧЕСКОМ МЫШЛЕНИИ
Концепт «критическое мышление» можно сравнить с концептом «колесный транспорт». Колеса в последнем являются фундаментально определяющим и конструирующим элементом. Критика в критическом мышлении выполняет ровно такую же роль: что бы к колесам ни прикручивали сверху, без них эти надстройки транспортом не будут. Так же и с критикой в критическом мышлении: без критики как постоянно работа…
ПАРЕЙДОЛИ́Я
или почему мы видим лица там, где их нет
Это иллюзорное восприятие в основе которых выступают самые обычные объекты. В ходе эволюции мы научились быстро распознавать всевозможные объекты, чтобы обезопасить себя, оценивая окружающую обстановку. Этот механизм дошёл до автоматизма.
Пример: при разглядывании облаков, деревьев, переплетений корней, игр теней видятся образы людей, дворцов, животных, мифически…
ЗНАНИЕ И НЕВЕЖЕСТВО
Каждое решение некоторой проблемы порождает новые нерешённые проблемы, более глубокие по сравнению с первоначальной проблемой и требующие более смелых решений.
Чем больше и глубже мы погружаемся в изучение мира, тем более осознанным и точным становится наше знание о том, чего мы не знаем, знание нашего невежества. В самом деле, основной источник нашего невежества заключен в том факте, что наше з…
👍62👎1
Showing the 12 most recent of 22 posts we hold for @ThinkCritical. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Citation-graph rank
Citation-graph rank — 313,833 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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 5 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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“Критическое мышление” (@ThinkCritical), 47,395 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/ThinkCritical.
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.