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 10 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
36 measurements spanning 69 days, net -23. 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 23,100–23,175 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 36
Measured (UTC)
Subscribers
Change
13 Oct 2026, 21:41
23,133
-11
10 Oct 2026, 10:20
23,144
-2
6 Oct 2026, 09:57
23,146
+8
1 Oct 2026, 11:36
23,138
+5
18 Sept 2026, 09:41
23,133
+3
14 Sept 2026, 11:56
23,130
+4
12 Sept 2026, 19:17
23,126
+3
10 Sept 2026, 17:00
23,123
+4
7 Sept 2026, 16:40
23,119
+5
4 Sept 2026, 16:38
23,114
+5
3 Sept 2026, 02:35
23,109
-8
2 Sept 2026, 01:35
23,117
+3
31 Aug 2026, 23:38
23,114
-6
30 Aug 2026, 21:25
23,120
+1
29 Aug 2026, 21:17
23,119
+2
28 Aug 2026, 22:06
23,117
-7
27 Aug 2026, 23:44
23,124
-4
26 Aug 2026, 22:14
23,128
+1
25 Aug 2026, 20:11
23,127
-4
24 Aug 2026, 22:17
23,131
first reading
Engagement
67 posts held, back to 14 July 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 90 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.38%
avg views ÷ 23,133 subscribers
Avg views / post
1,480
19 posts measured
Reaction rate
1.79%
reactions ÷ views · ER floor
Posts in window
19
of 67 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 14 October 2026
Posts held
67 (14 July 2026 – 14 October 2026)
Views total
28,046
Reactions total
501
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
14 Oct 2026, 18:14 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
≈2,750
Videos
≈353
Links
≈1,490
Lifetime counters from Telegram’s own channel header, read 14 October 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
47m 12s
Average length
7m 52s
Measured directly from 6 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
1,729 reactions across 67 posts, in 11 distinct kinds. The most used accounts for 29.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
513
29.7%
❤
485
28.1%
👍
480
27.8%
😁
104
6.02%
🎉
64
3.70%
🥰
39
2.26%
💯
26
1.50%
😍
8
0.463%
🤔
8
0.463%
👎
1
0.058%
🤮
1
0.058%
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 67 of the 67 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 1,729 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 67 most recent posts we hold, published 14 July 2026 to 14 October 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
75
across the posts below
Posts paid on
28
of 67 we hold a reading for · 42%
Most on one post
20
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @ProProfiIing. 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 67 most recent posts we hold for this entry, published 14 July 2026 to 14 October 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.
🚀 Вчера у нас стартовал сертификационный курс по профайлингу!
🧠 Мы за актуальные знания и практику XXI века — никаких устаревших концепций и сомнительных теорий, только современные и работающие инструменты.
Что Вас ждет на обучении:
📌 15 тем и регулярные практические занятия для отработки навыков.
📚 Более 6 000 содержательных слайдов и огромная база материалов строго по теме.
🎯 Уникальная видео- и фотобаза для раз…
На прошлой неделе был аж на 2х масштабных мероприятиях в Сочи.
На первом хоть и недолго, но очень приятно – 27-й Сочинской конференции полиграфологов. Успели пообщаться с коллегами, обменяться новостями и планами. Огромная благодарность Николаевой И.Н. за традиционно отличную организацию столь значимого мероприятия.
Второе – наш сёрчевский форум по информационной безопасности и противодействию рискам. Там снова рас…
🎓 До старта Сертификационного курса по профайлингу и детекции лжи – 2 дня!
Уже 13 октября 2026 года стартует Сертификационный курс Алексея Филатова по профайлингу и детекции лжи.
Это полный глубокий курс изучения профайлинга и детекции лжи.
В программе – 15 базовых тем, каждая включает теоретическую и практическую часть.
По результатам прохождения курса вы можете получить диплом о профессиональной переподготовке …
Внимание!
Научно доказано, что имитация бурной деятельности впечатляет начальника больше, чем реальные результаты).
Пользуйтесь). Теперь о доказательствах.
Иногда карьера зависит не только от того, насколько хорошо вы работаете. Но и от того, насколько хорошо выглядит то, как вы работаете.
В начале 2026 года Klaus Moser и коллеги опубликовали метаанализ связи самомониторинга с профессиональной эффективностью.
Сам…
Хватит изучать теорию – пора переходить к практике!
Приглашаем вас отточить навыки определения лжи под руководством Алексея Филатова – профессионального профайлера, эксперта в области поведенческого анализа и детекции лжи.
⏰ 7 октября в 20:00 Алексей Филатов проведет практикум по профайлингу «Как определить ложь по словам, мимике и жестам».
Только научная информация, современные методы распознавания лжи и, главное…
И традиционная пятничная иллюзия)
Цветовая стереофония.
Красный цвет на этой «мишени» воспринимается как более близкий к наблюдателю, хотя физически это не так. Во многом потому, что красное для нас более приоритетное, чем синее.
Желаю всем четкого понимания своих иллюзий, правильных приоритетов и отличных выходных!
#мозг, #обманзрения, #восприятие, #неоднозначность, #иллюзии, #профайлинг, #ProProfiling, #Филатов,…
Эмоции и провокации в детекции лжи.
В профайлинге существует одна довольно занятная быстрая методика выявления лжи, одна из моих любимых. Суть – эмоциональная провокация в ответ на лживую или правдивую информацию с последующей оценкой вызванной реакции. И очередной провокацией.
Идейный вдохновитель этой методики – Пол Экман, - один из наиболее авторитетных мэтров изучения эмоций, верификации лжи и кстати, - консуль…
13) Скрипты, опросные беседы и расследования: правила составления вопросов в детекции лжи для целей расследования и проведения интервью. Детекция лжи в деловой коммуникации и в практике профайлинга. Инструменты получения признания и проведения расследования.
14) Темная триада личности: психопаты, манипуляторы и нарциссы. Инструменты оперативного определения и управления коммуникацией с психопатом. Ложь психопатов и …
СЕРТИФИКАЦИОННЫЙ КУРС ПО ПРОФАЙЛИНГУ И ДЕТЕКЦИИ ЛЖИ
Друзья, напоминаю что, уже через пару недель, 13-го октября 2026 года стартует мой Сертификационный курс по профайлингу и детекции лжи!
Это полный курс изучения профайлинга и детекции лжи, в ходе которого мы детально, на практике изучим все важнейшие темы профайлинга. А это, помимо точечных лайфхаков и приемов – 15 базовых тем. Каждой теме посвящена теоретическая …
Сегодня стартовая первая в этом учебном году группа по выявлению и профилактике групп риска среди подростков в ИДПО работников социальной сферы.
Это уже четвертый год моей работы в этой области. Вместе с командой профессионалов обучаем социальных работников, психологов и учителей тому, как на ранних этапах понять, что конкретный подросток оказался в группе риска по употреблению психоактивных веществ, деструктивному …
Вчера и сегодня на конференции полиграфологов и профайлеров «Легенда»!
Отличная программа, великолепные спикеры, прекрасное общение с коллегами и друзьями и безупречная организация мероприятия!
И традиционный пятничный пост.
Напоминаю, что важнейшей темой профайлинга является то, как мы воспринимаем, оцениваем и используем информацию. И с какими барьерами и особенностями мы при этом сталкиваемся.
Видео демонстрирует иллюзию перспективы. Перспектива важна, но иногда – она лишь кажущаяся.
Всегда оценивайте то, как текущие события повлияют на будущее, на перспективу. И не позволяйте иллюзиям в это вмешивать…
👍19❤7🔥4
Signed Aleksei Filatov
Showing the 12 most recent of 67 posts we hold for @ProProfiIing. 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.
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 4 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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Академия исследования лжи (StudyLie) @studylie · 4,976#1
Профайлинг и психология | Светлана Грунина @ano_niipp · 4,170#2
Read from Telegram’s recommendation API, most recently 19 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
Психология @PsyEducation · 59,109 Telegram ranks this channel #17 of 95 here — alongside 94 others — read 22 August 2026
Критическое мышление @ThinkCritical · 47,157 Telegram ranks this channel #26 of 97 here — alongside 96 others — read 26 August 2026
Михаил Дементьев. Кинетический интеллект @kintellect · 36,111 Telegram ranks this channel #31 of 90 here — alongside 89 others — read 1 September 2026
Менталист @mentalroom · 22,565 Telegram ranks this channel #46 of 84 here — alongside 83 others — read 20 September 2026
Татьяна Черниговская @tvchernigovskya · 110,749 Telegram ranks this channel #64 of 90 here — alongside 89 others — read 20 August 2026
«СУТЬ ВЕЩЕЙ» | Центр интеллектуальных практик Андрея Курпатова @myshlenie_kurpatov · 42,944 Telegram ranks this channel #70 of 89 here — alongside 88 others — read 28 August 2026
This channel appears in 6 seed channels' Telegram-generated recommendation lists 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 13 October 2026 — this
entry's latest reading, not the date you are reading this.
“Профайлинг, нейротехнологии и детекции лжи” (@ProProfiIing), 23,133 subscribers as measured 13 October 2026. Telegram Register, tgregister.com/channel/ProProfiIing.
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.