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 82% 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
33 measurements spanning 43 days, net -353. 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 13,752–14,211 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
19 Sept 2026, 11:36
13,805
-18
16 Sept 2026, 22:37
13,823
-30
15 Sept 2026, 03:00
13,853
-11
13 Sept 2026, 15:41
13,864
-14
11 Sept 2026, 18:19
13,878
-56
9 Sept 2026, 08:14
13,934
-20
5 Sept 2026, 21:56
13,954
-10
3 Sept 2026, 16:39
13,964
-5
2 Sept 2026, 07:07
13,969
-14
1 Sept 2026, 04:26
13,983
-8
31 Aug 2026, 02:36
13,991
+5
29 Aug 2026, 23:44
13,986
+15
28 Aug 2026, 21:44
13,971
-8
27 Aug 2026, 22:45
13,979
-19
27 Aug 2026, 00:11
13,998
-10
26 Aug 2026, 01:33
14,008
-11
24 Aug 2026, 23:37
14,019
-19
23 Aug 2026, 12:48
14,038
-6
21 Aug 2026, 22:36
14,044
-12
20 Aug 2026, 16:38
14,056
first reading
Engagement
40 posts held, back to 22 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 50 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
4.25%
avg views ÷ 13,805 subscribers
Avg views / post
586
13 posts measured
Reaction rate
2.94%
reactions ÷ views · ER floor
Posts in window
14
of 40 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 1 September 2026
Posts held
40 (22 July 2026 – 1 September 2026)
Views total
7,623
Reactions total
224
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 19:24 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
Video runtime
55m 00s
Average length
3m 40s
Measured directly from 15 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
812 reactions across 39 posts, in 12 distinct kinds. The most used accounts for 40.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
330
40.6%
❤
294
36.2%
💯
62
7.64%
👍
41
5.05%
⚡
28
3.45%
😍
19
2.34%
😁
18
2.22%
🥰
9
1.11%
👏
5
0.616%
🤩
4
0.493%
❤🔥
1
0.123%
🤯
1
0.123%
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 39 of the 40 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 812 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 40 most recent posts we hold, published 22 July 2026 to 1 September 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.
Вы не захотите слышать ЭТО первого сентября 😅
Записала для вас не очень приятный подкаст) Но именно сегодня решается, сколько вы заработаете до Нового года.
– в какие сезоны пик и спад продаж
– что происходит с продажами в сентябре
– как вам заработать до конца 2026 года БОЛЬШЕ, чем за прошлые восемь месяцев
– практика якорения
Ребят, осень – лучшее время делать большие бабки во всех нишах, говорю вам как маркетол…
Поздравляйте! Теперь я продюсер онлайн-проектов Анфисы Чеховой 🥳🔥
За этим стоит не случайное знакомство, а 18 лет в маркетинге.
Моей онлайн-школе уже 9 лет, и за это время наши программы по маркетингу, контенту, личному бренду и нейросетям прошли 70 000+ студентов, а их суммарный заработок составил 940+ млн ₽ 🍋
Я постоянно учусь.
Для меня это лучший источник дофамина.
За плечами красный диплом магистра по маркети…
1️⃣0️⃣2️⃣ 📖📖📖📖
ДЛЯ КОНТЕНТА 🔥
Продолжаю потихоньку выгружать конспекты со своего обучения в Meta (признана экстремистской организацией, запрещена в РФ).
Перед вами банк из 102 идей, которые используют креаторы соцсетей зарубежом. Чтобы вам было проще ориентироваться, я адаптировала все темы на русский язык.
Практически все они подходят под любую нишу. Там найдёте и идеи для Reels, и для каруселей, и для экспертног…
ВИДЕОУРОК №2 🎬
«Секреты съёмки Reels, которые приносят подписчиков и продажи»
– Как выстраивать идеальный кадр дома без профессиональной студии
– Где удобнее и быстрее монтировать ролики
– Как записывать ролики с первого раза без сотни неудачных дублей
Вот базовые правила, которые работают всегда:
1. Свет.
Только дневной. Встаньте лицом к окну — солнце должно светить на вас, а не сзади.
2. Кадр.
Следите за рассто…
ВИДЕОУРОК 🎬
«Контент, который продаёт»
– Как сделать вирусный сценарий Reels за 5-7 минут
– Какие форматы роликов продают в 2026
– Через какие нейронки генерить сценарии на 100 000+ просмотров
– Как анализировать контент конкурентов через ИИ
Здесь кратко продублирую алгоритм создания контента.
ШАГ 1. Выберите нейронку, в которой будете работать. Про особенности каждой из них рассказываю в уроке
• DeepSeek → https…
ВИЗУАЛЬНЫЕ ХУКИ, которые пробивают баннерную слепоту
Как остановить скроллинг без слов? Этому мы научили наших учеников с UGC-Профи. Зацените сами, такие ролики приносят самые большие продажи ☝🏻😏
1. Разрыв паттерна
Начните с кадра, который не вписывается в ожидания зрителя. Человек стоит вверх ногами. Обычный предмет показан крупным планом так, что непонятно, что это.
2. Движение в первом кадре
Статичный кадр в пе…
Что вы точно должны сделать в 2026 году, чтобы расти в доходе в соцсетях?
Ответ: стать UGC-креатором для самого СЕБЯ.
☝🏻 Это Даша Гречишникова.
За один месяц обучения она успела заключить сотрудничества с 5 брендами!
Почему к ней выстраивается очередь из клиентов?
Она научилась снимать и монтировать так, чтобы контент залетал в соцсетях и приносил продажи. И неважно, в какой нише. Хоть вы делаете контент для себя…
30 РАСШИРЕНИЙ ДЛЯ CLAUDE, чтобы выжать максимум из нейронки
Сейчас докажу, что вы пользуетесь только 5% того, что реально умеют нейронки 🤯
Есть штука под названием «расширения». Звучит по-айтишному, но смысл простой: это как подключить к Claude дизайнера, маркетолога, копирайтера, методолога, программиста, личного бизнес-ассистента.
Вот что может делать Claude с помощью расширений вместо вас:
– собирает дизайн в …
ТСССС… а то разбудите! 😴
Давайте шёпотом плиз)
Зачем нам нужны креаторы Instagram (признана экстремистской, запрещена в РФ) если есть ТОПовые свои?
К вам в канал заглянула UGC-креатор Лера. Она родила за 6 дней до старта курса и обучалась прямо с младенцем на руках 🥹
Кто ищет отговорки зарабатывать деньги на контенте – тот всегда найдёт.
А Лера сразу включилась в работу. Она упаковала PR-аккаунт UGC-креатора и по…
🔥7❤6🥰4❤🔥1🤩1😍1
Showing the 12 most recent of 40 posts we hold for @al_trends_jr. 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.
Posts edited after publishing
@al_trends_jr edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
27 August 2026
Most recent edit
27 August 2026
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 3 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.
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.
Седа Каспарова @Seda_Kasparova_news · 257,316 Telegram ranks this channel #15 of 93 here — alongside 92 others — read 10 August 2026
TurboText AI. Нейросети @turbotext_ai · 45,201 Telegram ranks this channel #22 of 80 here — alongside 79 others — read 27 August 2026
Михаил Дементьев. Кинетический интеллект @kintellect · 36,355 Telegram ranks this channel #27 of 90 here — alongside 89 others — read 1 September 2026
Трус Юлия про бизнес @trusprobusiness · 37,739 Telegram ranks this channel #42 of 88 here — alongside 87 others — read 31 August 2026
Chek_Fit @chek_fit · 53,630 Telegram ranks this channel #60 of 76 here — alongside 75 others — read 24 August 2026
This channel appears in 5 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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“🟢 Маркетинг с Родочинской” (@al_trends_jr), 13,805 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/al_trends_jr.
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.