Technology — 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 11 September 2026 and assigned it the closest of 31 fixed categories, at 48% 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
23 measurements spanning 87 days, net -87. 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 9,494–9,684 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 23
Measured (UTC)
Subscribers
Change
2 Nov 2026, 21:19
9,575
+9
31 Oct 2026, 03:08
9,566
+38
28 Oct 2026, 02:39
9,528
-5
24 Oct 2026, 16:55
9,533
+2
21 Oct 2026, 15:07
9,531
+2
18 Oct 2026, 09:56
9,529
-2
15 Oct 2026, 06:19
9,531
+14
5 Oct 2026, 19:37
9,517
+1
28 Sept 2026, 11:17
9,516
-21
18 Sept 2026, 21:00
9,537
-6
14 Sept 2026, 03:58
9,543
-11
10 Sept 2026, 17:38
9,554
-23
5 Sept 2026, 17:38
9,577
-17
1 Sept 2026, 08:28
9,594
-11
29 Aug 2026, 14:17
9,605
-6
26 Aug 2026, 18:02
9,611
+7
23 Aug 2026, 09:12
9,604
+2
19 Aug 2026, 10:03
9,602
-12
16 Aug 2026, 17:44
9,614
-21
13 Aug 2026, 02:58
9,635
first reading
Engagement
20 posts held, back to 7 July 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 40 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
2.72%
avg views ÷ 9,575 subscribers
Avg views / post
260
2 posts measured
Reaction rate
2.21%
reactions ÷ views · ER floor
Posts in window
2
of 20 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 1 of 2 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 4 November 2026
Posts held
20 (7 July 2025 – 4 November 2026)
Views total
520
Reactions total
5
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
5 Nov 2026, 09:28 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
43
Videos
3
Links
22
Lifetime counters from Telegram’s own channel header, read 5 November 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.
Video runtime
1m 33s
Average length
47s
Measured directly from 2 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
108 reactions across 18 posts, in 7 distinct kinds. The most used accounts for 51.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
56
51.9%
❤
17
15.7%
🔥
16
14.8%
🤯
10
9.26%
👏
5
4.63%
🤔
3
2.78%
😁
1
0.926%
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 18 of the 20 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 108 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 7 July 2025 to 4 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: 0 of 2 posts held from the last 30 days (0%) 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).
👀 Dzine — целая ИИ-студия для создания визуального контента
ㅤ
Dzine — это не просто генератор изображений или видео, а полноценная творческая студия.
В одном пространстве собраны инструменты для генерации, редактирования и улучшения материалов.
ㅤ
—> Что умеет:
ㅤ
• Генерирует и редактирует изображения.
• Создаёт видео по тексту и изображениям.
• Улучшает качество фото и видеороликов.
• Меняет лица и синхронизирует гу…
👀 MAGI — генерация видео через ИИ
Тул создает видео по промптам с акцентом на качество движения, композиции и кинематографичную подачу.
ㅤ
—> Что умеет:
ㅤ
• Генерирует видео по тексту.
• Создает сцены с персонажами.
• Поддерживает image-to-video.
• Управляет стилем ролика.
• Создает плавную анимацию.
• Подходит для рекламного контента.
ㅤ
Сервис ускоряет создание качественных видеороликов без съемочной команды.
ㅤ
⚪Поп…
🔵Descript — all-in-one платформа для монтажа
Descript объединяет монтаж, озвучку, субтитры и обработку видео в одном редакторе.
ㅤ
Достаточно изменить текст расшифровки, чтобы автоматически изменить само видео.
ㅤ
—> Что умеет:
ㅤ
• Монтирует видео по тексту.
• Создает субтитры.
• Удаляет слова-паразиты.
• Клонирует голос.
• Записывает экран.
• Генерирует расшифровку.
Тул убирает часы ручного монтажа и ускоряет выпуск…
⚪️Floot — создание веб-приложений через ИИ
Floot генерирует полноценные веб-приложения по текстовому описанию без ручной разработки.
ㅤ
—> Что умеет:
ㅤ
• Создает web-приложения по промпту.
• Генерирует интерфейсы и логику.
• Работает без написания кода.
• Поддерживает быстрое создание MVP.
• Позволяет редактировать проект через чат.
• Ускоряет запуск новых продуктов.
ㅤ
Сервис превращает идею в рабочее приложение за м…
🔵Unbounce — быстрый конструктор лендингов
Тул помогает собирать лендинги и оптимизировать их под большее количество заявок.
—> Что умеет:
• Создает landing page.
• Запускает A/B тесты.
• Использует AI для генерации текста.
• Подключает pop-up и sticky bar.
• Интегрируется с CRM и аналитикой.
Сервис полезен, когда нужно быстро проверять гипотезы без участия разработчиков.
⚪Попробовать: здесь
❕ Stockimg — генерация визуалов и логотипов
Stockimg — сервис для создания логотипов, обложек, постеров и иллюстраций.
—> Что умеет:
• Генерирует логотипы.
• Делает постеры и баннеры.
• Создаёт иллюстрации.
• Подходит для соцсетей.
Сервис работает как универсальный генератор контента.
Подходит для быстрого создания визуалов под любые задачи.
⚪Попробовать: здесь
👀 Brandmark — брендирование любого бизнеса за 1 минуту
Brandmark — сервис для создания логотипов и полной айдентики бренда.
—> Что умеет:
• Генерирует логотипы.
• Подбирает цвета и шрифты.
• Создаёт бренд-кит.
• Делает графику для соцсетей.
Сервис сразу формирует целостный стиль бренда.
Подходит для бизнеса и проектов с нуля.
⚪Попробовать: здесь
❕ Удаляем водяные знаки с фото и видео за секунду
RemoveWatermarks — король очистки контента, который убирает логотипы, вотерки и визуальный мусор.
—> Что умеет:
• Сам находит водяные знаки, логотипы и лишние элементы на фото и видео.
• Удаляет даже сложные вотерки за секунды, без ручной возни.
• Сохраняет исходное качество, цвета, текстуры и детали.
• Чистит без артефактов и следов обработки.
• Обрабатывает ф…
⚙️ GigaSheet — таблица с мышцами для больших данных
Сервис работает с гигантскими таблицами и CSV/JSON-файлами без тормозов и кода, прямо в браузере.
Даже миллионы строк обрабатываются быстро и удобно.
—> Что умеет:
• Загружает огромные наборы данных — до миллионов строк.
• Фильтрует, сортирует и группирует данные на лету.
• Создаёт сводки, отчёты и визуализации за пару кликов.
• Поддерживает CSV, JSON, Excel и п…
👍4
Showing the 12 most recent of 20 posts we hold for @vendetta_blog. 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 recorded as deleted after publishing
We recorded 1 post on @vendetta_blog as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
3 November 2026
Most recent seen missing
3 November 2026
Median time before seen missing
3 months
Seen missing in the last 30 days
1
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
Names
Channels on the register whose handles appear in this channel's posts.
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.
CPA WORK | Вакансии арбитража трафика @works_cpa · 21,016 Telegram ranks this channel #54 of 79 here — alongside 78 others — read 26 September 2026
This channel appears in 1 seed channel's Telegram-generated recommendation list 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 2 November 2026 — this
entry's latest reading, not the date you are reading this.
“Vendetta Блог | Арбитраж трафика” (@vendetta_blog), 9,575 subscribers as measured 2 November 2026. Telegram Register, tgregister.com/channel/vendetta_blog.
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.