Art & design — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B, prompt version 1) read this channel’s own recent posts on 28 October 2026 and assigned it the closest of 31 fixed categories, at 84% 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
4 measurements spanning 22 days, net +4. 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 16–22 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
4 Nov 2026, 14:08
21
+2
28 Oct 2026, 06:34
19
+2
13 Oct 2026, 07:22
17
no change
13 Oct 2026, 06:47
17
first reading
Engagement
13 posts held, back to 5 September 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 1 page of Telegram’s post history, 20 posts per page.
ERR · 30 days
100.0%
avg views ÷ 21 subscribers
Avg views / post
21.0
1 post measured
Reaction rate
85.7%
reactions ÷ views · ER floor
Posts in window
1
of 13 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 12 October 2026
Posts held
13 (5 September 2026 – 12 October 2026)
Views total
21
Reactions total
18
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
13 Oct 2026, 07:22 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
3m 10s
Average length
16s
Measured directly from 12 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
193 reactions across 12 posts, in 6 distinct kinds. The most used accounts for 55.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
107
55.4%
🔥
49
25.4%
👍
26
13.5%
👏
8
4.15%
🏆
2
1.04%
🥰
1
0.518%
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 12 of the 13 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 193 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 13 most recent posts we hold, published 5 September 2026 to 12 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.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 1 post 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).
Резиновые сапоги? Значит, лужи, дождь, мокрый асфальт?
Ну слишком предсказуем🥱
Мне захотелось уйти от избитой истории с водой, но сохранить лёгкую деревенскую эстетику.
Сделала светлую каталожную fashion-съёмку, добавила немного фермерского антуража.
А потом появился он 🪿 Один гусь, и сразу +100 к вайбу💫
Как вам воплощение?
Тут реальная одежда с WB на цифровой модели
Хотелось показать спортивный образ в движении, поэтому вместо спокойных каталожных поз сделала серию динамичных кадров, где хорошо видны и посадка вещей, и их фактура, и то, как они работают в движении.
📍 Для связи @Elena_leta
Пришёл заказ
Из одной фотки товара сделать полноценный рекламный ролик для WB
С помощью AI я создала всю визуальную историю: сгенерировала необходимые сцены и персонажей, оживила их и собрала в полноценный рекламный ролик с помощью ручного монтажа 🎞️✂️
В итоге из одного фото набора получился готовый видеоконтент, который уже загружен в карточку товара.
Любой AI-контент для вашего продукта или услуг под 🔑
📍 Для свя…
Я создала этот рекламный ролик, имея на руках только статичные фотографии упаковок и самих масок.
Что было сделано:
1️⃣ Создана цифровая модель с идеальной кожей и реалистичной мимикой.
2️⃣ Анимировано точное нанесение двух разных продуктов из линейки: прозрачной гидрогелевой маски и эластичной розовой маски-бандажа для лифтинга подбородка.
3️⃣ Сохранена 100% точность бренда: упаковка в кадре, текстура гидрогеля, цве…
Реалистичные ИИ-ролики, где модели демонстрируют одежду в движении в заданной локации.
Для кого это?
Брендам одежды и шоурумам — для эффектной презентации новых коллекций.
Маркетплейсам (Wildberries, Ozon, Lamoda, Kaspi) — чтобы выделить карточки товара среди конкурентов динамичным контентом.
Байерам и дизайнерам — для визуализации концептов одежды еще до запуска в производство.
Быстро | Реалистично | Выгодно
📍 Для…
Фэшн-кампейн без съёмок, студий и моделей
100% сохранность вашей одежды и аксессуаров.
Никаких искажений: крой, швы, посадка и принты передаются 1:1
Текстура ткани: виден каждый изгиб, плотность и фактура материала в движении.
Детали: фурнитура, пуговицы, замки и аксессуары остаются четкими и узнаваемыми.
💼 Какая выгода для бренда?
Экономия до 80% бюджета: никаких расходов на аренду студий, гонорары моделям, фотог…
Нейро-ролик для бренда косметики (Cucumber Body Lotion)🥒
Концепт: Свежесть, увлажнение и эстетика жаркого лета.
В этом ролике я передала тактильное ощущение влаги и сочности через визуальные образы: бассейн, огурцы и идеальная текстура кожи.
Что было реализовано с помощью ИИ (без реальных съемок):
Сложная физика жидкостей и объектов: Реалистичное падение флакона лосьона и сочных долек огурца в воду с генерацией прав…
❤6🔥1
Showing the 12 most recent of 13 posts we hold for @promoneuro. 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.
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 4 November 2026 — this
entry's latest reading, not the date you are reading this.
“ИИ ролики | Рекламные нейровидео | анимация” (@promoneuro), 21 subscribers as measured 4 November 2026. Telegram Register, tgregister.com/channel/promoneuro.
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.