Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 99% 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 42 days, net -4,010. 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 103,716–108,929 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
18 Sept 2026, 08:58
104,317
-194
16 Sept 2026, 03:39
104,511
-149
14 Sept 2026, 10:41
104,660
-129
12 Sept 2026, 20:40
104,789
-133
10 Sept 2026, 18:59
104,922
-225
7 Sept 2026, 18:17
105,147
-160
4 Sept 2026, 19:19
105,307
-144
3 Sept 2026, 05:58
105,451
-92
2 Sept 2026, 01:44
105,543
-147
31 Aug 2026, 22:16
105,690
-134
30 Aug 2026, 19:18
105,824
-72
29 Aug 2026, 16:34
105,896
-95
28 Aug 2026, 13:34
105,991
-123
27 Aug 2026, 10:29
106,114
-104
26 Aug 2026, 13:04
106,218
-144
25 Aug 2026, 11:54
106,362
-174
24 Aug 2026, 12:19
106,536
-130
22 Aug 2026, 21:45
106,666
-164
21 Aug 2026, 12:47
106,830
-121
20 Aug 2026, 12:23
106,951
first reading
Engagement
69 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 94 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
3.39%
avg views ÷ 104,317 subscribers
Avg views / post
3,540
32 posts measured
Reaction rate
0.428%
reactions ÷ views · ER floor
Posts in window
32
of 69 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 18 September 2026
Posts held
69 (22 July 2026 – 18 September 2026)
Views total
113,200
Reactions total
484
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
20 Sept 2026, 21:30 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
≈1,310
Videos
≈320
Links
≈783
Lifetime counters from Telegram’s own channel header, read 20 September 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.
Reaction mix
1,031 reactions across 69 posts, in 13 distinct kinds. The most used accounts for 40.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
420
40.7%
🔥
261
25.3%
👍
118
11.4%
🐳
113
11.0%
🥰
84
8.15%
😍
13
1.26%
👏
8
0.776%
❤🔥
6
0.582%
😁
3
0.291%
🤡
2
0.194%
👎
1
0.097%
🤣
1
0.097%
🥱
1
0.097%
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 69 of the 69 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 1,031 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 69 most recent posts we hold, published 22 July 2026 to 18 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.
Telegram Stars
Stars received
6
across the posts below
Posts paid on
2
of 69 we hold a reading for · 3%
Most on one post
5
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @chatgptme. 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 69 most recent posts we hold for this entry, published 22 July 2026 to 18 September 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.
🙂↕️ ✍️ 4 промта для работы с текстом, которые заменят несколько часов редактуры
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣ Отредактировать текст, сохранив свой стиль
Отредактируй мой текст: [ВСТАВЬТЕ ТЕКСТ].
Не переписывай его полностью и не меняй мой авторский стиль.
Найди только те места, которые действительно стоит улучшить: повто…
🙂↕️🧮 4 промтов, которые помогут наконец понять математику
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣Разобрать доказательство от формул до простого объяснения
Докажи [ТЕОРЕМА / УТВЕРЖДЕНИЕ].
Сначала дай строгое математическое доказательство шаг за шагом, с объяснением каждого перехода.
Затем отдельно объясни ту же идею простыми словам…
🙂↕️🧠5 промтов, которые помогут принимать решения более рационально
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣ Построить дерево последствий
Помоги мне проанализировать решение: [ОПИШИТЕ СИТУАЦИЮ].
Варианты, которые я рассматриваю: [ВАРИАНТЫ].
Для каждого варианта построй дерево возможных последствий на 3 шага вперед.
Покажи:
краткос…
Серия атмосферных осенних кадров с опавшими листьями 🍁
❓ Как сделать такое фото:
1️⃣ Открываем @GHATGPT_BOT
2️⃣ Создать картинку → Nano Banana 2 или GPT image 2 (редактировать изображение)
3️⃣ Добавляем свое фото и промпт:
Кадр 1:
СТРОГО сохранить внешность 1:1 — лицо, черты, пропорции. Формат 3:4. Создать фотореалистичный роскошный осенний бьюти-портрет. Человек лежит на спине на поверхности, полностью покрытой н…
🙂↕️ 📊 5 промтов для маркетинговой аналитики и стратегии
Нейросеть полезна не только для написания текстов. С ее помощью можно анализировать показатели, изучать аудиторию и конкурентов, строить воронки и превращать данные в конкретные гипотезы
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣Анализ конкурентов
Ты - маркетинговый аналитик. На …
🙂↕️ 5 промтов для создания контента и рекламы
Нейросеть может за несколько минут придумать посты, письма, рекламные концепции и гипотезы для тестов. Но хороший результат начинается с четкого ТЗ
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣Пост для соцсетей
Ты - SMM-специалист. Создай пост для [TELEGRAM / VK / ДРУГАЯ ПЛОЩАДКА].
Продукт: …
Романтичные парные кадры в осеннем парке 🍂
❓ Как сделать такое фото:
1️⃣ Открываем @GHATGPT_BOT
2️⃣ Создать картинку → Nano Banana 2 или GPT image 2 (редактировать изображение)
3️⃣ Добавляем свое фото и промпт:
Кадр 1:
СТРОГО сохранить внешность 1:1 — лицо, черты, пропорции. Формат 9:16. Реалистичное романтическое фото по пояс в осеннем парке. Мужчина стоит позади девушки и обнимает ее, обхватив руками спереди, ег…
Серия уютных осенних кадров на отдыхе за городом 🍂
❓ Как сделать такое фото:
1️⃣ Открываем @GHATGPT_BOT
2️⃣ Создать картинку → Nano Banana 2 или GPT image 2 (редактировать изображение)
3️⃣ Добавляем свое фото и промпт:
Кадр 1:
СТРОГО сохранить внешность 1:1 — Pinterest aesthetic, Scandinavian slow living, autumn forest retreat, тёплый домашний уют. Формат 9:16. Коллаж из двух кадров в едином стиле: девушка сидит н…
🙂↕️💰Промты для финансов, которые посчитают все за вас
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣Рассчитать ROI проекта
Ты - финансовый аналитик. Рассчитай ROI проекта.
Инвестиции: [СУММА]
Доход от проекта: [СУММА]
Дополнительные расходы: [СУММА]
Период: [ПЕРИОД]
Покажи используемую формулу, расчет по шагам и итоговый ROI в процентах.…
Студийные семейные кадры в одинаковых свитерах 🧸
❓ Как сделать такое фото:
1️⃣ Открываем @GHATGPT_BOT
2️⃣ Создать картинку → Nano Banana 2 или GPT image 2 (редактировать изображение)
3️⃣ Добавляем свое фото и промпт:
Кадр1
СТРОГО сохранить внешность 1:1 — лицо, черты, пропорции. Формат 9:16.
Реалистичное студийное семейное фото. Четыре члена семьи расположены стопкой: внизу лежит мужчина, скрестив руки и опираясь …
🙂↕️📈Промт, который превратит контент в воронку продаж
Не просто публиковать посты, а последовательно вести человека от первого знакомства до целевого действия. Этот промт поможет понять, какой контент нужен аудитории на каждом этапе 👇
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
Ты - контент-маркетолог. Создай контентную воронку для [ПРОДУКТ…
🙂↕️ 📱Как создать контент-план с помощью нейросети
❓ Как использовать:
1️⃣Открываем @GHATGPT_BOT
2️⃣Текстовые нейросети → выбираем модели любую модель отправляем промпт:
1️⃣Определить цели блога и KPI
Ты - маркетолог и контент-стратег. Помоги определить цели блога компании [НАЗВАНИЕ / ОПИСАНИЕ БИЗНЕСА].
Основная бизнес-цель: [ЦЕЛЬ].
Определи 3–5 задач, которые должен решать контент: узнаваемость, вовлечение, заяв…
👍7❤5🥰1
Showing the 12 most recent of 69 posts we hold for @chatgptme. 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.
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.
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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“DeepSeek | Nano Banana | ChatGPT prompts” (@chatgptme), 104,317 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/chatgptme.
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.