5 measurements spanning 7 days, net +3. 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 2,683–2,693 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 23:03
2,691
-1
9 Aug 2026, 14:15
2,692
+8
6 Aug 2026, 18:12
2,684
-4
6 Aug 2026, 05:19
2,688
no change
6 Aug 2026, 01:46
2,688
first reading
Engagement
20 posts held, back to 16 October 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 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
50.8%
avg views ÷ 2,691 subscribers
Avg views / post
1,370
5 posts measured
Reaction rate
1.76%
reactions ÷ views · ER floor
Posts in window
5
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.
What these figures were computed from
Window
Rolling 30 days · latest post in window 5 August 2026
Posts held
20 (16 October 2025 – 5 August 2026)
Views total
6,831
Reactions total
120
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 16:14 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.
Reaction mix
575 reactions across 20 posts, in 9 distinct kinds. The most used accounts for 41.7% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
240
41.7%
❤
119
20.7%
🫡
113
19.7%
🌚
53
9.22%
🤔
33
5.74%
🗿
8
1.39%
🤯
4
0.696%
🤷
3
0.522%
🤝
2
0.348%
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 20 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 575reactions 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 16 October 2025 to 5 August 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
10
across the posts below
Posts paid on
2
of 20 we hold a reading for · 10%
Most on one post
9
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @growthmrkt. 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 20 most recent posts we hold for this entry, published 16 October 2025 to 5 August 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.
Вам всем нужны красивые GitHub аккаунты
Гитхаб это новый LinkedIn. В линкдине написано, где вы работали, и к каким достижениям пришли. Титанический, кстати, труд все это высасывать из пальца, жизненно??
В гитхаб же можно положить то, что вы сделали. Раньше так могли только программисты, теперь могут все. Ваши скилы для ЛЛМ, навайбкоженные тематические пет-проекты, все должно быть публичным.
В среднем гитхаб аккаун…
Недавно делал помогатора для получения/продления испанского номад-внж. Отработал он хорошо и чтобы добро не пропадало, я анонимизировал этот проект и выложил на гитхаб.
У проекта два основных режима (работают и для первичной подачи и для продления):
1. Консультант, который оценивает ваш кейс
2. Помогатор, который берет за руку, делает пошаговый план (и далее ведет по нему) по конкретно вашему случаю
Наверняка у в…
ИИ скилы для того, чтобы быстро писать ЛИ-посты
Хорошего настроениям тем, кто подхейчивает LinkedIn и угорает с контента, который видит в ЛИ ленте. К остальным много вопросов!
Вместе с тем, нам всем приходится участвовать в этой специальной олимпиаде и что-то туда постить
(чтобы поднять видимость профиля для рекрутеров или если что-то ПРОДАЕМ)
Я сделал себе пайплайн из скилов для клод кода, чтобы генерить неплохие…
Два карьерных стула: ИИ-дженералист vs "AI-native обычный маркетолог"
Вопрос подписчикам, особенно тем, кто ищет работу (или подумывает..)
Если слушать западных ЛОМов, то в отношение формы и содержания "маркетолог будущего" (это же плюс-минус касается и продактов и т.д.) сформирован консенсус
Маркетолог-будущего это человек, который:
1. Дженералист, понимает как работают все основные каналы, на каких и в каких ус…
Как поднять конверсию для распродаж и акций. Свежее исследование
✍️ TLDR
Разбитая на компоненты (stacked) скидка бустит конверсию в покупку на 16% vs такой же по размеру цельной скидки
Т.е. cкидка 35% одним куском и 15% + 10% + 10% математически одно и то же, но во втором случае конверия выше. Работает логика «собрать комбо и на*бать систему»
🔍 Подробнее об исследовании
Работяги из Bentley University сделали 6 эк…
Потный пайплайн для генерации тактик для партизанского маркетинга
Подписчики, хвастаюсь, что сделал бесплатный плагин для клод кода и кодекса, который умеет генерить growth-тактики для продвижения вашего стартапа/продукта
Ссылка на GitHub
💎 Что выдает
7-10 неконвенциональных хитрожопых тактик для привлечения юзеров. То, что приходится делать, когда у конкурентов больше денег и других ресурсов и нужно их перехитр…
Опять про AI SEO, но теперь мощный аргумент, что к нему нужно относится как к отдельной функции, а не придатку олдового SEO.
В Ahrefs (=авторитетно) за полгода сделали кучу рисерчей, проанализировали 1 млрд дата поинтов и выяснили следующее:
1️⃣ Статьи-листиклы "Best X" - лучший формат для ИИ-цитирования. 43.8% всех ссылок ChatGPT ведут на них
2️⃣ 67% топ-цитат ChatGPT = источники, на которые мы не слишком сильно …
Подъехали новые данные по доле AI-поиска (и том насколько опять умерло SEO)
✍️ TLDR
AI disruption поиска опять не подтверждается данными. AI-тулы растут, но с очень низкой базы. Источник данных - clickstream (десктоп онли).
💾 Данные
🔵 Доля олдскульного поиска в гугле в США: 10.4% в Q1 2026 против 9.9% в Q1 2025. В EU/UK - 11.2% против 10.8%. Де-факто, выросла.
🔵 Но AI растет быстрее поиска? В относительных проц…
Зацените мой продукт для генерации аквизишен тактик для стартапов
⛓️ Ссылка - diffmode.app
🎯 Для кого
В первую очередь для стартапов, у которых низкий MRR и мало денег на маркетинг (или не сходится экономика в платных каналах). Таргет на США в первую очередь, но в целом весь глобал работает адекватно.
🤔 В чем прикол
Если вы хотите использовать AI для помощи с брейнштормом подходов к аквизишену, по-умолчанию вс…
Подъехал потный рисерч перформанса GEO (поиска в нейронках) как канала привлечения
Помним что GEO = SGE = AIEO = LLMEO = AISEO=... ☠️
✍️ TLDR
ЛЛМ-трафик по revenue per session проигрывает всем традиционным каналам, кроме paid social. Единственное исключение - сложные продукты с долгим research cycle: там траф из ChatGPT/Perplexity конвертит сильно лучше.
🔍 Подробнее об исследовании
Авторы из University of Hamb…
Сделал себе проект для нейро-аудита рилсов / тиктоков через Gemini
Имба фича Gemini — анализ видеофайлов напрямую. Не скринов, не транскрипта, буквально кидаешь ей видик и модель его реально "смотрит".
Получается, можно на коленке собрать аудитора, который распарсит видос на кирпичики и прогонит по базе знаний с выводами из behavioral science исследований (когнитивная нагрузка, prediction error, дофаминовые механиз…
🔥21❤8🤷2
Showing the 12 most recent of 20 posts we hold for @growthmrkt. 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.
Citation-graph rank
Citation-graph rank — 15,716 of 1,345,403entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Mentions
Named by 2 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.
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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“Growth Marketing Insights 🇺🇸🇬🇧” (@growthmrkt), 2,691 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/growthmrkt.
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.