6 measurements spanning 7 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 1,704–1,710 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
14 Aug 2026, 00:38
1,705
-1
11 Aug 2026, 01:51
1,706
no change
10 Aug 2026, 21:31
1,706
-1
8 Aug 2026, 01:42
1,707
no change
7 Aug 2026, 17:34
1,707
-2
7 Aug 2026, 02:02
1,709
first reading
Engagement
18 posts held, back to 7 May 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 3 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
18.7%
avg views ÷ 1,705 subscribers
Avg views / post
318
2 posts measured
Reaction rate
1.73%
reactions ÷ views · ER floor
Posts in window
2
of 18 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 23 July 2026
Posts held
18 (7 May 2026 – 23 July 2026)
Views total
636
Reactions total
11
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 17:42 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
49s
Average length
25s
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
81 reactions across 16 posts, in 11 distinct kinds. The most used accounts for 37.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
30
37.0%
👍
11
13.6%
🔥
11
13.6%
😍
8
9.88%
custom 5431737524550651697
6
7.41%
🤩
4
4.94%
custom 5361839607572877436
3
3.70%
❤🔥
3
3.70%
🤣
3
3.70%
👌
1
1.23%
💯
1
1.23%
Custom emoji. 2 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them areTelegram’s.
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 16 of the 18 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 81reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 7 May 2026 to 23 July 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
2
across the posts below
Posts paid on
2
of 18 we hold a reading for · 11%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @teamly. 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 18 most recent posts we hold for this entry, published 7 May 2026 to 23 July 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.
Cотрудник уволился, а вместе с ним ушли знания, инструкции и та самая невидимая часть айсберга бизнес-процессов. Знакомо? 🥺
Когда такое случается, у руководителя остается только ощущение, что это точно где-то было. Но где?
Так происходит, если:
💙 Договоренности жили в чатах
💙 Решения принимались на созвонах экспромтом и нигде не фиксировались
💙 Финальную версию планировали оформить позже. Но оформлять, конечно, ник…
🌐 Дистанционное обучение в крупном бизнесе
Когда компания растет, очное обучение начинает проигрывать по скорости и стоимости. Поэтому все больше компаний переходят на дистанционный формат.
Но чтобы обучение действительно работало, оно должно быть:
💙 актуальным;
💙 персонализированным;
💙 связанным с практикой;
💙 измеримым.
Именно поэтому мы развиваем TEAMLY — платформу, которая объединяет знания, обучение и ИИ в ед…
🏭 Опыт машиностроительного холдинга
Во многих производственных компаниях знания существуют... но пользоваться ими сложно.
Инструкции лежат в PDF, регламенты — на сетевых дисках, часть информации хранится в локальных вики, а актуальную версию документа сотрудники ищут через коллег.
Именно с такой ситуацией столкнулся один из машиностроительных холдингов.
После внедрения единой базы знаний удалось добиться заметных…
🤔 У вас есть база знаний. Почему сотрудники всё равно спрашивают коллег?
Большинство компаний начинают одинаково.
Сначала появляется база знаний. В неё собирают инструкции, регламенты, шаблоны и опыт команды.
Кажется, что проблема решена.
Но спустя несколько месяцев ничего не меняется.
💙Новички продолжают задавать вопросы наставникам.
💙Команды работают по-разному.
💙Одни и те же ошибки повторяются снова и снова…
☄️ Сколько компания может сэкономить благодаря ИИ?
На конференции «Знания, обучение и ИИ – в единой рабочей среде» директор по развитию QSOFT Олег Демченко рассказал, как команда использует ИИ в рекрутинге, обучении, продажах и управлении знаниями. И главное — какой эффект это приносит бизнесу.
💙 Например, в рекрутинге:
• раньше на обработку 180 резюме уходило около 12 часов, теперь — всего 2 часа;
• ещё 9,5 часа …
👀 Что нового появилось в TEAMLY этой весной?
Мы продолжаем развивать платформу так, чтобы знания, документы и обучение работали как единая система. В весеннем релизе появилось сразу несколько важных обновлений.
💗 ИИ-ассистент стал умнее. Теперь он ищет ответы не только в статьях, но и в PDF, Word и Excel-файлах, а также может работать со всей базой знаний или отдельным пространством.
💗 Внешний ИИ-виджет. Его можно…
На конференции «Знания, обучение и ИИ – в единой рабочей среде» Евгения Давыденкова, руководитель бизнес-акселератора «Росатом Энергосбыт» рассказала, как создавали базу знаний проекта и вернули порядок в общий процесс.
Конкурс «Энергетика лидеров» в «Росатом Энергосбыт» запустили 10 лет назад. Тогда в портфеле было 12 инициатив. Через пять лет их стало 170 и число участников достигло 3000.
💙 То, что раньше работал…
В TEAMLY можно создавать курсы с помощью ИИ. Но что, если у компании уже есть готовый курс в формате SCORM? Хранить его отдельно от платформы совместной работы не логично.
Мы тоже так считаем, поэтому в весеннем релизе добавили возможность импорта SCORM-пакетов. При этом:
💙Структура обучения сохраняется
💙Сотрудники продолжают обучение в привычном формате
💙Не нужно выделять бюджет на повторную разработку
Работает в…
👀 Зачем производству умные таблицы? И как они работают?
Если база знаний, данные о запасах и складских остатках живут в разных таблицах и файлах, компания каждый день теряет деньги на простоях и браке.
«Новичок в цехе тратит час, чтобы найти актуальную инструкцию, логист сверяет остатки с Excel-файлом трёхмесячной давности, а руководитель узнаёт про ошибку в рецептуре уже по итогам списаний и претензий клиентов».
💙…
🖥 Мы уже писали о том, как изменилась логика создания корпоративных курсов
Мало вложить деньги в обучение и заказать создание дорогого курса. Без анализа результатов эффект может оказаться нулевым.
Поэтому мы уделили большое внимание разработке инструментов для аналитики процесса и итогов обучения.
На платформе доступны:
💙подробные отчёты по урокам и тестам
💙индивидуальная статистика сотрудника
💙экспорт отчётов дл…
Потратили миллионы на обучение, курсы пройдены, сертификаты получены. Теперь бизнес пойдёт в гору?
Не обязательно 🫠
Представьте, что завтра вам нужно собрать команду для запуска нового продукта, выхода на другой рынок или внедрения новой технологии. Сможете ли вы быстро ответить на несколько вопросов?
Кто внутри компании уже обладает нужной экспертизой?
Каких компетенций не хватает прямо сейчас?
Какие знания придё…
Недавно мы писали о том, почему компании всё чаще отказываются от «идеальных курсов»
Потому что идеальный курс легко может устареть ещё до запуска.
Особенно если речь идёт о продажах, клиентском сервисе, новых продуктах или изменениях в законодательстве. Пока идут согласования, информация уже успевает поменяться.
Но тогда возникает другой вопрос
А как создавать курсы максимально быстро и не тратить недели на сбор…
❤3👍2😍1
Showing the 12 most recent of 18 posts we hold for @teamly. 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.
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
Shares as published. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 18 most recent posts we hold, published 7 May 2026 to 23 July 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Citation-graph rank
Citation-graph rank — 1,018,736 of 1,183,361entries 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 1 registered channel — 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 14 August 2026 — this
entry's latest reading, not the date you are reading this.
“TEAMLY” (@teamly), 1,705 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/teamly.
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.