Telegram RegisterThe public register of Telegram

Channel

Reveal the Data

@revealthedata

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

27,910subscribers

-46 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001490689117
TypeChannel
Username@revealthedata
DescriptionКанал Ромы Бунина про визуализацию данных, дашборды и развитие BI-систем. Подробнее про канал, рубрики, правила и контакты — https://t.me/revealthedata/386 Сайт и блог — https://revealthedata.com/
Created26 February 2020measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live14 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 14 August 2026
On Telegramt.me/revealthedata

Growth

27,91027,95627,9336 August 2026 — 27,956 subscribers7 August 2026 — 27,949 subscribers8 August 2026 — 27,932 subscribers9 August 2026 — 27,934 subscribers11 August 2026 — 27,930 subscribers12 August 2026 — 27,919 subscribers13 August 2026 — 27,917 subscribers14 August 2026 — 27,910 subscribers6 August 202614 August 2026
8 measurements spanning 8 days, net -46. 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 27,903–27,963 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
14 Aug 2026, 13:5627,910-7
13 Aug 2026, 02:1827,917-2
12 Aug 2026, 04:3627,919-11
11 Aug 2026, 06:0127,930-4
9 Aug 2026, 07:5127,934+2
8 Aug 2026, 05:0327,932-17
7 Aug 2026, 02:5827,949-7
6 Aug 2026, 04:1827,956first reading

Engagement

11 posts held, back to 23 June 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 22 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
19.0%
avg views ÷ 27,910 subscribers
Avg views / post
5,320
2 posts measured
Reaction rate
1.90%
reactions ÷ views · ER floor
Posts in window
2
of 11 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
WindowRolling 30 days · latest post in window 19 July 2026
Posts held11 (23 June 202619 July 2026)
Views total10,630
Reactions total202
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken15 Aug 2026, 15:19 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
1,080
Videos
41
Links
746

Lifetime counters from Telegram’s own channel header, read 15 August 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.

Video runtime
1m 10s
Average length
1m 10s

Measured directly from 1 video 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

441 reactions across 11 posts, in 6 distinct kinds. The most used accounts for 50.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
22450.8%
👍11425.9%
😁6013.6%
🔥388.62%
😈40.907%
🕊10.227%

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 11 of the 11 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 441reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 11 most recent posts we hold, published 23 June 2026 to 19 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.

Recent posts

19 Jul 2026, 16:15 UTC≈5,030 views144 reactionsread 15 August 2026
Photo

Ахахах, я чёт не думал, что найду себя в воскресенье в окружении трех ноутбуков с агентами, которые гоняют таски 🙈 Агенты реально как какая-то зависимость блин. И моя эго-часть явно хочет с вами поделиться и похвастаться, смотрите блин какой же я крутой. А нормальная моя часть рекомендует — если вы нашли себя в такой же ситуации, то пойдите отдохните пожалуйста 🫂 Я пошёл!

94👍29😁17😈4

17 Jul 2026, 12:55 UTC≈5,600 views58 reactionsread 15 August 2026

Posted without readable text

🔥31👍1510😁2

12 Jul 2026, 07:24 UTC≈5,770 views55 reactionsread 15 August 2026
Photo

🥞 Искусство, AI и панкейки Я знаю, что не хорошо газлайтить LLM-ки, но не смог удержаться. Несерьёзный воскресный пост. Вчера в музее во Франкфурте наткнулся на интересный проект — сразу и дашборды, и AI и искусство. Есть один источник воды, который используется для трех вещей: из него может пить директор музея, из него можно поливать растения или этой же водой можно охлаждать GPU. Есть локально развернутая модель,

😁3520

10 Jul 2026, 14:33 UTC≈4,800 views44 reactionsread 15 August 2026
Forwarded from @data_publicationPhoto

📈 Зачем нужны аналитики в мире победившего ИИ? 14 июля поговорим с Ромой Буниным о том, как меняется профессия аналитика в эпоху ИИ и какие навыки становятся важнее, чем знание инструментов. 🎙 Гость — Рома Бунин, AI Adoption Lead в Nebius, экс-руководитель аналитики в Яндексе, автор канала Reveal the Data. О чем поговорим: 🔹 Что ИИ уже умеет делать за аналитика 🔹 Почему ИИ пока не заменил аналитиков 🔹 Аналитик буду

👍2618

10 Jul 2026, 14:33 UTC≈4,370 views10 reactionsread 15 August 2026

Астрологи объявили недели эфиров. В следующей вторник встречаюсь с Андреем Дорожным — поговорим про аналитику и AI, присоединяйтесь к обсуждению! Андрей особенный человек для меня и канала — он был первым гостем, кто пришёл на мой подкаст про визуализацию данных 6 лет назад и дал буст и старт каналу. One love! ❤️

10

9 Jul 2026, 07:01 UTC≈4,660 views12 reactionsread 15 August 2026
Forwarded from @datanaturePhoto

AI не готовность - пост о том, как препарируем доменные данные и контекст в Авито Антропик напомнил всем кто забыл, что Text2sql бесполезен, если он не шарит в данных домена. Мы догадывались. Одна из тестируемых тут идей - AI-ready score в целях тимлидов доменов. Это булевые проверки условно трех групп: - Роли и в домене (BI-партнёр, Куратор метрик, AI-чемпион). Скучный компонент, но без гавернанс-людей никак; -

👍111

9 Jul 2026, 07:01 UTC≈4,380 views4 reactionsread 15 August 2026

Уровень злободневности в посте у Саши просто зашкаливает 🙈😑 А вы уже измеряете насколько домены готовы к внедрению агентов? А планы запросов в базу от агентов анализируете?

😁31

3 Jul 2026, 08:47 UTC≈6,370 views44 reactionsread 15 August 2026
Photo

🤖AGI BI eval, v.3 Что же там вернули Fable, поэтому пора снова провести самый лучший и точный бэнчмарк в мире — моя субъективная оценка как LLM строят дашборды 😈 Я повторил упражнение, которые делал уже раньше — попросил составить список вопросов, которые модель задала бы для создания дашбрда → ответил → попросил сделать макет → реализовать HTML-дашборд. Тестировал три версии: Opus 4.8 High и Fable5 High и Extra Hig

👍3011😁3

1 Jul 2026, 12:05 UTC≈5,030 views30 reactionsread 15 August 2026
Forwarded from @cryptoEssay

Завтра делаем открытый эфир о том как крупнейшие компании проходят процесс ИИ-трансформации. Роман Бунин из Nebius и Антон Граборов из Альфа-Капитал: как крупные компании внедряют AI Это выпускники лаборатории AI-native, с ними разберём, как внедряют AI-native подход внутри больших организаций: с регуляторикой, наследием процессов, продуктовой сложностью и ценой ошибки. ​Подсмотрим в процессы: 📝 Романа Бунина – AI

22🔥5👍3

1 Jul 2026, 12:05 UTC≈4,970 views19 reactionsread 15 August 2026

Ребята из AI Mindset пригласили к себе на эфир, рассказать примерно о том же о чем рассказывал на прошлой неделе на конференции, но чуть подробнее. Опять расскажу про метрики для измерения Adoption, но ещё покажу какую базу знаний / second brain, мы строим для своей команды. Приходите, будет интересно. Запись: https://youtu.be/Ex9YFZzLk3g

16🔥2🕊1

23 Jun 2026, 14:39 UTC≈7,170 views21 reactionsread 15 August 2026

Конференция стартует через 20 минут, послушать можно будет тут, приходите! https://us06web.zoom.us/j/82701825581 Записи и конспект — https://t.me/communitysprints/60

21

Showing the 11 most recent of 11 posts we hold for @revealthedata. 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.

Citation-graph rank

Citation-graph rank — 103,523 of 1,480,688entries 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.

Forward network

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

Named by 14 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.

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 14 August 2026 — this entry's latest reading, not the date you are reading this.

“Reveal the Data” (@revealthedata), 27,910 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/revealthedata.

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.