Channel
@simulative_official
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
7,398subscribers
+15 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001373128436 |
|---|---|
| Type | Channel |
| Username | @simulative_official |
| Created | Between 1 March 2018 and 30 June 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/simulative_official |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 05:05 | 7,398 | +7 |
| 9 Aug 2026, 23:35 | 7,391 | +8 |
| 7 Aug 2026, 02:36 | 7,383 | no change |
| 7 Aug 2026, 00:45 | 7,383 | first reading |
24 posts held, back to 24 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 12 pagesof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 12 August 2026 |
|---|---|
| Posts held | 24 (24 July 2026 – 12 August 2026) |
| Views total | 16,546 |
| Reactions total | 193 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 17:31 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.
193 reactions across 23 posts, in 5 distinct kinds. The most used accounts for 37.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 73 | 37.8% | |
| ❤ | 65 | 33.7% | |
| 👍 | 52 | 26.9% | |
| 🤩 | 2 | 1.04% | |
| custom 5265117111570698149 | 1 | 0.518% |
Custom emoji. One row above is a 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 count beside it isTelegram’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 23 of the 24 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 193reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 24 most recent posts we hold, published 24 July 2026 to 12 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.
🤖 Как я использую ИИ? Всем привет! На связи Александр Грудинин, ментор профессии «Аналитик данных» 👋🏻 Сегодня я расскажу, как я использую ИИ в работе. 🧠 Еженедельный отчёт Раньше требовался час, чтобы перебрать Jira руками и перечитать чаты. Теперь агент сам вытаскивает закрытые за неделю тикеты, ходит в Slack, группирует по направлениям, пишет по строке результата на каждый. Я правлю пару строк и публикую. Работа…
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VAR — математическая взаимосвязь рядов Всем привет, с вами Павел Беляев, ведущий канала Тимлидское об аналитике и автор курса «Временные ряды» 👋🏻 Анализировать один временной ряд — это хорошо, а два и более — лучше! В природе и в бизнесе метрики, как правило, не существуют изолированно — на них что-то влияет, какие-то процессы, факторы, словом, другие метрики. Это сложное взаимодействие разных процессов подводит н…
🔥3❤1👍1
💻💻💻💻💻День джуна в прямом эфире! Представьте, что уже сегодня вы решили стать аналитиком. На нашем вебинаре за 1,5 часа вы пройдёте все этапы, через которые проходит почти каждый кандидат: получите тестовое, разберете его вместе с тимлидом одного из крупнейших банков страны, а затем окажетесь на собеседовании с HR и узнаете, что на самом деле влияет на решение работодателя. Итого за 90 минут вы проживете путь начина…
👍5❤2🔥2
#проанализировали_и_поняли
❤10🔥3👍1
Posted without readable text
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Зачем нужна декомпозиция и фильтрация временных рядов? Всем привет, с вами Павел Беляев, ведущий канала Тимлидское об аналитике и автор курса «Временные ряды» 👋🏻 Первое, что хочется сделать аналитику при виде некоторой кривой, растянувшейся по временной оси — декомпозировать её на составляющие. (В общем-то, даже визуально можно их увидеть, не применяя специальных методов.) Почему же дата-аналитик мгновенно произво…
👍2❤1🔥1
💻💻💻💻💻 Практика по BI: строим дашборды на реальном кейсе Написать запрос — половина работы аналитика. Вторая начинается там, где цифры нужно превратить в дашборд, который поймут без вас коллеги, руководитель, заказчик и т. д. Этот навык одинаково нужен и джуну на старте — без него не пройти собеседование, где просят показать визуализацию — и действующему аналитику, которому важно, чтобы на его выводы реально опиралис…
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💻 Острова и промежутки в SQL Всем привет! На связи Александр Грудинин, ментор профессии «Аналитик данных» 👋🏻 Разберём приём в SQL, который порой выглядит как магия, а держится на простой арифметике. Называется islands and gaps, «острова и промежутки». ✅ Задача Найти пользователей, которые заходили в сервис 5 и более дней подряд. Как только начинаешь думать про «подряд», в голове каша из «оконок» и самосоединений.…
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📈 Этот лайфхак экономит кучу времени при создании дашбордов Привет, друзья! На связи Евгений Буторин, ментор интенсива по SQL и руководитель направления аналитики в Альфа Банке 👋🏻 Хочу рассказать вам лайфхак, которым пользуюсь каждый раз, когда собираю дашборд под конкретную задачу. Часто вижу, что многие сразу лезут в BI-систему и начинают тянуть сырые таблицы, строить связи и уже потом мучаются с моделью. Я дела…
❤4👍4🔥3
💻💻💻💻💻 Аналитик данных: кто это и почему ему важно знать SQL Привет! На связи Евгений Буторин, ментор интенсива по SQL 👋🏻 Вам хочется попробовать себя в аналитике, но непонятно, кто такой аналитик на самом деле, что нужно уметь на старте и почему все вокруг твердят именно про SQL? Приходите на мой вебинар — разберём это на реальных примерах и задачах. Разберём: ➖ Кто такой аналитик данных и чем он занимается каждый…
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#проанализировали_и_поняли
❤7👍4🔥3
Posted without readable text
🤩2❤1👍1
Showing the 12 most recent of 24 posts we hold for @simulative_official. 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 — 441,605 of 1,176,251entries 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.
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.
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.
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.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“Симулейтив” (@simulative_official), 7,398 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/simulative_official.
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.