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Channel

Product education: курсы, видео, статьи и материалы для продактов и предпринимателей

@eduproduct

On this record: Topic · Observations · Also posting the same content · Growth · Engagement · Reactions · Stars · Advertising · Posts · Citations · Cite this entry

3,907subscribers

-5 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001500587245
TypeChannel
Username@eduproduct
CreatedBetween 1 January 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live10 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 10 August 2026
On Telegramt.me/eduproduct

Topic

Education — 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 11 August 2026 and assigned it the closest of 31 fixed categories, at 98% 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.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear word for word on 3 other registered channels. They sit inside a group of 5 channels that share the same post bodies with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (6 of the pairs behind the counts below)
Posted firstThenOverlapGap
@sergeyproduct/69830 May 2026, 10:50 UTC@eduproduct/581 · this entry31 May 2026, 10:53 UTC1.0024 hours
@eduproduct/583 · this entry7 Jun 2026, 07:24 UTC@sergeyproduct/7007 Jun 2026, 08:25 UTC1.0062 minutes
@eduproduct/584 · this entry8 Jun 2026, 08:47 UTC@sergeyproduct/7018 Jun 2026, 08:47 UTC1.00under a minute
@sergeyproduct/70211 Jun 2026, 07:01 UTC@eduproduct/585 · this entry11 Jun 2026, 07:35 UTC1.0034 minutes
@sergeyproduct/70313 Jun 2026, 09:56 UTC@eduproduct/586 · this entry13 Jun 2026, 13:51 UTC1.003.9 hours
@eduproduct/592 · this entry28 Jun 2026, 07:31 UTC@sergeyproduct/7145 Jul 2026, 09:32 UTC1.007 days
Every channel this entry shares post bodies with
ChannelMatching postsText overlapTypical gapPublished first
@sergeyproduct12 (8/8 hand-verifiable sample passed)1.002.5 hourseven
@productcasebar6 (6/6 hand-verifiable sample passed)1.004.3 hoursthis entry (60)
@productconsult5 (5/5 hand-verifiable sample passed)1.00under a minutethis entry (32)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 8 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.

“Published first” means first in this corpus. We hold 20 comparable posts for this entry, running 23 May 2026 to 5 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Across the whole group of 5, the earliest publisher we hold is @eduproduct — which is this entry. That is a statement about our reading window, not a claim of authorship.

Recorded under the keys clone_mutual · clone_source, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Also posting the same content

This channel’s posts match, word for word or near enough, posts on 4 other registered channels, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.

Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

3,9073,9123,909.56 August 2026 — 3,912 subscribers7 August 2026 — 3,910 subscribers10 August 2026 — 3,907 subscribers6 August 202610 August 2026
3 measurements spanning 4 days, net -5. 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 3,906–3,913 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
10 Aug 2026, 01:013,907-3
7 Aug 2026, 03:113,910-2
6 Aug 2026, 12:353,912first reading

Engagement

21 posts held, back to 23 May 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 4 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
4.58%
avg views ÷ 3,907 subscribers
Avg views / post
179
4 posts measured
Reaction rate
1.61%
reactions ÷ views · ER floor
Posts in window
4
of 21 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. It is computed over the 3 of 4 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 8 August 2026
Posts held21 (23 May 20268 August 2026)
Views total715
Reactions total6
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 05:57 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

30 reactions across 13 posts, in 3 distinct kinds. The most used accounts for 53.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥1653.3%
1033.3%
👍413.3%

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 13 of the 21 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 30reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 21 most recent posts we hold, published 23 May 2026 to 8 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
1
across the posts below
Posts paid on
1
of 21 we hold a reading for · 5%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @eduproduct. 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 21 most recent posts we hold for this entry, published 23 May 2026 to 8 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.

Advertising

Ad load
9.52%
2 of 21 posts carry an ad marker
Regulatory tokens
2
posts carrying an erid · 2 distinct tokens
Median views · ads
41.0
over 2 measured posts
Median views · rest
263
over 19 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor.A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2W5zFH4j5HK123 June 202623 June 2026
2W5zFK5j5BX18 August 20268 August 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 21 most recent posts we hold, published 23 May 2026 to 8 August 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

8 Aug 2026, 04:50 UTC41 views1 reactionsread 8 August 2026
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⚡️Массовый переход на электронные транспортные накладные. Как перестроить работу. С 1 сентября бумажные транспортные накладные отменят. Оформлять электронные придется большинству фирм. Система Главбух поможет перейти на новые правила. Пройдите короткий тест и закажите пробный доступ к Системе Главбух, чтобы подготовиться к новым правилам работы заранее и избежать ошибок. В подарок – детальная памятка по изменениям

1

5 Aug 2026, 14:05 UTC128 views3 reactionsread 8 August 2026

Как траблшутинг помогает решать проблемы продакт-менеджера? В большинстве компаний проблемы диагностируют очень поверхностно. Упала конверсия - значит проблема в UX. Упали продажи - значит проблема в маркетинге. Пользователи жалуются - значит проблема в продукте. Но если посмотреть разборы крупных инцидентов в банках, маркетплейсах, финтехе и SaaS-компаниях, оказывается, что реальная причина почти всегда находится н

🔥3

27 Jul 2026, 07:04 UTC203 views2 reactionsread 8 August 2026

Закономерности остаются удивительно стабильными уже десятилетиями Фокус продакта не только про Gartner Hype Cycle. Гораздо важнее понимать, какие силы реально двигают продукты, рынки и поведение пользователей. Есть несколько моделей, которые я бы поставил в обязательный набор любого сильного продакта. 1. S-Curve Innovation. Практически любая технология проходит одинаковый путь: медленный старт, взрывной рост, насыщ

👍1🔥1

18 Jul 2026, 08:36 UTC343 viewsread 8 August 2026

Все для роста от редакции канала 1. Образовательные возможности редакции (можно с счета юрлица, можно придумать персональную рассрочку): Для тех, кто хочет системно расти в продакт-менеджменте: - Курс по продакт-менеджменту, базовый - Карьерный интенсив с разборами тестовых - Курс по аналитике, базовый - Курс по Продуктовой стратегии и защите инициативы 2. Разборы кейсов, 76 разборов прошли, всего свыше 190 тестов

7 Jul 2026, 14:17 UTC349 viewsread 8 August 2026

Если хотите искать идеи для своего продукта – смотрите не на конкурентов, а на страны Большинство тиражируемых продуктовых фич родились как решение очень локальной проблемы. А потом их начал копировать весь мир. • Китай – Super App (WeChat, Alipay). Проблема была простой: пользователям приходилось устанавливать десятки приложений, а мобильная инфраструктура росла быстрее, чем цифровые сервисы. Решением стало объеди

1 Jul 2026, 13:24 UTC236 views1 reactionsread 8 August 2026

Вопросы для понимания, на чем лучше зарабатывать в продукте После прошлого поста многие задали хороший вопрос: а как вообще понять, на чем компании выгоднее всего зарабатывать? Есть ощущение, что это какая-то магия, доступная только CEO и финансистам. На самом деле у сильных продактов есть несколько очень практичных приемов. Если вам нужна помощь в росте продуктов и команды, пишите @SKoloskov (свыше 120 кейсов на 7

👍1

28 Jun 2026, 07:31 UTC223 viewsread 8 August 2026

Все для роста от редакции канала 1. Обратите внимание на предложение в основном канале редакции https://t.me/FreshProductGo/1792 2. Образовательные возможности редакции (можно с счета юрлица, можно придумать персональную рассрочку): Для тех, кто хочет системно расти в продакт-менеджменте: - Курс по продакт-менеджменту, базовый - Карьерный интенсив с разборами тестовых - Курс по аналитике, базовый - Курс по Продукт

26 Jun 2026, 09:11 UTC224 views3 reactionsread 8 August 2026

🎉Когда стратегия становится результатом За каждой успешной стратегией стоит не только идея, но и анализ, фреймворки, знание рынка и понимание того, как масштабировать продукт. Умение видеть потенциал продукта — значит понимать, как его можно адаптировать для разных сегментов, каналов или новых рынков. Создание плана для постепенного масштабирования с гибким подходом — это ключ к успешному и безопасному расширению п

🔥3

23 Jun 2026, 12:01 UTC230 viewsread 8 August 2026
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21 Jun 2026, 08:16 UTC230 viewsread 8 August 2026

Все для роста от редакции канала 1. Образовательные возможности редакции (можно с счета юрлица, можно придумать персональную рассрочку): Для тех, кто хочет системно расти в продакт-менеджменте: - Курс по продакт-менеджменту, базовый - Карьерный интенсив с разборами тестовых - Курс по аналитике, базовый - Курс по Продуктовой стратегии и защите инициативы - Сайт команды 2. Разборы кейсов, 76 разборов пришли, всего

20 Jun 2026, 06:53 UTC229 views4 reactionsread 8 August 2026

Компания зарабатывает не всегда на том, за что пользователь платит • Например, многие банковские приложения активно развивают оплату ЖКХ. Кажется, что цель очевидна – заработать комиссию. Но в большинстве случаев комиссия там минимальна или ее нет вообще. Настоящая ценность в другом. Регулярные платежи помогают банку понимать состав семьи, район проживания, наличие недвижимости, дисциплину платежей, сезонность расхо

🔥4

13 Jun 2026, 13:51 UTC270 views2 reactions1 Starread 8 August 2026

Какие механики заставляют пользователя возвращаться снова и снова Большинство людей думают, что лучшие продукты побеждают благодаря технологиям. На практике многие крупнейшие продукты мира выросли благодаря гораздо более простой вещи — пониманию человеческой психологии. Хотите расширить насмотренность и разобрать задачи из продуктовой практики и тестовых заданий? Подключайтесь на Разборы от редакции. 1. Возьмем лен

2

Showing the 12 most recent of 21 posts we hold for @eduproduct. 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 — 938,311 of 1,160,990entries 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.

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

“Product education: курсы, видео, статьи и материалы для продактов и предпринимателей” (@eduproduct), 3,907 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/eduproduct.

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.