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Channel

Борода продакта

@productclub

Has discussion group

On this record: Growth · Engagement · Reactions · Stars · Posts · Citations · Cite this entry

14,119subscribers

-28 since we began measuring on 5 August 2026

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

Register entry

Telegram ID-1001025953977
TypeChannel
Username@productclub
Created15 February 2016measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded5 August 2026
Last confirmed live18 August 2026
Measurements held12
Confirmed unchanged1 time, most recently 18 August 2026
On Telegramt.me/productclub

Growth

14,11814,15414,1365 August 2026 — 14,147 subscribers7 August 2026 — 14,154 subscribers7 August 2026 — 14,152 subscribers9 August 2026 — 14,145 subscribers10 August 2026 — 14,142 subscribers11 August 2026 — 14,133 subscribers12 August 2026 — 14,132 subscribers13 August 2026 — 14,128 subscribers14 August 2026 — 14,123 subscribers16 August 2026 — 14,120 subscribers17 August 2026 — 14,118 subscribers18 August 2026 — 14,119 subscribers14,1195 August 202618 August 2026
12 measurements spanning 13 days, net -28. 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 14,113–14,159 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
18 Aug 2026, 12:5214,119+1
17 Aug 2026, 14:3414,118-2
16 Aug 2026, 06:2214,120-3
14 Aug 2026, 14:4614,123-5
13 Aug 2026, 04:2614,128-4
12 Aug 2026, 03:2114,132-1
11 Aug 2026, 04:5114,133-9
10 Aug 2026, 01:4114,142-3
9 Aug 2026, 00:5214,145-7
7 Aug 2026, 23:3714,152-2
7 Aug 2026, 00:0014,154+7
5 Aug 2026, 18:1914,147first reading

Engagement

18 posts held, back to 13 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 23 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
10.9%
avg views ÷ 14,119 subscribers
Avg views / post
1,540
2 posts measured
Reaction rate
1.95%
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
WindowRolling 30 days · latest post in window 31 July 2026
Posts held18 (13 May 202631 July 2026)
Views total3,080
Reactions total60
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken18 Aug 2026, 07:05 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

433 reactions across 17 posts, in 10 distinct kinds. The most used accounts for 32.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍14232.8%
12528.9%
🔥7417.1%
😁5713.2%
🤔163.70%
🦄112.54%
👎40.924%
👏20.462%
😱10.231%
🤯10.231%

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 17 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 433reactions 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 13 May 2026 to 31 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
1
across the posts below
Posts paid on
1
of 18 we hold a reading for · 6%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @productclub. 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 13 May 2026 to 31 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.

Recent posts

31 Jul 2026, 12:07 UTC≈1,440 views17 reactionsread 18 August 2026

Грачи на Product Sense 2026 Не так давно я написал заметку на тему, туда ли мы свернули с приходом ИИ, где разобрал два подхода к изменению менеджерской функции: 1. Менеджер продукта как вайбкодер, который использует ИИ для того, чтобы самому запилить продукт с 0 до 1. Человек-команда, который сам и задискаверит, и задеплоит. Его функция – сокращение PDLC (Product Development Life Cycle). Ключевое слово здесь “devel

👍9🦄52🤔1

30 Jul 2026, 08:32 UTC≈1,640 views43 reactionsread 18 August 2026

Когда палец указывает на небо, дурак смотрит на палец Знаете, я уже много лет занимаюсь образованием взрослых людей. Как приглашенный преподаватель нескольких вузов, как автор и методолог внутрикорпоративных и общих программ, так и просто лектор. Практически каждый поток студентов или отдельное мероприятие я сталкиваюсь с реакцией, которая всегда звучит одинаково: "Так то, что вы рассказываете, это всего лишь X". Эт

👍32🔥73🤯1

Signed Sergey Tikhomirov

17 Jul 2026, 11:06 UTC≈2,630 views61 reactionsread 18 August 2026
Photo

Ладно, в защиту силы вайбкодинга приложу мем, сделанный семь лет назад.

😁538

Signed Sergey Tikhomirov

16 Jul 2026, 07:30 UTC≈2,760 views20 reactionsread 18 August 2026
Photo

Поэтому встречайте мои рассуждения на тему, туда ли мы свернули с приходом ИИ, как менеджеры продуктов :) Заметка по ссылке https://telegra.ph/Tuda-li-prodaktov-vedyot-II-07-16

12👍8

Signed Sergey Tikhomirov

16 Jul 2026, 07:29 UTC≈2,350 views7 reactionsread 18 August 2026
Photo

Помните, некоторое время назад я писал про Community Sprints, где разные эксперты показывали свои кейсы? Надеюсь, кто-то сходил и вдохновился. Мне очень сильно зашли два доклада. 1) Алёны Квашниной из EMCD про автоматизацию сбора данных из соцсетей и открытых источников, потому что это фактически прямой пример реализации Кортекса для Нексуса рынка (термины из PAF). Вот ссылочка на её презентацию. 2) Димы Зборовског

4👍2😱1

Signed Sergey Tikhomirov

14 Jul 2026, 13:56 UTC≈2,210 views37 reactionsread 18 August 2026

"Пантеон" - лучшая фантастика со времён "Задачи трёх тел"! Исключительно прекрасно.

24👍10🔥2🤔1

Signed Sergey Tikhomirov

9 Jul 2026, 14:34 UTC≈1,980 viewsread 18 August 2026

Вот так читаешь, читаешь коллег, а потом узнаёшь, что они тоже читают тебя.

Signed Sergey Tikhomirov

9 Jul 2026, 14:34 UTC≈2,900 views21 reactionsread 18 August 2026
Forwarded from @aiforproductPhoto

Продуктовый подход в эпоху ИИ. Что меняется Рядом с ИИ у продакта меняется даже то, что казалось незыблемым. Например, отношение к бэклогу. А что если дело не в том, что у вас плохой бэклог? А в том, что вы вообще управляете продуктом через бэклог. Так предлагает смотреть PAF (Product Architecture Framework) от Сергея Тихомирова, продуктового архитектора и автора канала «Борода продакта». Заглянули в его фреймворк

👍165

Signed Sergey Tikhomirov

5 Jul 2026, 11:42 UTC≈3,720 views30 reactionsread 18 August 2026
Video

Пару дней назад Байрам Аннаков в своём канале EDU написал: Вы наверное заметили, что я сильно реже стал писать сюда. Я задумался о 2х вещах, на которые пока до конца не ответил для себя: 1) Что реально-системно нового произошло в мире AI, что не создает FOMO, а дает продуктивный и практичный ответ на вопрос: о чем я должен подумать или что попробовать, чтобы стать конкурентоспособнее в этом мире? 2) Что из этого я

🔥16👍8😁3👏21

Signed Sergey Tikhomirov

30 Jun 2026, 13:36 UTC≈2,580 views20 reactionsread 18 August 2026
Photo

На прошлой неделе мы с Димой Капаевым записали выпуск подкаста "На глубине" про управление продуктом в эпоху ИИ, где я рассказал об основных концепциях AI Product Operations по версии моего фреймворка PAF. Мы обсудили, зачем вообще PAF нужен и какую задачу решает, насколько он сложен для внедрения, кто такие продуктовые инженеры и вообще насколько ИИ делает эффективным процесс product discovery. Дима Капаев - один

👍9🦄65

Signed Sergey Tikhomirov

23 Jun 2026, 11:03 UTC≈2,970 views24 reactionsread 18 August 2026
Photo

Три с половиной года назад, когда очередной раз анализировал ежегодное исследование Product Sense, я отметил, что наступил кризисный период в российском продуктовом телеграм сообществе. Рынок внимания консолидировался: есть "голубой огонёк" из каналов с большой аудиторией и множество небольших каналов специалистов, которым есть что сказать и чем поделиться, но которые практически никак не могут это сделать по причине

9👍8🔥6😁1

Signed Sergey Tikhomirov

18 Jun 2026, 09:26 UTC≈3,320 views12 reactionsread 18 August 2026
Photo

Большая конференция "AI Skills" про AI-юзкейсы из Miro, Nebius, Avito и других топовых компаний ⚡️ Всем привет! Вдохновившись постом про AI Product Operations, мне предложили поучаствовать в онлайн-конференции AI Skills, где продакты, маркетологи и инженеры поделятся своим опытом использования ИИ. В своём выступлении я расскажу, туда ли мы свернули с приходом ИИ, как менеджеры продуктов. И вкратце расскажу про ядро

5🔥4👍3

Signed Sergey Tikhomirov

Showing the 12 most recent of 18 posts we hold for @productclub. 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 — 252,941 of 1,549,376entries 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

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Republishes

Channels on the register whose posts this channel has forwarded.

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 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.

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

“Борода продакта” (@productclub), 14,119 subscribers as measured 18 August 2026. Telegram Register, tgregister.com/channel/productclub.

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.