Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 71% 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 the other topics.
Growth
25 measurements spanning 93 days, net -120. 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 8,732–8,888 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 25
Measured (UTC)
Subscribers
Change
8 Nov 2026, 16:09
8,750
-8
4 Nov 2026, 23:06
8,758
-1
1 Nov 2026, 22:09
8,759
-2
30 Oct 2026, 00:17
8,761
-1
27 Oct 2026, 00:54
8,762
-3
23 Oct 2026, 18:25
8,765
-6
20 Oct 2026, 10:20
8,771
-4
17 Oct 2026, 16:47
8,775
-5
14 Oct 2026, 18:08
8,780
-9
11 Oct 2026, 04:01
8,789
-5
5 Oct 2026, 03:59
8,794
-8
26 Sept 2026, 13:01
8,802
-18
15 Sept 2026, 20:42
8,820
-9
12 Sept 2026, 07:16
8,829
-7
8 Sept 2026, 07:21
8,836
-11
3 Sept 2026, 11:37
8,847
-1
31 Aug 2026, 15:25
8,848
-5
28 Aug 2026, 06:44
8,853
-3
25 Aug 2026, 13:27
8,856
-2
22 Aug 2026, 18:03
8,858
first reading
Engagement
26 posts held, back to 23 January 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 47 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
9.68%
avg views ÷ 8,750 subscribers
Avg views / post
847
2 posts measured
Reaction rate
0.177%
reactions ÷ views · ER floor
Posts in window
2
of 26 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 15 October 2026
Posts held
26 (23 January 2026 – 15 October 2026)
Views total
1,694
Reactions total
3
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
11 Nov 2026, 00:08 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
296
Videos
5
Links
334
Lifetime counters from Telegram’s own channel header, read 11 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
132 reactions across 23 posts, in 10 distinct kinds. The most used accounts for 37.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
49
37.1%
👎
37
28.0%
💩
22
16.7%
❤
9
6.82%
🤮
6
4.55%
🤡
5
3.79%
👌
1
0.758%
🔥
1
0.758%
🖕
1
0.758%
🤔
1
0.758%
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 26 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 132 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 26 most recent posts we hold, published 23 January 2026 to 15 October 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
Telegram’s own ‘edited’ marker: 0 of 2 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
🎥 Вебинар по C++: Магия чисел в C++: как заставить компилятор считать за нас
Программа ещё не запущена, а таблица значений уже готова. C++ позволяет выполнить часть вычислений на этапе компиляции и передать программе готовый результат.
👉 На уроке разберём:
- Как выполнять вычисления на этапе компиляции.
- Как получать числа Фибоначчи и факториал до запуска программы.
- Как поручить компилятору генерацию таблиц.
🤓 …
📚Interpretable Machine Learning with Python, 2nd Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples (2023)
✍️Автор: Serg Masis
📃Страниц: 606
Второе издание раскрывает ключевые понятия интерпретации моделей машинного обучения на основе анализа реальных данных, предоставляя вам широкий спектр навыков и инструментов для расшифровки результатов даже самых сложных моде…
🚀 Подборка полезных IT каналов в Max
Системное администрирование, DevOps 📌
https://max.ru/i_odmin Все для системного администратора
https://max.ru/bash_srv Bash Советы
https://max.ru/sysadminof Книги для админов, полезные материалы
https://max.ru/i_odmin_book Библиотека Системного Администратора
https://max.ru/i_devops DevOps: Пишем о Docker, Kubernetes и др.
https://max.ru/tipsysdmin Типичный Сисадмин
https://max…
🎥 Вебинар по C++: Паттерн многопоточного программирования «Producer-Consumer»
Когда несколько потоков одновременно работают с данными, недостаточно просто «запустить работу параллельно». Нужно правильно организовать передачу задач между потоками, не потерять данные и избежать ситуаций, когда приложение внезапно зависает.
👉 Разберём один из самых распространённых паттернов многопоточной разработки - Producer-Consume…
📚Spring Boot 3.0 Cookbook: Proven recipes for building modern and robust Java web applications with Spring Boot (2024)
✍️Автор: Felip Miguel Puig
📃Страниц: 426
Вы начнете с рецептов, демонстрирующих возможности Spring Boot. По мере продвижения вы поймете, как он помогает упростить разработку приложений, не отставая от технологических тенденций. Книга поможет вам эффективно усвоить концепции, изучить базовые REST API…
SQL Cookbook: Query Solutions and Techniques for All SQL Users
Автор: Anthony Molinaro, Robert de Graaf (2020)
Возможно, вы знаете основы SQL, но используете ли все возможности в полной мере? Программисты, использующие SQL, аналитики, специалисты по обработке данных, администраторы баз данных и даже относительно обычные пользователи SQL могут воспользоваться данным ценным руководством по решению повседневных проблем…
🚀 Подборка полезных IT каналов в Max
Системное администрирование, DevOps 📌
https://max.ru/i_odmin Все для системного администратора
https://max.ru/bash_srv Bash Советы
https://max.ru/sysadminof Книги для админов, полезные материалы
https://max.ru/i_odmin_book Библиотека Системного Администратора
https://max.ru/i_devops DevOps: Пишем о Docker, Kubernetes и др.
https://max.ru/tipsysdmin Типичный Сисадмин
https://max…
👨💻На работе код читают чаще, чем пишут. Когда смысл приходится восстанавливать по комментариям, неочевидным параметрам и скрытым договорённостям, команда теряет время, а риск ошибок растёт.
📆16 июля в 20:00 МСК на открытом уроке разберем, как переносить смысл программы в типы, сигнатуры и структуру кода. На примерах из реальных проектов участники сделают сигнатуры понятнее с помощью std::optional и enum class, упро…
📚Machine Learning For Network Traffic and Video Quality Analysis: Develop and Deploy Applications Using JavaScript and Node.js (2024)
✍️Автор: Lavesh Babooram
📃Страниц: 478
Книга начинается с углубления в NTMA, объяснения фундаментальных концепций и обзора существующих приложений и исследований в этой области. В ней также рассматриваются основы VQA и предлагается обзор последних разработок в области алгоритмов VQA. …
🔮 Асинхронный код часто воспринимается как магия: функции работают, задачи выполняются, но при сбоях или оптимизации становится непонятно, как всё устроено. Без этого сложно писать предсказуемые и эффективные решения.
❗️ На открытом уроке разберём, как Rust представляет асинхронные задачи, как компилятор преобразует async-функции и что происходит во время выполнения. Покажем, как описывать собственные асинхронные за…
📚Accelerate Model Training with PyTorch 2.X: Build more accurate models by boosting the model training process (2024)
✍️Автор: Maicon Melo Alves
📃Страниц: 230
Вы начнете с понимания того, как сложность модели влияет на время обучения, а затем узнаете о различных уровнях настройки производительности для ускорения процесса обучения. Вы также узнаете, как использовать новую функцию PyTorch для компиляции модели и ее ус…
🚀 Подборка полезных IT каналов в Max
Системное администрирование, DevOps 📌
https://max.ru/i_odmin Все для системного администратора
https://max.ru/bash_srv Bash Советы
https://max.ru/sysadminof Книги для админов, полезные материалы
https://max.ru/i_odmin_book Библиотека Системного Администратора
https://max.ru/i_devops DevOps: Пишем о Docker, Kubernetes и др.
https://max.ru/tipsysdmin Типичный Сисадмин
1C разрабо…
💩10👎3👍1
Showing the 12 most recent of 26 posts we hold for @book_for_dev. 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.
Posts recorded as deleted after publishing
We recorded 1 post on @book_for_dev as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
8 August 2026
Most recent seen missing
8 August 2026
Median time before seen missing
14 days
Seen missing in the last 30 days
0
Mentions
Named by 4 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.
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 8 November 2026 — this
entry's latest reading, not the date you are reading this.
“Книги для программистов” (@book_for_dev), 8,750 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/book_for_dev.
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.