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Channel

Машинное обучение RU

@machinelearning_ru

On this record: Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Telegram's recommendations · Cite this entry

18,144subscribers

+51 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-1001262100203
TypeChannel
Username@machinelearning_ru
DescriptionВсе о машинном обучении админ - @workakkk @data_analysis_ml - анализ даннных @ai_machinelearning_big_data - Machine learning @itchannels_telegram -лучшие ит-каналы @pythonl - Python @pythonlbooks- python 📚 @datascienceiot - 📚 РКН: clck.ru/3FmrUw
Created23 December 2020measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live22 August 2026
Measurements held15
Confirmed unchanged1 time, most recently 22 August 2026
On Telegramt.me/machinelearning_ru

Growth

18,08218,14418,1136 August 2026 — 18,093 subscribers6 August 2026 — 18,086 subscribers7 August 2026 — 18,083 subscribers8 August 2026 — 18,082 subscribers10 August 2026 — 18,085 subscribers11 August 2026 — 18,118 subscribers12 August 2026 — 18,120 subscribers13 August 2026 — 18,118 subscribers15 August 2026 — 18,111 subscribers16 August 2026 — 18,116 subscribers17 August 2026 — 18,119 subscribers18 August 2026 — 18,131 subscribers19 August 2026 — 18,137 subscribers20 August 2026 — 18,140 subscribers22 August 2026 — 18,144 subscribers6 August 202622 August 2026
15 measurements spanning 16 days, net +51. 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 18,073–18,153 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
22 Aug 2026, 06:0818,144+4
20 Aug 2026, 20:2818,140+3
19 Aug 2026, 22:0618,137+6
18 Aug 2026, 20:1518,131+12
17 Aug 2026, 21:2218,119+3
16 Aug 2026, 21:5218,116+5
15 Aug 2026, 12:4318,111-7
13 Aug 2026, 23:3718,118-2
12 Aug 2026, 17:2218,120+2
11 Aug 2026, 18:0318,118+33
10 Aug 2026, 15:0518,085+3
8 Aug 2026, 16:2018,082-1
7 Aug 2026, 17:1818,083-3
6 Aug 2026, 20:2718,086-7
6 Aug 2026, 03:2218,093first reading

Engagement

37 posts held, back to 20 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 36 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
10.6%
avg views ÷ 18,144 subscribers
Avg views / post
1,920
32 posts measured
Reaction rate
0.698%
reactions ÷ views · ER floor
Posts in window
32
of 37 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 30 of 32 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 22 August 2026
Posts held37 (20 July 202622 August 2026)
Views total61,477
Reactions total409
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken23 Aug 2026, 10:38 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
1,770
Videos
252
Links
2,200

Lifetime counters from Telegram’s own channel header, read 23 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
5m 44s
Average length
1m 26s

Measured directly from 4 videos 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

479 reactions across 35 posts, in 11 distinct kinds. The most used accounts for 28.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
😁13728.6%
13628.4%
👍11724.4%
🔥5711.9%
👎112.30%
🤔91.88%
👏51.04%
🎉20.418%
🤩20.418%
🤯20.418%
🥰10.209%

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 35 of the 37 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 479reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 37 most recent posts we hold, published 20 July 2026 to 22 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.

Advertising

Ad load
8.11%
3 of 37 posts carry an ad marker
Regulatory tokens
3
posts carrying an erid · 3 distinct tokens
Median views · ads
1,460
over 3 measured posts
Median views · rest
1,760
over 34 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
2Vtzqx2EBP9114 August 202614 August 2026
2Vtzqxf37Xp125 July 202625 July 2026
2W5zFJBbjsw118 August 202618 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 37 most recent posts we hold, published 20 July 2026 to 22 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

22 Aug 2026, 11:32 UTC813 views23 reactionsread 23 August 2026
Video

Автор `uv` пришёл в OpenAI и сразу ускорил запуск Codex CLI примерно в 25 раз Астрэл-гуру Rust, стоящий за одним из самых быстрых Python-инструментов uv, после присоединения к OpenAI взялся за производительность Codex CLI. Результат - время холодного старта сократилось примерно в 25 раз. На практике разница ощущается сразу: Codex теперь открывается практически мгновенно и субъективно стартует даже быстрее Pi и Cla

12🔥7🎉2👍2

22 Aug 2026, 09:45 UTC712 views7 reactionsread 23 August 2026
Photo

🔥 Хочешь быстрее расти в IT? Хватит учиться в одиночку Окружение решает больше, чем кажется. Собрал папки и каналы, где можно быстрее влиться в нужное направление, следить за трендами и не вариться в своём пузыре. AI: t.me/ai_machinelearning_big_data Python: t.me/pythonl Linux: t.me/linuxacademiya Хакинг: t.me/linuxkalii DevOps: t.me/DevOPSitsec Docker: https://t.me/+90Z5TAyfuNU5YmRi Golang: t.me/Golang_google Rus

3👍2🔥2

21 Aug 2026, 11:11 UTC912 views10 reactionsread 23 August 2026
Forwarded from @machinelearning_interviewPhoto

OpenCode появилась загадочная модель Ox Alpha - и первые тесты выглядят очень хорошо Новая stealth-модель Ox Alpha уже появилась у пользователей OpenCode, но её происхождение пока официально не раскрыто. В одном из первых небольших прогонов на 10 сложных DeepSWE-задачах ей приписывают около 80% успешных решений - выше Fable 5, GLM-5.3, Grok 4.6 и GPT-5.6 Sol в том же мини-тесте. Важно не путать это с официальным D

4🔥4👍2

21 Aug 2026, 09:16 UTC≈1,140 views8 reactionsread 23 August 2026
Photo

Tencent вывела свои модели перевода в OpenRouter В OpenRouter появились: • Hy-MT2-1.8B • Hy-MT2-30B-A3B Модели поддерживают перевод между 33 языками и умеют работать не только с обычным текстом, но и с контекстом, структурой и заданным стилем. Попробовать можно здесь: https://openrouter.ai/tencent

5👍3

19 Aug 2026, 13:45 UTC≈1,270 views6 reactionsread 23 August 2026
Photo

Бесплатная книга по performance engineering В Algorithmica хорошо разобрали, почему классическая оценка сложности всё хуже отражает реальную производительность на современном железе. Раньше модель была довольно логичной: процессор выполняет инструкции почти последовательно, у каждой есть своя стоимость, а значит можно примерно оценить время работы алгоритма количеством операций. Потом всё упростили до асимптотики.

👍51

18 Aug 2026, 14:02 UTC≈5,160 views18 reactionsread 23 August 2026
Video

Плачу $60 в месяц, а в итоге всё равно гоняю один и тот же баг через все три аккаунта.

😁13👍3👏2

18 Aug 2026, 11:58 UTC≈1,460 viewsread 23 August 2026
Advertisementerid 2W5zFJBbjswPhoto

Как будут развиваться технологии искусственного интеллекта в ближайшие годы? Какие исследовательские идеи уже превращаются в работающие продукты? С какими задачами сегодня сталкиваются AI-команды? 17 сентября в Москве пройдёт AI R&D DAY — конференция для исследователей, разработчиков, руководителей R&D-команд и специалистов, которые создают и внедряют передовые решения на основе искусственного интеллекта. В центре п

17 Aug 2026, 10:56 UTC≈1,830 views9 reactionsread 23 August 2026
Photo

🔥 Qwen3.8 27B уже «раздели» от большей части встроенных отказов и подготовили для локального запуска Huihui-Qwen3.8-27B-abliterated — модифицированная версия Qwen3.8-27B, где через abliteration заметно ослабили safety-фильтры и склонность модели отказываться от запросов. Авторы прямо называют реализацию экспериментальной. Теперь выложены все GGUF-квантизации, так что модель можно запускать через llama.cpp, LM Studi

👍43👏1😁1

16 Aug 2026, 10:03 UTC≈1,550 views2 reactionsread 23 August 2026
Photo

🔐 Как может работать watermarking в Claude. При генерации текста модель почти никогда не имеет ровно один «правильный» следующий токен. Обычно есть несколько вариантов с близкими вероятностями, и выбор между ними делается через sampling. Watermarking добавляет ещё один фактор: секретный ключ влияет на то, какой из подходящих токенов будет выбран в конкретной позиции. Сам текст при этом выглядит обычным. Но если п

1👍1

15 Aug 2026, 10:00 UTC≈1,440 views9 reactionsread 23 August 2026
Photo

💸 Дешёвый токен ещё не значит дешёвую модель. По исследованию AlphaSense, Kimi может стоить заметно дешевле GPT-5.6 Sol за токен, но обходиться дороже за один завершённый вопрос. Причина простая: модель тратит больше токенов на сбор контекста, повторный retrieval и длинные цепочки перед тем, как добраться до полезного ответа. Реальная формула стоимости выглядит так: цена токена × количество токенов до завершения

👍71🔥1

14 Aug 2026, 16:26 UTC≈1,450 views2 reactionsread 23 August 2026
Advertisementerid 2Vtzqx2EBP9Photo

Что скрывается за монетизацией поиска и реков в Авито 👀 Рассчитать ожидаемую выручку сложнее, чем кажется: нельзя просто умножить вероятность на ставку. На практике всё упирается в данные, метрики и бизнес-ограничения. Если вам интересно, как работает наше ранжирование, приходите на Хабр почитать и оставить комментарии.

👍2

14 Aug 2026, 10:46 UTC≈1,430 views13 reactionsread 23 August 2026

🚀 Вышла dots3-note preview - открытая MoE-модель с прицелом на долгоживущих AI-агентов. Внутри 280B параметров, из которых активны 16B, контекст 512K и мультимодальность: текст, изображения и аудио. Главная фишка - TEMPO, новый подход к RL для обучения агентов на длинных задачах. Модель учится критиковать собственные действия, оценивать промежуточные решения и лучше работать там, где одной попытки недостаточно. do

👍64🔥3

Showing the 12 most recent of 37 posts we hold for @machinelearning_ru. 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 — 44,609 of 1,584,733entries 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 5 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.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Искусственный интеллект. Высокие технологии
@vistehno · 72,222
Telegram ranks this channel #6 of 94 here — alongside 93 others — read 20 August 2026
Machinelearning
@ai_machinelearning_big_data · 284,523
Telegram ranks this channel #11 of 95 here — alongside 94 others — read 10 August 2026
Python/ django
@pythonl · 58,935
Telegram ranks this channel #14 of 84 here — alongside 83 others — read 22 August 2026
Physics.Math.Code
@physics_lib · 145,896
Telegram ranks this channel #55 of 72 here — alongside 71 others — read 13 August 2026
Data Secrets
@data_secrets · 92,333
Telegram ranks this channel #57 of 98 here — alongside 97 others — read 17 August 2026

This channel appears in 5 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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

“Машинное обучение RU” (@machinelearning_ru), 18,144 subscribers as measured 22 August 2026. Telegram Register, tgregister.com/channel/machinelearning_ru.

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.