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Channel

Data Science | Machinelearning [ru]

@devsp

On this record: Topic · Observations · Channels linked by matching posts · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry

19,659subscribers

-230 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-1001121902388
TypeChannel
Username@devsp
DescriptionВсе о Data Science, машинном обучении и искусственном интеллекте: от базовой теории до cutting-edge исследований и LLM. MAX - https://max.ru/devsp Личный блог - @just_genych По вопросам рекламы или разработки - @g_abashkin РКН: https://vk.cc/cJPGXD
Created8 February 2018 — measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live9 November 2026
Measurements held62
Confirmed unchanged1 time, most recently 9 November 2026
On Telegramt.me/devsp

Topic

Technology — 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 10 August 2026 and assigned it the closest of 31 fixed categories, at 97% 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.

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 in near-verbatim form on 6 other registered channels. This entry also sits inside a group of 7 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts 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
@devsp/5777 · this entry25 Jul 2026, 10:17 UTC@webdevlair/441325 Jul 2026, 10:17 UTC1.00under a minute
@webdevlair/441826 Jul 2026, 07:37 UTC@devsp/5780 · this entry26 Jul 2026, 07:37 UTC1.00under a minute
@webdevlair/442227 Jul 2026, 19:27 UTC@devsp/5783 · this entry27 Jul 2026, 19:27 UTC1.00under a minute
@devsp/5786 · this entry29 Jul 2026, 06:07 UTC@webdevlair/442629 Jul 2026, 06:07 UTC1.00under a minute
@webdevlair/443330 Jul 2026, 16:37 UTC@devsp/5789 · this entry30 Jul 2026, 16:37 UTC1.00under a minute
@devsp/5790 · this entry31 Jul 2026, 14:37 UTC@webdevlair/443531 Jul 2026, 14:37 UTC1.00under a minute
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
ChannelMatching postsText overlapTypical gapPublished first
@webdevlair9 (8/8 hand-verifiable sample passed)1.00under a minute@webdevlair (5–4)
@frontendnoteschannel_ru9 (8/8 hand-verifiable sample passed)1.00under a minutethis entry (5–4)
@python_tesst9 (8/8 hand-verifiable sample passed)1.00under a minute@python_tesst (5–4)
@we_use_js8 (8/8 hand-verifiable sample passed)1.00under a minuteeven
@true_js7 (7/7 hand-verifiable sample passed)1.00under a minutethis entry (4–3)
@qa_logia6 (6/6 hand-verifiable sample passed)1.00under a minute@qa_logia (5–1)

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 26 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 July 2026 to 7 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).

Of the group of 7 channels, the one whose matching posts we hold earliest is @devsp — which is this entry. That is a statement about our reading window, not a claim of authorship.

Recorded under the keys clone_copy · clone_mutual, 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.

Channels linked by matching posts

This channel is in a group of 8 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.

Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim 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

19,65919,88919,7746 August 20269 November 2026
62 measurements spanning 96 days, net -230. 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 19,625–19,924 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 62
Measured (UTC)SubscribersChange
9 Nov 2026, 21:5919,659-13
8 Nov 2026, 13:1819,672-5
7 Nov 2026, 08:1619,677-3
6 Nov 2026, 06:1119,680-2
5 Nov 2026, 02:5619,682-1
4 Nov 2026, 05:1119,683-5
3 Nov 2026, 06:2119,688-7
2 Nov 2026, 08:3719,695-5
31 Oct 2026, 04:3819,700-7
30 Oct 2026, 06:5619,707-7
29 Oct 2026, 09:1819,714-4
28 Oct 2026, 10:0019,718-1
27 Oct 2026, 08:0719,719+2
26 Oct 2026, 07:4619,717+15
25 Oct 2026, 05:2719,702-10
24 Oct 2026, 07:1619,712-6
23 Oct 2026, 10:2519,718-9
22 Oct 2026, 08:5019,727-8
21 Oct 2026, 02:5419,735-4
19 Oct 2026, 22:5619,739first reading

Engagement

117 posts held, back to 23 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 81 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
6.83%
avg views ÷ 19,659 subscribers
Avg views / post
1,340
35 posts measured
Reaction rate
0.272%
reactions ÷ views · ER floor
Posts in window
36
of 117 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 21 of 35 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 10 November 2026
Posts held117 (23 July 2026 – 10 November 2026)
Views total46,962
Reactions total83
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Nov 2026, 03:45 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
≈1,010
Videos
≈61
Links
≈3,930

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. 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
3m 45s
Average length
56s

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

621 reactions across 84 posts, in 8 distinct kinds. The most used accounts for 39.1% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
😁24339.1%
❤16426.4%
👍9114.7%
🔥6811.0%
🐳203.22%
👀182.90%
👎142.25%
⚡30.483%

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 92 of the 117 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 624 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 117 most recent posts we hold, published 23 July 2026 to 10 November 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
1.71%
2 of 117 posts carry an ad marker
Regulatory tokens
2
posts carrying an erid · 2 distinct tokens
Median views · ads
955
over 2 measured posts
Median views · rest
1,540
over 114 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
2SDnjcsy7o9123 July 202623 July 2026
2W5zFGB6LM711 September 20261 September 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 117 most recent posts we hold, published 23 July 2026 to 10 November 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

Telegram’s own ‘edited’ marker: 0 of 36 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).

10 Nov 2026, 17:07 UTC542 viewsread 11 November 2026
Photo

Идея — ИИ — авария: в каком мире мы теперь живём Материал про то, как существовать в новом мире ИИ и какие косяки вылезают на практике. На что реально смотреть, когда каждая собака орёт про ИИ, а директор впихивает его в дела всё злее. Можно до посинения спорить, случилась ли революция и вообще что-то поменялось. Но инструменты уже не откатить — как и мысли у людей в головах. Часть этих мыслей решает, останешься ли…

10 Nov 2026, 15:07 UTC666 viewsread 11 November 2026
Photo

Кто в IT платит выше рынка? И какой оффер считается хорошим? Предлагаем опытным IT-специалистам поделиться мнением о зарплатах и бенефитах в крупных технологических компаниях. Кто, на ваш взгляд, платит выше рынка? У кого привлекательнее премии? Насколько важны ДМС, спорт, питание и другие бонусы при выборе работы? 👉 Поделиться мнением: https://clck.ru/3WNjfX

10 Nov 2026, 13:07 UTC906 viewsread 11 November 2026
Photo

Anthropic запилила гайд: какую модель брать под конкретную задачу Anthropic выкатила гайд, где разжёвано, какую именно ИИ-модель тащить под конкретные задачи. И там прямо сказано: самая жирная по мощности система — не всегда лучший вариант в ряде случаев. Читать далее 👉 Telegram | Max | 🌥 Claude и другие ИИ

10 Nov 2026, 10:35 UTC950 viewsread 11 November 2026
Photo

Вышло исследование про то, как масштабировать мультиагентные системы. Главный головняк в этой теме сейчас один: заставить толпу ИИ-агентов нормально координироваться и тащить задачу вместе, а не тянуть одеяло каждый в свою сторону. Как обычно устроено? Разработчик сверху рисует план: вот этот агент делает вот это, вот тут они перекидываются данными. И вся эта схема, как правило, работает одинаково — хоть задачу поме…

9 Nov 2026, 17:07 UTC978 viewsread 11 November 2026
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9 Nov 2026, 09:07 UTC≈1,180 views1 reactionsread 11 November 2026
Photo

Прогнал Kimi AI, Qwen, DeepSeek и Grok по текстам. Результаты — ниже Дай нейросетке статью на вычитку — назад прилетит вылизанный текст, в котором автора уже не читается. В препринте 2026 года исследователи из Berkeley, Google DeepMind и других организаций показали: ИИ-модели перекручивают смысл даже тогда, когда их просят только поправить грамматику. Kimi AI, Qwen, DeepSeek и Grok всё чаще суют в тексты вместо Cha…

❤1

8 Nov 2026, 17:07 UTC≈1,230 views2 reactionsread 11 November 2026
Photo

Как я перекроил свою CRM без прогеров По рассказу автора, в его CRM шесть сервисов накручены вокруг одной базы, и команда кодеров пилила это годами. Плюс SQLite-базы рядом, скрипты, тесты и поддержка — всё это докручивал Claude Code. Теперь, с его слов, Claude Code сам тащит весь этот воз: от ковыряния в чужом коде до новых доработок. Читать далее 👉 Telegram | Max | 🌥 Claude и другие ИИ

🔥2

8 Nov 2026, 14:51 UTC≈1,240 viewsread 11 November 2026
Photo

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6 Nov 2026, 17:07 UTC≈1,430 views0 reactionsread 11 November 2026
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6 Nov 2026, 13:07 UTC≈1,370 views2 reactionsread 11 November 2026
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Alibaba обкатала самообучающихся ИИ-агентов на живом A/B-тесте Alibaba выкатила исследование про самообучающихся ИИ-агентов — и не в песочнице, а в реальном A/B-тесте с участием больше 200 тысяч продавцов. Система SPAR рулит B2B-продажами на площадке Alibaba 1688: конверсия подскочила на 15,8%, а продавцов с высокой заинтересованностью стало больше на 35,8%. Фишка SPAR — учится на двух уровнях и при этом не трогает…

👍2

6 Nov 2026, 09:07 UTC≈1,290 viewsread 11 November 2026
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5 Nov 2026, 13:07 UTC≈1,460 views0 reactionsread 11 November 2026
Photo

LLM научились думать до того, как выдать ответ. И теперь у разрабов очередной головняк, знакомый каждому: сколько времени, чёрт побери, надо думать над конкретной таской. В «Союзе рыжих» у Конан Дойла Холмс за пару секунд считывает по рукам, манжетам и булавке Джабеза Уилсона, что тот нюхает табак, состоит в масонской ложе, раньше вкалывал физически, бывал в Китае и много писал. А саму историю Уилсона обзывает «зада…

Showing the 12 most recent of 117 posts we hold for @devsp. 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 10 posts on @devsp 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
15 August 2026
Most recent seen missing
2 September 2026
Median time before seen missing
2 days
Seen missing in the last 30 days
0

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 10 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.

Channels Telegram recommends alongside this one

Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

Machinelearning
@ai_machinelearning_big_data · 275,659
#1
Data Secrets
@data_secrets · 96,035
#2
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
#3
Machine learning Interview
@machinelearning_interview · 30,560
#4
Математика Дата саентиста
@data_math · 14,419
#5
DeepSchool
@deep_school · 10,723
#6
Машинное обучение RU
@machinelearning_ru · 18,339
#7
Библиотека дата-сайентиста | Data Science, Machine learning, анализ данных, машинное обучение
@dsproglib · 18,290
#8
эйай ньюз
@ai_newz · 98,262
#9
Data Science
@datascienceiot · 42,496
#10
Big Data AI
@bigdatai · 18,329
#11
Yandex for ML
@yandexforml · 19,136
#12
Dealer.AI
@dealerAI · 17,113
#13
Нескучный Data Science
@not_boring_ds · 12,081
#14
Сиолошная
@seeallochnaya · 81,254
#15
Data Science Jobs
@datascienceml_jobs · 21,642
#16
Библиотека собеса по Data Science | вопросы с собеседований
@ds_interview_lib · 4,459
#17
Нейроканал
@neuro_channel · 12,073
#18
Data Science Jobs
@datasciencejobs · 22,587
#19
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
#20
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 15,139
#21
🏆 AIeron | Data Feeling
@datafeeling · 14,208
#22
About Python [ru]
@python_tesst · 6,377
#23
Машинное обучение. Книги по программированию
@maschinelearning · 10,849
#24
Машиннное обучение | Наука о данных Библиотека
@machinelearning_books · 16,955
#25
Data Science. SQL hub
@sqlhub · 36,025
#26
Start Career in DS
@start_ds · 11,463
#27
Время Валеры
@cryptovalerii · 32,082
#28
Denis Sexy IT 🤖
@denissexy · 140,753
#29
Python вопросы с собеседований
@python_job_interview · 24,906
#30
LLM под капотом
@llm_under_hood · 30,219
#31
Python/ django
@pythonl · 58,287
#32
Библиотека задач по Data Science | тесты, код, задания
@ds_problems_lib · 3,966
#33
что-то на DL-ском
@nadlskom · 5,152
#34
ODS #jobs
@odsjobs · 14,582
#35
Data Secrets | Карьера
@data_secrets_career · 7,395
#36
Machine Learning | Нейронные сети, ИИ, Big Data
@ML_secrets · 7,365
#37
Neural Networks | Нейронные сети
@neural · 11,724
#38
Техножрица 👩‍💻👩‍🏫👩‍🔧
@tech_priestess · 15,110
#39
[PYTHON:TODAY]
@python2day · 63,348
#40
XOR
@xor_journal · 195,549
#41
DATApedia | Data science
@ds_wiki · 3,183
#42
BOGDANISSSIMO
@bogdanisssimo · 7,019
#43
Институт AIRI
@airi_research_institute · 13,977
#44
🏄 Соревновательный Data Science | Kaggle | Чемпионаты
@data_science_winners · 3,366
#45
Старший Авгур
@senior_augur · 8,372
#46
ML for Value / Ваня Максимов
@ml4value · 5,676
#47
Complete AI
@complete_ai · 7,887
#48
Борис опять
@boris_again · 17,963
#49
PyMagic
@pymagic · 5,521
#50
Инжиниринг Данных
@rockyourdata · 23,685
#51
Mashkka про Data Science
@mashkka_ds · 5,305
#52
Kali Novskaya
@rybolos_channel · 19,136
#53
Жёлтый AI
@zheltyi_ai · 9,371
#54
Аналитик данных
@dataanlitics · 6,386
#55
Reliable ML
@reliable_ml · 6,606
#56
Artificial Intelligence
@ArtificialIntelligencedl · 16,815
#57
GitHub Community
@github · 154,995
#58
grokaem себя
@grokaem_seby · 2,759
#59
DataFrog: Data Science
@datasfrog · 1,829
#60
Библиотека питониста | Python, Django, Flask
@pyproglib · 36,972
#61
Просто Python | Data science | ML | AI
@pro100_python · 9,447
#62
Senior Python Developer
@seniorpy · 39,134
#63
Big Data Science [RU]
@bdscience_ru · 1,602
#64
New Yorko Times
@new_yorko_times · 10,720
#65
Python RU
@pro_python_code · 12,302
#66
Библиотека программиста
@proglibrary · 77,612
#67
Kantor.AI
@kantor_ai · 12,215
#68
BZD • Книги для программистов
@bzd_channel · 36,213
#69
AI для Всех
@nn_for_science · 15,453
#70
Хабр
@habr_com · 134,497
#71
Dev & ML Connectable Jobs
@dev_connectablejobs · 29,084
#72
DLStories
@dl_stories · 15,546
#73
Нескучный Data Science Jobs
@not_boring_ds_jobs · 9,430
#74
ML — это ОК
@mlvok · 2,094
#75
Стать специалистом по машинному обучению
@toBeAnMLspecialist · 10,879
#76
Neurohive
@neurohive · 5,628
#77
Datalytics
@datalytx · 8,783
#78
Технотренды
@techno_media · 928,461
#79
ML Underhood
@MLunderhood · 5,064
#80
addmeto
@addmeto · 72,001
#81
Душный NLP
@stuffyNLP · 6,718
#82
Пристанище Дата Сайентиста
@renat_alimbekov · 6,287
#83
karpov.courses
@KarpovCourses · 27,313
#84
Neural Shit
@NeuralShit · 54,643
#85
GigaChat
@official_gigachat · 303,004
#86
Physics.Math.Code
@physics_lib · 146,676
#87
CodeCamp
@codecamp · 177,629
#88
Простой Python | Программирование
@python_piton_javascript · 123,691
#89
Заметки Computer Vision инженера
@CVML_team · 6,118
#90
Метаверсище и ИИще
@cgevent · 53,591
#91

Read from Telegram’s recommendation API, most recently 30 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

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 · 70,139
Telegram ranks this channel #12 of 94 here — alongside 93 others — read 20 August 2026
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
Telegram ranks this channel #13 of 93 here — alongside 92 others — read 25 August 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #14 of 76 here — alongside 75 others — read 29 August 2026
Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #24 of 87 here — alongside 86 others — read 2 September 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #26 of 90 here — alongside 89 others — read 15 September 2026
GeekBrains
@geekbrains_ru · 24,512
Telegram ranks this channel #28 of 83 here — alongside 82 others — read 15 September 2026
Machine learning Interview
@machinelearning_interview · 30,560
Telegram ranks this channel #33 of 98 here — alongside 97 others — read 7 September 2026
Python Academy
@python_academy · 43,823
Telegram ranks this channel #33 of 87 here — alongside 86 others — read 28 August 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #34 of 97 here — alongside 96 others — read 24 September 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #37 of 84 here — alongside 83 others — read 22 August 2026
Senior Python Developer
@seniorpy · 39,134
Telegram ranks this channel #39 of 92 here — alongside 91 others — read 30 August 2026
Machinelearning
@ai_machinelearning_big_data · 275,659
Telegram ranks this channel #39 of 95 here — alongside 94 others — read 10 August 2026
Data Science Jobs
@datasciencejobs · 22,587
Telegram ranks this channel #41 of 93 here — alongside 92 others — read 21 September 2026
Pythonist.ru - образование по питону
@pythonist_ru · 23,873
Telegram ranks this channel #42 of 82 here — alongside 81 others — read 17 September 2026
Sber AI
@SberAIScience · 26,512
Telegram ranks this channel #46 of 95 here — alongside 94 others — read 10 September 2026
Python обучающий
@pythonist24 · 55,221
Telegram ranks this channel #46 of 88 here — alongside 87 others — read 23 August 2026
Data Secrets
@data_secrets · 96,035
Telegram ranks this channel #58 of 98 here — alongside 97 others — read 17 August 2026
Однажды в трендах
@trendo · 66,478
Telegram ranks this channel #62 of 90 here — alongside 89 others — read 20 August 2026
Библиотека питониста | Python, Django, Flask
@pyproglib · 36,972
Telegram ranks this channel #64 of 84 here — alongside 83 others — read 1 September 2026
Python Hacks
@python_secrets · 40,332
Telegram ranks this channel #65 of 82 here — alongside 81 others — read 30 August 2026
Простой Python | Программирование
@python_piton_javascript · 123,691
Telegram ranks this channel #66 of 84 here — alongside 83 others — read 13 August 2026
Python Learning
@Python_per_month · 27,879
Telegram ranks this channel #67 of 86 here — alongside 85 others — read 9 September 2026
Python Job | Вакансии | Стажировки
@job_python · 23,452
Telegram ranks this channel #84 of 96 here — alongside 95 others — read 19 September 2026

This channel appears in 23 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 9 November 2026 — this entry's latest reading, not the date you are reading this.

“Data Science | Machinelearning [ru]” (@devsp), 19,659 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/devsp.

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.