🔥 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…

Channel
Data Science
@datascienceiot
On this record: Growth · Engagement · What this channel posts · Advertising · Posts · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry
42,492subscribers
+250 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
Register entry
| Telegram ID | -1001131189982 |
|---|---|
| Type | Channel |
| Username | @datascienceiot |
| Description | DS По всем вопросам- @haarrp @ai_machinelearning_big_data - machine learning @pythonl - Python @itchannels_telegram - 🔥 best it channels @ArtificialIntelligencedl - AI @pythonlbooks-📚 @programming_books_it -📚 Реестр РКН: https://clck.ru/3Fk3zS |
| Created | 26 July 2017 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 21 August 2026 |
| Measurements held | 16 |
| Confirmed unchanged | 1 time, most recently 21 August 2026 |
| On Telegram | t.me/datascienceiot |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 21 Aug 2026, 23:58 | 42,492 | +3 |
| 20 Aug 2026, 20:44 | 42,489 | +19 |
| 19 Aug 2026, 20:02 | 42,470 | -3 |
| 18 Aug 2026, 17:22 | 42,473 | +24 |
| 17 Aug 2026, 13:53 | 42,449 | +9 |
| 16 Aug 2026, 08:15 | 42,440 | +23 |
| 14 Aug 2026, 18:06 | 42,417 | +14 |
| 13 Aug 2026, 12:06 | 42,403 | +3 |
| 12 Aug 2026, 09:23 | 42,400 | +22 |
| 11 Aug 2026, 08:46 | 42,378 | +35 |
| 10 Aug 2026, 10:32 | 42,343 | +29 |
| 9 Aug 2026, 08:43 | 42,314 | +37 |
| 8 Aug 2026, 06:12 | 42,277 | +44 |
| 7 Aug 2026, 03:00 | 42,233 | -9 |
| 6 Aug 2026, 03:30 | 42,242 | no change |
| 6 Aug 2026, 03:23 | 42,242 | first reading |
Engagement
27 posts held, back to 7 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 36 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 8.48%
- avg views ÷ 42,492 subscribers
- Avg views / post
- 3,600
- 17 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 17
- of 27 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.
| Window | Rolling 30 days · latest post in window 22 August 2026 |
|---|---|
| Posts held | 27 (7 July 2026 – 22 August 2026) |
| Views total | 61,276 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 22 Aug 2026, 15:28 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
- ≈1,750
- Videos
- ≈4
- Links
- ≈2,150
Lifetime counters from Telegram’s own channel header, read 22 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.
Advertising
- Ad load
- 11.1%
- 3 of 27 posts carry an ad marker
- Regulatory tokens
- 3
- posts carrying an erid · 3 distinct tokens
- Median views · ads
- 2,100
- over 3 measured posts
- Median views · rest
- 4,850
- over 24 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.
| erid | Posts | First seen | Last seen |
|---|---|---|---|
| 2VtzqxCfpUj | 1 | 21 August 2026 | 21 August 2026 |
| 2W5zFGRNGWm | 1 | 16 July 2026 | 16 July 2026 |
| 2W5zFJwEKww | 1 | 18 August 2026 | 18 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 27 most recent posts we hold, published 7 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
What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics 5× context compression did surprisingly little to GPT-5.5’s final task result. 📓 Read @datascienceiot
Вырастили целое дерево из агентов 🌳 Мы в Авито разработали единую платформу для процессов. Самые лучшие и популярные становятся агентами. В итоге каждый сотрудник может создать свой процесс или воспользоваться готовым. Классно? Не то слово! Поэтому мы написали статью, как создавали это решение. О чём узнаете, если прочитаете: 🔸как устроена архитектура платформы, 🔸как процесс становится агентом, 🔸как измерять качест…
Deep systemic analysis of AI constraints from context to internal weight editing. 📂 PDF #AIRedTeaming #AISafety #LLMs #AIJailbreak #Pliny
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.…
Title: "A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing" arxiv. org/abs/2608.13573
Часто заказчики отказываются от внедрения ИИ-ассистентов из-за «грязных» входных данных: PDF с таблицами или сканы плохо индексируются, нейросеть галлюцинирует 🤯 А что, если был бы ИТ-продукт, который не просто распознает символы, а сохраняет логику документа, превращая его в готовый формат для RAG (Markdown/JSON)❔ Это позволило бы клиентам в разы быстрее выводить ИИ-продукты в эксплуатацию и закрыть вопрос импортоз…
From Worm to Human: Scaling Brain Emulation 📓 Read @datascienceiot
Kimi Delta Attention. — a minimal implementation of the Kimi K3 architecture. http://k-a.in/KDA.html
Today's AI systems learn mostly by passively absorbing data, but the brain builds intelligence in the reverse order. Grounded world models in biological organisms and future embodied AI 📓 Read @datascienceiot
Займи слот ИТ-Пикником от Т-Банка 8 августа — время отложить ноутбуки и встретиться офлайн на ИТ-Пикнике от Т-Банка в музее-заповеднике «Коломенское». Вот сколько всего запланировано: — научпоп-лекции; — мастер-классы; — дискуссии об ИИ и больших языковых моделях; — доклады о кибербезопасности; — примеры, как данные из логов становятся решениями; — много музыки — Сream Soda, IOWA, LAB Антона Беляева и другие артист…
Google gave AI consciousness and it aligned with human beliefs across every domain they tested. They took consciousness away and the model rejected them. 📓 Read @datascienceiot
Showing the 12 most recent of 27 posts we hold for @datascienceiot. 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 — 36,833 of 1,583,249entries 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 3 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.
Names
Channels on the register whose handles appear in this channel's posts.
@ai_machinelearning_big_data · 284,5231 postAndroid разработка
@android_its · 4,8421 postBig Data AI
@bigdatai · 18,1491 postC++ Academy
@cpluspluc · 15,4681 postC# 1001 notes
@csharp_1001_notes · 6,5561 postАнализ данных (Data analysis)
@data_analysis_ml · 50,4871 postМатематика Дата саентиста
@data_math · 14,1341 postDevOps
@DevOPSitsec · 23,5141 postАнглийский для программистов
@english_forprogrammers · 8,2591 postGameDev Pulse
@GameDEV · 4,1251 postGolang
@Golang_google · 40,2901 postJava Books
@java_library · 14,2381 postJavascript
@javascriptv · 17,1921 postLinux Academy
@linuxacademiya · 28,2501 postKali Linux
@linuxkalii · 55,0911 postMachine learning Interview
@machinelearning_interview · 30,1521 postIT мемы | Мемы программиста
@memes_prog · 6,2491 postМобильная разработка
@mobdevelop · 3,8491 postPHP Academy
@phpshka · 9,2261 postPython вопросы с собеседований
@python_job_interview · 24,7861 postPython/ django
@pythonl · 58,9001 postRust
@rust_code · 8,7241 postData Science. SQL hub
@sqlhub · 35,8731 postИскусственный интеллект. Высокие технологии
@vistehno · 72,2221 post
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.
Handles this channel named that no longer answer
- Dead references
- 3
- handles named in this channel’s posts, vacant today
- Evidenced gone
- 0
- we ourselves saw one of these resolve, at some point
- Never seen alive
- 3
- vacant every time we have ever looked
@datascienceiot named 3 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
named in 6 posts, 8 August 2026 – 16 August 2026
named in 6 posts, 8 August 2026 – 16 August 2026
named in 6 posts, 8 August 2026 – 16 August 2026
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.
@physics_lib · 145,882
Telegram ranks this channel #6 of 72 here — alongside 71 others — read 13 August 2026
@pythonl · 58,900
Telegram ranks this channel #7 of 84 here — alongside 83 others — read 22 August 2026
@computer_science_and_programming · 140,868
Telegram ranks this channel #7 of 87 here — alongside 86 others — read 13 August 2026
@vistehno · 72,222
Telegram ranks this channel #10 of 94 here — alongside 93 others — read 20 August 2026
@ai_machinelearning_big_data · 284,523
Telegram ranks this channel #17 of 95 here — alongside 94 others — read 10 August 2026
@CodeProgrammer · 68,188
Telegram ranks this channel #30 of 84 here — alongside 83 others — read 20 August 2026
@datasciencefree · 67,978
Telegram ranks this channel #43 of 88 here — alongside 87 others — read 20 August 2026
@datasciencefun · 77,305
Telegram ranks this channel #48 of 85 here — alongside 84 others — read 19 August 2026
@Artificial_intelligence_in · 65,539
Telegram ranks this channel #58 of 90 here — alongside 89 others — read 21 August 2026
@dsabooks · 59,037
Telegram ranks this channel #61 of 89 here — alongside 88 others — read 22 August 2026
@ieofficial · 66,331
Telegram ranks this channel #64 of 92 here — alongside 91 others — read 21 August 2026
This channel appears in 11 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 21 August 2026 — this entry's latest reading, not the date you are reading this.
“Data Science” (@datascienceiot), 42,492 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/datascienceiot.
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.