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Channel

Аналитик данных

@dataanlitics

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

6,386subscribers

+188 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001657726685
TypeChannel
Username@dataanlitics
CreatedBetween 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live9 November 2026
Measurements held24
Confirmed unchanged1 time, most recently 9 November 2026
On Telegramt.me/dataanlitics

Topic

Technology — 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 12 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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

6,1966,3876,291.57 August 2026 — 6,198 subscribers7 August 2026 — 6,198 subscribers7 August 2026 — 6,200 subscribers10 August 2026 — 6,198 subscribers13 August 2026 — 6,196 subscribers16 August 2026 — 6,201 subscribers19 August 2026 — 6,207 subscribers23 August 2026 — 6,224 subscribers25 August 2026 — 6,231 subscribers28 August 2026 — 6,232 subscribers31 August 2026 — 6,233 subscribers3 September 2026 — 6,239 subscribers16 September 2026 — 6,250 subscribers25 September 2026 — 6,253 subscribers4 October 2026 — 6,252 subscribers10 October 2026 — 6,249 subscribers14 October 2026 — 6,255 subscribers17 October 2026 — 6,257 subscribers20 October 2026 — 6,269 subscribers23 October 2026 — 6,383 subscribers27 October 2026 — 6,386 subscribers30 October 2026 — 6,387 subscribers5 November 2026 — 6,385 subscribers9 November 2026 — 6,386 subscribers6,3867 August 20269 November 2026
24 measurements spanning 94 days, net +188. 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 6,167–6,416 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 24
Measured (UTC)SubscribersChange
9 Nov 2026, 10:586,386+1
5 Nov 2026, 15:396,385-2
30 Oct 2026, 11:566,387+1
27 Oct 2026, 03:376,386+3
23 Oct 2026, 23:366,383+114
20 Oct 2026, 15:066,269+12
17 Oct 2026, 18:576,257+2
14 Oct 2026, 08:276,255+6
10 Oct 2026, 11:566,249-3
4 Oct 2026, 16:416,252-1
25 Sept 2026, 02:206,253+3
16 Sept 2026, 23:176,250+11
3 Sept 2026, 16:406,239+6
31 Aug 2026, 07:386,233+1
28 Aug 2026, 16:456,232+1
25 Aug 2026, 23:096,231+7
23 Aug 2026, 05:146,224+17
19 Aug 2026, 18:026,207+6
16 Aug 2026, 20:056,201+5
13 Aug 2026, 16:076,196first reading

Engagement

31 posts held, back to 31 March 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 14 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.53%
avg views ÷ 6,386 subscribers
Avg views / post
481
1 post measured
Reaction rate
1.87%
reactions ÷ views · ER floor
Posts in window
1
of 31 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 28 October 2026
Posts held31 (31 March 2026 – 28 October 2026)
Views total481
Reactions total9
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Nov 2026, 11:33 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

Video runtime
5m 35s
Average length
1m 07s

Measured directly from 5 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

158 reactions across 27 posts, in 10 distinct kinds. The most used accounts for 53.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤8453.2%
👍3320.9%
🔥2113.3%
🤔74.43%
👎63.80%
😁31.90%
💩10.633%
🙏10.633%
🤡10.633%
🤮10.633%

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 27 of the 31 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 158 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 31 most recent posts we hold, published 31 March 2026 to 28 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 1 post 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).

28 Oct 2026, 15:24 UTC481 views9 reactionsread 7 November 2026
Video

Сколько предков понадобилось, чтобы ты родился

❤7👍2

3 Oct 2026, 12:00 UTC≈1,050 views9 reactionsread 7 November 2026
Photo

🔥 Бесплатный курс по Claude Code на русском: от установки до команды агентов На GitHub выложили Claude Code PRO Course, бесплатный практический курс по Claude Code на русском языке. Он ведёт от первого запуска до настройки многоагентной работы в команде. Внутри 12 модулей в четырёх уровнях сложности: основы, рабочий процесс, расширения и масштабирование. Сначала установка, промптинг, память проекта через CLAUDE.md,…

❤5👍4

2 Oct 2026, 10:02 UTC856 views6 reactionsread 7 November 2026
Photo

🚀 LLM всё лучше пишут код. Следующая проблема - как человеку быстро понимать их результат. Несколько полезных приёмов: - Текст: попросите объяснять в стиле ASD-STE100 - это строгий язык технической документации из авиации. Обычно текст становится заметно чище и понятнее. Можно попросить «80% ASD-STE100», чтобы стиль не был слишком сухим. - Диаграммы: вместо длинного объяснения попросите схему - сложную систему част…

🔥4🤔2

30 Sept 2026, 11:38 UTC656 viewsread 7 November 2026
Forwarded from @ai_machinelearning_big_dataPhoto

⚡️ DeepSeek перенесла DeepGEMM на Huawei Ascend и выжала из железа 99,8% DeepSeek выложила DeepGEMM-Ascend, порт своей библиотеки матричных ядер на NPU Huawei Ascend 950. API остался тем же, что у оригинального DeepGEMM, а сами ядра переписаны под особенности Ascend. Поддерживаются GEMM в FP4, FP8 и BF16, grouped GEMM для MoE, MQA logits и слитые операторы MegaMoE. Плотный GEMM почти упирается в теоретический потол…

22 Sept 2026, 20:28 UTC918 views3 reactionsread 7 November 2026
Photo

👣 Google открыла исходный код AX, среды для запуска AI-агентов в продакшене. AX изолирует задачи, подключает репозитории, MCP-серверы и навыки, ограничивает сетевой доступ, а также позволяет приостанавливать и возобновлять работу агентов с сохранением состояния. Это не новый агентный фреймворк и не облачный сервис, а инфраструктурный слой для масштабирования долгоживущих агентов в Kubernetes. https://github.com/go…

❤3

15 Sept 2026, 12:01 UTC≈1,010 viewsread 7 November 2026
Photo

📘 Бесплатный 17-страничный PDF по Hidden Markov Models (HMM) Короткий материал от Stanford по одной из классических моделей машинного обучения для работы с последовательностями. Внутри разбираются: - состояния и наблюдения - transition и emission probabilities - вычисление вероятности последовательности - декодирование скрытых состояний - обучение параметров HMM - применение к последовательным данным Хороший комп…

15 Sept 2026, 09:48 UTC793 views2 reactionsread 7 November 2026
Photo

🔥 Один из лучших обучающих курсов на StepiK по SQL SQL можно знать годами и всё равно теряться, когда запрос внезапно начинает тормозить в проде. Этот курс про уровень, где ты не просто пишешь SELECT, а понимаешь, что происходит внутри MySQL, почему запрос работает медленно и как ускорить его без новых проблем. Здесь разбираются EXPLAIN, индексы, CTE, оконные функции, транзакции, аналитические паттерны, legacy SQL …

❤1🙏1

10 Sept 2026, 09:51 UTC754 views6 reactionsread 7 November 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ Hugging Face запустила агента для ML-экспериментов Платформа встроила в свой чат-бот открытого ассистента ML Intern, который берет на себя полный цикл машинного обучения по текстовому описанию задачи. Инструмент ориентирован на пользователей без профильного опыта. Для старта достаточно сформулировать идею в окне ввода текста, включить агента и он сам найдет релевантные модели и датасеты на Hugging Face Hub, в Gi…

❤6

19 Aug 2026, 12:44 UTC≈1,050 views4 reactionsread 7 November 2026

Как нормально тестировать AI-агента, если правильный ответ ещё ничего не доказывает У обычного чат-бота можно проверить финальный ответ. С агентами этого уже мало: модель может выдать вполне убедительный текст, но перед этим выбрать не тот skill, вызвать неправильный tool, потерять контекст на втором сообщении или выполнить только половину задачи. Ed Crewe описал, как они строили eval-фреймворк для агентного чат-бо…

❤3👍1

16 Aug 2026, 10:34 UTC≈1,030 views1 reactionsread 7 November 2026
Photo

⚡️ Harness Engineering - полный курс на русском Как заставить AI-агента писать код надёжно. Не «какую модель выбрать», а как спроектировать вокруг неё рабочую систему: инструкции, инструменты, среду, состояние и верификацию. Курс - от нуля до продвинутых тем: теоретическая база из 11 блоков, 14 модулей, 8 практических проектов, 14 лабораторных работ, диагностический протокол «как починить агента», библиотека готовы…

❤1

8 Aug 2026, 12:28 UTC≈1,010 viewsread 7 November 2026
Forwarded from @sqlhubPhoto

🚀 ИИ-агент ускорил SQLite до 59% меньше чем за 8 часов Ускорить SQLite хотя бы на 5% уже было бы серьёзным результатом. Это один из самых зрелых и оптимизированных проектов в мире - его команда почти 20 лет выжимает из кода каждую долю производительности. Но AI-агент KISS Sorcar менее чем за 8 часов и с затратами меньше $150 добился заметного ускорения сразу в нескольких типах нагрузки. Результаты: - 2,06× быстре…

3 Aug 2026, 14:21 UTC929 views9 reactionsread 7 November 2026
Forwarded from @rust_codePhoto

✔️ Firecrawl открыла один из самых быстрых PDF-парсеров Компания выложила в open source pdf-inspector - библиотеку на Rust, которая превращает PDF в Markdown, сохраняя структуру документа, заголовки и таблицы. По заявленным тестам, обработка одной страницы занимает около 0,002 секунды, а набор из 200 PDF был пройден примерно за 2,8 секунды. Главное здесь не одна скорость: парсер старается сохранить исходную логику…

❤5🔥4

Showing the 12 most recent of 31 posts we hold for @dataanlitics. 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.

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

Names

Channels on the register whose handles appear in this channel's posts.

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 · 70,139
Telegram ranks this channel #11 of 94 here — alongside 93 others — read 20 August 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #13 of 76 here — alongside 75 others — read 29 August 2026
Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #15 of 87 here — alongside 86 others — read 2 September 2026
Анализ данных (Data analysis)
@data_analysis_ml · 51,923
Telegram ranks this channel #18 of 93 here — alongside 92 others — read 25 August 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #19 of 90 here — alongside 89 others — read 15 September 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #28 of 97 here — alongside 96 others — read 24 September 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #31 of 84 here — alongside 83 others — read 22 August 2026
Machine learning Interview
@machinelearning_interview · 30,562
Telegram ranks this channel #48 of 98 here — alongside 97 others — read 7 September 2026
Data Science | Machinelearning [ru]
@devsp · 19,651
Telegram ranks this channel #55 of 91 here — alongside 90 others — read 30 September 2026
DevOps
@DevOPSitsec · 23,892
Telegram ranks this channel #70 of 90 here — alongside 89 others — read 18 September 2026

This channel appears in 10 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.

“Аналитик данных” (@dataanlitics), 6,386 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/dataanlitics.

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.