Telegram RegisterThe public register of Telegram

Channel

Аналитик данных

@dataanlitics

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

6,198subscribers

+0 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
DescriptionАналитика данных, Дата Сеанс @workakkk - по всем вопросам
CreatedBetween 1 December 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live10 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 10 August 2026
On Telegramt.me/dataanlitics

Growth

6,1986,2006,1997 August 2026 — 6,198 subscribers7 August 2026 — 6,198 subscribers7 August 2026 — 6,200 subscribers10 August 2026 — 6,198 subscribers7 August 202610 August 2026
4 measurements spanning 3 days. 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 6,198–6,200 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
10 Aug 2026, 08:266,198-2
7 Aug 2026, 18:146,200+2
7 Aug 2026, 14:016,198no change
7 Aug 2026, 13:536,198first reading

Engagement

21 posts held, back to 31 March 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 8 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
7.96%
avg views ÷ 6,198 subscribers
Avg views / post
494
3 posts measured
Reaction rate
1.40%
reactions ÷ views · ER floor
Posts in window
3
of 21 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 2 of 3 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 8 August 2026
Posts held21 (31 March 20268 August 2026)
Views total1,481
Reactions total17
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 03:26 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
254
Videos
36
Links
232

Lifetime counters from Telegram’s own channel header, read 12 August 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.

Video runtime
4m 25s
Average length
1m 28s

Measured directly from 3 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

116 reactions across 19 posts, in 9 distinct kinds. The most used accounts for 50.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
5850.0%
👍2622.4%
🔥1512.9%
👎65.17%
🤔54.31%
😁32.59%
💩10.862%
🤡10.862%
🤮10.862%

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 19 of the 21 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 116reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 21 most recent posts we hold, published 31 March 2026 to 8 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.

Recent posts

8 Aug 2026, 12:28 UTC264 viewsread 12 August 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 UTC435 views8 reactionsread 12 August 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🔥3

19 Jul 2026, 11:02 UTC782 views9 reactionsread 12 August 2026
Forwarded from @data_analysis_mlVideo

Ох уж, эта богатая жизнь

6😁3

11 Jul 2026, 16:09 UTC≈1,090 views1 reactionsread 12 August 2026
Photo

Агенты прокачивают друг друга UCSB-AI выложили GEA, фреймворк для open-ended self-improvement агентов через обмен опытом. Идея простая, но мощная: улучшать не одного агента в вакууме, а целую группу агентов как одну эволюционирующую систему. Один агент нашёл удачный паттерн, другой переиспользовал его, третий доработал, группа стала сильнее. Это похоже на переход от «один AI сам себя улучшает» к «популяция агенто

1

4 Jul 2026, 14:04 UTC≈1,070 views11 reactionsread 12 August 2026
Photo

✔️ Data-Juicer: пайплайн для подготовки данных под foundation models Alibaba и сообщество Data-Juicer развивают open-source систему для обработки датасетов перед обучением, дообучением и RAG. Data-Juicer помогает чистить, фильтровать, дедуплицировать, синтезировать и анализировать данные. Работает не только с текстом, но и с мультимодальными датасетами: изображениями, аудио и видео. В версии 2.0 заявлено больше 100

5👍3🔥2🤡1

24 Jun 2026, 00:29 UTC≈1,360 views21 reactionsread 12 August 2026
Photo

Мы вообще понимаем, насколько Месси статистически ненормален? Я наткнулся на цифру: по голам + ассистам за 90 минут он почти на 6 стандартных отклонений выше среднего нападающего из топ-лиг. Для контекста: это уже не «очень сильный игрок». Это уровень, который статистика почти не ожидает увидеть при жизни одного поколения. Вот почему спор про Месси часто ломается: его сравнивают как футболиста, а он по цифрам бли

13🤔4👎1💩1🔥1🤮1

16 Jun 2026, 22:59 UTC≈1,040 views3 reactionsread 12 August 2026
Photo

⚡️ Ling & Ring 2.6: новый техрепорт и open-weight модели Ant Ling выпустили технический отчёт по Ling & Ring 2.6 и открыли два base checkpoint. Главное: * 7:1 Hybrid Linear Attention: 7 Lightning Attention слоёв + 1 MLA слой, чтобы сделать 256K context практичнее * KPop RL: адаптивный Binary KL вместо uniform KL, прирост SWE-bench Verified с 70.8% до 76.28% * ~4× token efficiency: больше “интеллекта” на меньшее чи

👍21

13 Jun 2026, 16:03 UTC973 views4 reactionsread 12 August 2026
Photo

The Information: по сообщениям, Anthropic переходит от аренды облачных вычислений к аренде и самостоятельному управлению дата-центрами. Планируемая мощность в США — более 1 ГВт, а Google потенциально может выступить гарантом или поддержкой по арендным платежам. Старая модель была простой: Anthropic платит облачным провайдерам за GPU или кастомные чипы, но сам провайдер контролирует здание, электропитание, сеть, охла

4

13 Jun 2026, 13:49 UTC835 views5 reactionsread 12 August 2026
Photo

🖥 Python в 2026 - уже не просто «первый язык программирования». Это инструмент, с которым можно автоматизировать задачи, писать скрипты, собирать проекты, работать с данными, делать ботов и использовать ИИ как ускоритель разработки. Но есть проблема: большинство новичков учат Python кусками. Немного синтаксиса, пару задачек, немного теории - и потом ступор: «а что с этим делать дальше?» Этот курс сделан иначе. Зде

👍32

16 May 2026, 09:16 UTC≈1,230 views4 reactionsread 12 August 2026
Forwarded from @rust_codePhoto

👣 Я заставил LLM писать Rust полгода. Вот что они стабильно ломают Полгода я использовал Claude, GPT и Cursor как основной инструмент для написания Rust-кода в проде. Не как «помощник для бойлерплейта», а как полноценного второго разработчика на монолите примерно в 80 тысяч строк (бэкенд обработки потоковых данных, tokio, sqlx, немного unsafe в hot path). Доля сгенерированного кода в коммитах последних шести месяце

4

10 May 2026, 13:59 UTC≈1,370 views13 reactionsread 12 August 2026
Video

20-летний парень заработал $37 250 за месяц на YouTube-контенте, почти не открывая монтажку. Он собрал автономную «фабрику контента», где Claude работает мозгом, а Premiere Pro - руками. Система крутится 24/7, пока он просто живёт своей жизнью. Claude анализирует ниши с высоким CPM, пишет сценарии и через Python-скрипты запускает озвучку и генерацию видео. Уже в первый месяц один из его каналов набрал сотни тысяч п

4👍4👎2🔥2🤔1

30 Apr 2026, 10:03 UTC≈1,130 views5 reactionsread 12 August 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ Mistral выпустила Medium 3.5 и Remote Agents в среде Vibe Medium 3.5 - модель на 128 млрд параметров с контекстным окном 256K токенов. Веса опубликованы на Hugging Face под модифицированной лицензией MIT. Цена API - $1,50/$7,50 за млн. входящих/сгенерированных токенов. Уровень рассуждений настраивается под каждый промпт. На SWE-Bench Verified модель набрала 77,6%, опередив Claude Sonnet 4.5. Вместе с моделью Mi

2👍2🔥1

Showing the 12 most recent of 21 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.

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

“Аналитик данных” (@dataanlitics), 6,198 subscribers as measured 10 August 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.