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Channel

Artificial Intelligence

@ArtificialIntelligencedl

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

16,775subscribers

-8 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-1001197924174
TypeChannel
Username@ArtificialIntelligencedl
Created13 January 2019 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live26 September 2026
Measurements held32
Confirmed unchanged1 time, most recently 26 September 2026
On Telegramt.me/ArtificialIntelligencedl

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 10 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 why it has no browse page of its own yet.

Growth

16,75616,81016,7836 August 2026 — 16,783 subscribers7 August 2026 — 16,784 subscribers7 August 2026 — 16,786 subscribers10 August 2026 — 16,785 subscribers10 August 2026 — 16,788 subscribers11 August 2026 — 16,777 subscribers12 August 2026 — 16,775 subscribers14 August 2026 — 16,770 subscribers15 August 2026 — 16,758 subscribers17 August 2026 — 16,756 subscribers18 August 2026 — 16,759 subscribers19 August 2026 — 16,763 subscribers20 August 2026 — 16,770 subscribers21 August 2026 — 16,774 subscribers22 August 2026 — 16,784 subscribers24 August 2026 — 16,788 subscribers25 August 2026 — 16,784 subscribers27 August 2026 — 16,778 subscribers29 August 2026 — 16,777 subscribers30 August 2026 — 16,787 subscribers31 August 2026 — 16,799 subscribers1 September 2026 — 16,807 subscribers2 September 2026 — 16,808 subscribers3 September 2026 — 16,810 subscribers5 September 2026 — 16,808 subscribers9 September 2026 — 16,797 subscribers11 September 2026 — 16,794 subscribers13 September 2026 — 16,790 subscribers15 September 2026 — 16,795 subscribers16 September 2026 — 16,798 subscribers19 September 2026 — 16,794 subscribers26 September 2026 — 16,775 subscribers16,7756 August 202626 September 2026
32 measurements spanning 51 days, net -8. 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 16,748–16,818 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)SubscribersChange
26 Sept 2026, 08:1516,775-19
19 Sept 2026, 09:1616,794-4
16 Sept 2026, 21:0016,798+3
15 Sept 2026, 01:3616,795+5
13 Sept 2026, 12:0116,790-4
11 Sept 2026, 17:5516,794-3
9 Sept 2026, 01:1516,797-11
5 Sept 2026, 15:3816,808-2
3 Sept 2026, 18:3916,810+2
2 Sept 2026, 10:0616,808+1
1 Sept 2026, 10:4616,807+8
31 Aug 2026, 08:1616,799+12
30 Aug 2026, 05:2816,787+10
29 Aug 2026, 03:5316,777-1
27 Aug 2026, 00:2816,778-6
25 Aug 2026, 06:2716,784-4
24 Aug 2026, 05:5916,788+4
22 Aug 2026, 15:4216,784+10
21 Aug 2026, 09:4816,774+4
20 Aug 2026, 12:0216,770first reading

Engagement

24 posts held, back to 10 January 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 54 pages of Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 24 posts for this entry, the most recent from 20 August 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
11m 43s
Average length
3m 54s

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

129 reactions across 22 posts, in 7 distinct kinds. The most used accounts for 45.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤5845.0%
👍4837.2%
🔥1511.6%
👎43.10%
🤔21.55%
👏10.775%
🥰10.775%

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 23 of the 24 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 129 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 24 most recent posts we hold, published 10 January 2026 to 20 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
8.33%
2 of 24 posts carry an ad marker
Regulatory tokens
2
posts carrying an erid · 1 distinct token
Median views · ads
2,170
over 2 measured posts
Median views · rest
3,250
over 22 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
2W5zFHgXtWd215 July 202616 July 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 24 most recent posts we hold, published 10 January 2026 to 20 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

20 Aug 2026, 13:45 UTC≈1,080 views4 reactionsread 3 September 2026
Forwarded from @ai_machinelearning_big_dataVideo

✔️ 24 августа в ChatGPT появится реклама для европейских пользователей OpenAI начнет показывать рекламу пользователям из 31 европейской страны. Блоки будут только на тарифах Free и Go, платные подписки их не увидят. В США и еще восьми регионах рекламная платформа работает с февраля 2026 года. Деньги от рекламы, по словам компании, пойдут на поддержку бесплатного доступа к моделям. OpenAI обещает помечать объявления…

❤3👍1

20 Aug 2026, 11:45 UTC≈1,110 viewsread 3 September 2026

KDD 2026: что происходит с рекомендательными системами Рекомендательные системы постепенно становятся заметно сложнее обычного «предскажем следующий клик». На KDD 2026 AI VK Research представила две работы, которые хорошо это доказывают. Обе посвящены разным этапам recommendation pipeline, но объединяет их общий сдвиг: модель рассматривает рекомендации не как набор независимых предсказаний, а как последовательность…

20 Aug 2026, 11:00 UTC930 views3 reactionsread 3 September 2026
Video

Generalist AI представила GEN-1.5 - foundation model для роботов, которая умеет учиться новому физическому действию по одному короткому примеру, без дообучения. Модели показывают действие длиной всего 3–12 секунд, после чего робот сразу пытается выполнить его сам. В тестах на 10 разных задачах такой one-shot подход дал в среднем 59% успешных выполнений. Если дать модели 5 минут демонстраций и всего 10 шагов градиен…

❤2👍1

12 Aug 2026, 08:09 UTC≈1,790 views6 reactionsread 3 September 2026
Forwarded from @aihubvkPhoto

📢 Исследователи AI VK Research научили двухбашенный трансформер над историей оптимизировать удовлетворённость пользователя за всю сессию. Работа принята на воркшоп конференции KDD 2026, которая проходит сейчас в Южной Корее. Большинство современных рекомендательных моделей решает локальную задачу — предсказать следующее взаимодействие или следующий положительный фидбэк. Мы сформулировали задачу как RL на уровне сесс…

❤3👍2🔥1

10 Aug 2026, 19:10 UTC≈1,860 views4 reactionsread 3 September 2026
Photo

🚨 Цукерберг опубликовал большой текст о будущем ИИ - и его позиция довольно жёсткая: сверхинтеллект не должен принадлежать нескольким закрытым лабораториям. Главные тезисы: - задержка релиза американских моделей даже на месяц может ослабить лидерство США; - самоулучшающийся ИИ теоретически способен в разы повысить эффективность вычислений; - «единого доброжелательного сверхинтеллекта» не существует; - опаснее всего…

👍2🔥2

10 Aug 2026, 17:08 UTC≈1,590 views1 reactionsread 3 September 2026
Photo

История кликов, просмотров и действий пользователя сама по себе мало что даёт. Гораздо интереснее — собрать всё это в единое представление его интересов. Именно такой подход разобрали в VK: трансформерная модель строит нейропрофиль пользователя на сигналах из разных сервисов. В статье — токенизация событий, временные и контентные признаки, attention, двухбашенная архитектура и обучение на больших каталогах.

❤1

3 Aug 2026, 14:28 UTC≈2,150 views12 reactionsread 3 September 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…

❤7👍3🔥2

26 Jul 2026, 13:00 UTC≈3,190 views13 reactionsread 3 September 2026
Photo

Andrej Karpathy just dropped 12-page PDF on "Graph Engineering" for multi-agentic systems the shift: Karpathy's loop runs 700 experiments and forgets all of them. A graph remembers forever here's the full system: step 1 → build one loop: generate, critique, revise. 630 lines, 700 experiments in 48 hours step 2 → go parallel: agents in separate worktrees, same repo, different branches, no conflicts step 3 → add a…

❤9👍4

16 Jul 2026, 13:15 UTC≈3,490 views9 reactionsread 3 September 2026
Photo

Google Research попыталась объяснить, откуда у diffusion-моделей берётся «креативность». Почему генератор изображений не просто копирует обучающие примеры, а создаёт новые сцены, которых не было в датасете? Ответ оказался математическим. Во время обучения нейросеть не запоминает идеальную функцию удаления шума. Из-за регуляризации и особенностей градиентного обучения она усваивает её более сглаженную версию. Googl…

👍6👎2❤1

16 Jul 2026, 13:15 UTC≈2,520 views0 reactionsread 3 September 2026
Advertisementerid 2W5zFHgXtWd

Один из мифов вокруг ИИ-кодинга: если подобрать нужный промпт — модель сама напишет качественный код На деле это проверяется за минуту. Два разработчика с одной и той же просьбой «собери сервис для регистрации пользователей» получат разные результаты. У одного выйдет чистый Litestar с типизацией и тестами. У второго — заготовка, которая ляжет при первой же доработке. Дело не в тексте запроса. Модель не видит ваш п…

15 Jul 2026, 10:04 UTC≈2,170 views3 reactionsread 3 September 2026
Advertisementerid 2W5zFHgXtWd

Один из мифов вокруг ИИ-кодинга: если подобрать нужный промпт — модель сама напишет качественный код На деле это проверяется за минуту. Два разработчика с одной и той же просьбой «собери сервис для регистрации пользователей» получат разные результаты. У одного выйдет чистый Litestar с типизацией и тестами. У второго — заготовка, которая ляжет при первой же доработке. Дело не в тексте запроса. Модель не видит ваш п…

👎2❤1

13 Jul 2026, 17:19 UTC≈2,410 views2 reactionsread 3 September 2026
Photo

Most upvoted papers on huggingface this week (July 6-12): - The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning - Vidu S1: A Real-Time Interactive Video Generation Model - RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation - AlayaWorld: Long-Horizon and Playable Video World Generation - Accurate, Interdisciplinary and Transparent…

❤1👍1

Showing the 12 most recent of 24 posts we hold for @ArtificialIntelligencedl. 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

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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 2 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.

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.

Artificial Intelligence
@Artificial_intelligence_in · 65,766
Telegram ranks this channel #8 of 90 here — alongside 89 others — read 21 August 2026
Data Science
@datascienceiot · 42,634
Telegram ranks this channel #9 of 76 here — alongside 75 others — read 29 August 2026
Искусственный интеллект. Высокие технологии
@vistehno · 71,491
Telegram ranks this channel #17 of 94 here — alongside 93 others — read 20 August 2026
Artificial Intelligence && Deep Learning
@DeepLearning_ai · 57,432
Telegram ranks this channel #24 of 87 here — alongside 86 others — read 23 August 2026
Python/ django
@pythonl · 58,866
Telegram ranks this channel #25 of 84 here — alongside 83 others — read 22 August 2026
Анализ данных (Data analysis)
@data_analysis_ml · 50,656
Telegram ranks this channel #29 of 93 here — alongside 92 others — read 25 August 2026
Data Science. SQL hub
@sqlhub · 35,960
Telegram ranks this channel #30 of 87 here — alongside 86 others — read 2 September 2026
Python вопросы с собеседований
@python_job_interview · 24,881
Telegram ranks this channel #34 of 90 here — alongside 89 others — read 15 September 2026
Нейроскептик
@neuroskep · 29,484
Telegram ranks this channel #41 of 90 here — alongside 89 others — read 8 September 2026
Data Science Jobs
@datascienceml_jobs · 21,227
Telegram ranks this channel #46 of 97 here — alongside 96 others — read 24 September 2026
Machinelearning
@ai_machinelearning_big_data · 279,417
Telegram ranks this channel #46 of 95 here — alongside 94 others — read 10 August 2026
DevOps
@DevOPSitsec · 23,684
Telegram ranks this channel #49 of 90 here — alongside 89 others — read 18 September 2026
Coding Resources
@CodingResourcees · 43,841
Telegram ranks this channel #59 of 68 here — alongside 67 others — read 28 August 2026
CloudyML - Data Science & Analytics
@cloudymlofficial · 47,680
Telegram ranks this channel #72 of 88 here — alongside 87 others — read 26 August 2026
Computer Science and Programming
@computer_science_and_programming · 140,074
Telegram ranks this channel #72 of 87 here — alongside 86 others — read 13 August 2026

This channel appears in 15 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 26 September 2026 — this entry's latest reading, not the date you are reading this.

“Artificial Intelligence” (@ArtificialIntelligencedl), 16,775 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/ArtificialIntelligencedl.

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.