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Channel

Generative Ai

@deeplearning_ru

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

3,702subscribers

+37 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1001089529223
TypeChannel
Username@deeplearning_ru
Created16 August 2016 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live15 September 2026
Measurements held14
Confirmed unchanged1 time, most recently 15 September 2026
On Telegramt.me/deeplearning_ru

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 14 September 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 why it has no browse page of its own yet.

Growth

3,6653,7173,6916 August 2026 — 3,665 subscribers6 August 2026 — 3,665 subscribers9 August 2026 — 3,711 subscribers12 August 2026 — 3,717 subscribers15 August 2026 — 3,711 subscribers18 August 2026 — 3,708 subscribers21 August 2026 — 3,712 subscribers25 August 2026 — 3,710 subscribers28 August 2026 — 3,706 subscribers31 August 2026 — 3,711 subscribers3 September 2026 — 3,707 subscribers8 September 2026 — 3,705 subscribers12 September 2026 — 3,703 subscribers15 September 2026 — 3,702 subscribers3,7026 August 202615 September 2026
14 measurements spanning 41 days, net +37. 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 3,657–3,725 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Sept 2026, 16:193,702-1
12 Sept 2026, 10:013,703-2
8 Sept 2026, 17:363,705-2
3 Sept 2026, 17:013,707-4
31 Aug 2026, 07:563,711+5
28 Aug 2026, 10:043,706-4
25 Aug 2026, 01:383,710-2
21 Aug 2026, 10:463,712+4
18 Aug 2026, 10:223,708-3
15 Aug 2026, 19:563,711-6
12 Aug 2026, 12:583,717+6
9 Aug 2026, 05:023,711+46
6 Aug 2026, 04:333,665no change
6 Aug 2026, 00:393,665first reading

Engagement

19 posts held, back to 19 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 3 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 19 posts for this entry, the most recent from 3 August 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
4m 01s
Average length
48s

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

101 reactions across 14 posts, in 7 distinct kinds. The most used accounts for 42.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤4342.6%
🤯2221.8%
🔥1413.9%
👍1211.9%
😁76.93%
🤔21.98%
🥰10.99%

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 15 of the 19 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 101 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 19 March 2026 to 3 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.

Telegram Stars

Stars received
1
across the posts below
Posts paid on
1
of 19 we hold a reading for · 5%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @deeplearning_ru. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 19 most recent posts we hold for this entry, published 19 March 2026 to 3 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

3 Aug 2026, 09:01 UTC368 viewsread 7 August 2026

😈 Челлендж по запуску 12 простых IT-проектов за 12 месяцев Уже 3 года как ребята из комьюнити инди-хакеров запускают 1 простой продукт в онлайне каждый месяц. И в реальном времени показывать: как разрабатывают, продвигают и сколько получилось заработать на запусках таких микро-проектов. Например, вот 👉 Telegram-бот для ИИ-фотосессий, который заработал $280К за полгода. Он не пытается заменить большие ИИ-сервисы, а…

31 Jul 2026, 21:28 UTC489 viewsread 7 August 2026
Forwarded from @seeallochnaya

Вчера Anthropic поделились историей: после взлома HuggingFace одной из моделей OpenAI они начали масштабную ретроспективную проверку своих запусков Claude. В ходе проверки тестов обнаружили три инцидента, в которых модель Claude вышла в интернет, а затем получила несанкционированный доступ к реальным системам трех разных организаций. Во всех трех случаях перед Claude была поставлена задача в формате «Захват флага» —…

28 Jul 2026, 08:32 UTC673 viewsread 7 August 2026
Forwarded from @vibecoding_tgVideo

Moonshot, как и обещали, открыли веса Kimi K3 😏 Модель уже доступна на Hugging Face. Kimi K3 — их самая мощная модель: 2,8 трлн параметров в MoE-архитектуре, нативное понимание изображений и контекстное окно на 1 млн токенов. Причём открыли не только веса. Moonshot также выложили часть стека вокруг K3: высокопроизводительные attention-ядра, библиотеку коммуникации для MoE и инфраструктуру для масштабного запуска а…

12 Jul 2026, 13:30 UTC≈1,270 views5 reactionsread 7 August 2026
Forwarded from @vibecoding_tgPhoto

Anthropic описала четыре типа циклов, которые можно строить с помощью Claude. Каждый из них передаёт агенту разную часть процесса принятия решений. Термин loop engineering сейчас используется настолько широко, что стоит чётко понимать, чем именно отличаются эти четыре варианта: что запускает цикл и что определяет момент его завершения. > Цикл на основе запросов. Вы отправляете запрос, Claude выполняет задачу, самос…

❤2🔥2🥰1

28 Jun 2026, 21:19 UTC≈1,570 viewsread 7 August 2026
Forwarded from @vibecoding_tgPhoto

Нашёл отличную книгу — The Hitchhiker’s Guide to Agentic AI, которая охватывает практически весь стек Agentic AI. Главная ценность книги - это широкий обзор всего направления: архитектура LLM, обучение моделей, методы обучения с подкреплением, системы инференса, оценка моделей, агентные системы и многое другое. Лучше всего использовать её как карту знаний. Сначала просмотреть оглавление, найти темы, в которых есть п…

24 Jun 2026, 13:01 UTC≈1,510 views0 reactionsread 7 August 2026
Forwarded from @vibecoding_tgVideo

Знакомьтесь: Clips. Бесплатная открытая замена Loom, заточенная под агентов. 😋 В отличие от Loom, агент понимает Clips просто по ссылке. Каждый клип содержит API и метаданные, благодаря которым агент может изучить его содержимое. Агенты видят и слышат не только транскрипт, а вообще всё, что происходит на видео в любой момент времени. Делишься баг-репортом, фидбеком, анализом — и передаёшь это агенту, чтобы он улучш…

9 Jun 2026, 08:13 UTC≈1,710 views9 reactionsread 7 August 2026
Forwarded from @vibecoding_tgVideo

Вышел Harness-1 — поисковый агент на 20B параметров с довольно необычной идеей. Вместо того чтобы заставлять модель хранить всю историю поиска в контексте, авторы решили вынести состояние наружу и обучить модель работать через специальный harness. Получился агент на 20B параметров, который на длинных поисковых задачах конкурирует с гораздо более крупными моделями. Обычно поисковые агенты работают по схеме: поиск →…

❤7👍2

8 Jun 2026, 14:14 UTC≈1,180 views13 reactionsread 7 August 2026
Forwarded from @vibecoding_tgVideo

Если у вас есть видеокарта с 8 ГБ VRAM, то у меня для вас хорошие новости. Вчера чувак тестировал Unsloth Gemma 4 12B Q4_K_XL на карте с 8 ГБ VRAM. Народ был в шоке и сразу спросил: А 25B+ модель на бюджетной карте вообще реально запустить? Оказалось — да. Чувак запускает локально огромную MoE-модель на 26 миллиардов параметров на обычном ноутбуке с RTX 4060 8 ГБ и 16 ГБ оперативки. Что по скорости: - стабильные…

🤯6❤5😁2

8 Jun 2026, 06:58 UTC825 views6 reactionsread 7 August 2026
Forwarded from @vsevolodustinovchannel

Посмотрел выступление Anthropic про то, как они собирают агентов, которые могут работать часами. Схема такая: planner → agent → evaluator Это маленькая продуктовая команда из агентов. У каждого своя роль, свой контекст и своя зона ответственности. Но есть важные нюансы. 1. Planner: верхний план и спринты Planner получает короткий запрос и превращает его в структуру работы: — что собираем — какие большие части н…

🔥3❤2🤔1

28 May 2026, 18:42 UTC≈1,180 views6 reactionsread 7 August 2026
Forwarded from @vibecoding_tgPhoto

Антропики выкатили Claude Opus 4.8. Доступна уже сегодня по той же цене 🎉 Из самого интересного – заметно подтянули честность модели. По словам Anthropic, Opus 4.8 примерно в 4 раза реже пропускает баги в коде, который написал сам, и не пытается выдать сломанное решение за рабочее. Также в Claude Code появилась новая фича: dynamic workflows (research preview) – для самых сложных задач Claude строит план, запускает …

🤯4❤2

27 May 2026, 15:01 UTC≈1,080 views6 reactionsread 7 August 2026
Forwarded from @vibecoding_tgPhoto

Microsoft открыла исходный код Webright. Это скилл для ИИ-агентов, позволяющий управлять браузерами. Фишка в том, что внутри он использует Playwright, генерирует код на лету, а производительность просто бешеная. 🧑‍🍳

🤯4🔥2

25 May 2026, 09:42 UTC≈1,070 views9 reactions1 Starread 7 August 2026
Forwarded from @ai_machinelearning_big_dataPhoto

🔥 AlphaProof Nexus: формальные доказательства начинают превращаться в инженерный пайплайн Google DeepMind показали AlphaProof Nexus - систему, которая автономно закрыла 9 открытых задач Эрдёша, часть из которых висела десятилетиями. По оценке авторов, стоимость решения одной задачи составила всего несколько сотен долларов. Кроме этого, система доказала 44 открытые гипотезы из OEIS, закрыла 15-летний вопрос в алгебр…

❤4👍3🔥2

Showing the 12 most recent of 19 posts we hold for @deeplearning_ru. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

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 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.

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.

2026 PDF DEPOSU
@yks_kpss_pdf_kanalii · 27,454
Telegram ranks this channel #57 of 58 here — alongside 57 others — read 12 September 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list 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 15 September 2026 — this entry's latest reading, not the date you are reading this.

“Generative Ai” (@deeplearning_ru), 3,702 subscribers as measured 15 September 2026. Telegram Register, tgregister.com/channel/deeplearning_ru.

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.