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Channel

Жёлтый AI

@zheltyi_ai

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

9,383subscribers

+44 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-1001373379552
TypeChannel
Username@zheltyi_ai
DescriptionПодпольный филиал https://t.me/kod_zheltyi AI/ML-related news by T-Bank AI teams Чат: https://t.me/zheltyi_aimeetup
CreatedBetween 1 April 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live16 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 16 August 2026
On Telegramt.me/zheltyi_ai

Growth

9,3399,3839,3616 August 2026 — 9,339 subscribers6 August 2026 — 9,340 subscribers9 August 2026 — 9,346 subscribers12 August 2026 — 9,367 subscribers16 August 2026 — 9,383 subscribers6 August 202616 August 2026
5 measurements spanning 10 days, net +44. 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 9,332–9,390 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
16 Aug 2026, 08:379,383+16
12 Aug 2026, 12:449,367+21
9 Aug 2026, 15:129,346+6
6 Aug 2026, 19:009,340+1
6 Aug 2026, 11:229,339first reading

Engagement

16 posts held, back to 12 November 2025the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 16 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
31.4%
avg views ÷ 9,383 subscribers
Avg views / post
2,950
1 post measured
Reaction rate
2.07%
reactions ÷ views · ER floor
Posts in window
1
of 16 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 21 July 2026
Posts held16 (12 November 202521 July 2026)
Views total2,950
Reactions total61
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken18 Aug 2026, 02:52 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
287
Videos
18
Links
254

Lifetime counters from Telegram’s own channel header, read 18 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
4s
Average length
4s

Measured directly from 1 video 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

760 reactions across 16 posts, in 19 distinct kinds. The most used accounts for 34.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥26534.9%
12216.1%
👍668.68%
❤‍🔥628.16%
🥰536.97%
445.79%
🤣385.00%
🥴283.68%
🏆192.50%
😍172.24%
🎉162.11%
😁91.18%
🐳60.789%
🙏40.526%
🤯40.526%
👏30.395%
👌20.263%
🌚10.132%
💯10.132%

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 16 of the 16 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 760reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 16 most recent posts we hold, published 12 November 2025 to 21 July 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

21 Jul 2026, 12:10 UTC≈2,950 views61 reactionsread 18 August 2026
Photo

⚡️Делимся двумя релизами, о которых рассказывали на Turbo ML Conf T-Search — открытый агент-ретривер для сложного многошагового поиска. Это специализированная модель на основе Qwen3-35B-A3B. Она не генерирует ответ, а за несколько шагов находит нужные фрагменты в документах. Попробовать модель в деле можно на Hugging Face, а почитать, как мы обучали агента многошаговому поиску — в статье на Хабре. Perseus — открыты

🔥33👍14121❤‍🔥1

2 Jul 2026, 13:48 UTC≈4,550 views4 reactionsread 18 August 2026
Sticker

Sticker, posted without a caption

🐳4

2 Jul 2026, 13:47 UTC≈4,610 views33 reactionsread 18 August 2026
Photo

✨ Пока ждем Turbo ML Conf предлагаем поиграть в мини-игру @LossMonkeyBot Всем же приятно смотреть как лосс падает? Пока fable делает вашу работу, можно вкатиться в макакинг 🦍 3 человека, которые попадут в начало лидерборда, заберут лего-банкоматы Т-Банк. Победителей объявим в комментариях 17 июля - за день до конференции!

🔥14🥴1152👌1

22 Jun 2026, 09:20 UTC≈5,240 views25 reactionsread 18 August 2026
Photo

Владивосток, мы знаем, чем вы займетесь 27 июня 👀 Встречаемся на T-Meetup: R&D! Нас ждут доклады, полезные знакомства и общение с топами индустрии. В программе 3 доклада: → «Почему существуют задачи, которые нельзя решить обычной разработкой?» — Станислав Моисеев Поговорим, где проходит граница между разработкой и R&D, почему некоторые технологические вызовы невозможно решить стандартными инженерными подходами и з

👍12🔥9🏆4

16 Jun 2026, 12:28 UTC≈5,080 views104 reactionsread 18 August 2026
Photo

Получили ранний доступ к секретной модели Mistral: Le Gros Chaton — 30 трлн параметров, 256 тысяч экспертов. Подняли инференс на всём кластере, неделя прогрева — пока что сгенерировали только 10 токенов. Но это лучшие 10 токенов в нашей жизни. Правда, они пока только на французском. Так что начинаем обучение «Большой ко-Т».

🥰48🤣35🥴14🤯4🏆1👌1👍1

10 Jun 2026, 11:03 UTC≈6,960 views38 reactionsread 18 August 2026
Photo

⚡️ Открываем регистрацию на Turbo ML Conf 2026! Соберемся, чтобы обсудить глубокие исследования, прикладной ML и инженерные системы. В программе три трека. ✨ Fundamental Advances & Exploratory R&D Поговорим об архитектуре и обучении современных моделей, их интерпретируемости, безопасном поведении и способности к рассуждению и самокоррекции. ✨ Applied ML at Scale & Business Impact Рассмотрим внедрение ML в продукт

🔥16155😁2

2 Jun 2026, 12:03 UTC≈5,870 views68 reactionsread 18 August 2026
Photo

Съездили в 🔤🔤🔤🔤🔤🔤 на AAMAS 2026, где у нас был oral с работой Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success. В статье мы обучаем VLM-агентов через RL в дешевых синтетических средах: MiniWorld, Gym-Cards, ALFWorld и WebShop. Основная идея — если хотим, чтобы модель не просто красиво описывала картинку, а умела смотреть на состояние мира и делать послед

27🔥2112🤣3😁3🥴2

21 May 2026, 11:00 UTC≈5,760 views36 reactionsread 18 August 2026
Photo

⚡️ Turbo ML Conf возвращается! Бронируем ваше 18 июля, чтобы обсудить тренды, кейсы и технологии в ML. В этом году помимо докладов у нас будут представлены демозоны с разными ML-решениями. Вы сможете представить продукт или платформу вашей компании, основанную на ML-технологиях. Если вам есть что показать — оставляйте заявку на сайте. Мы особенно заинтересованы в опыте использования CV, RecSys и NLP. Всем участник

👍14125❤‍🔥4🐳1

7 Apr 2026, 13:02 UTC≈7,370 views19 reactionsread 18 August 2026
Photo

Как эффективно объединить машинное обучение, разработку и эксплуатацию в устойчивую и масштабируемую систему? Обсудим на T-Meetup: MLOps в Нижнем Новгороде уже 9 апреля! Что будет в программе? → «Когда Kubernetes не справляется: как мы научили кластер жить под сильной батчевой нагрузкой» — Андрей Фунтиков Узнаем, как инфраструктурная команда ML Core прошла путь от регулярных падений под нагрузкой до уверенной рабо

14🔥5

15 Mar 2026, 15:15 UTC≈7,620 views19 reactionsread 18 August 2026

Приглашаем всех на хакатон BitGN PAC1 🚀 11 апреля в офисе на Свердловской набережной, 44с2, пройдет финальный день международного соревнования по созданию персональных AI-агентов. Что это такое? BitGN PAC — это соревнование, где участники создают свои собственные AI-агенты, которые будут решать различные задачи в симулированной среде. Вам предстоит написать ядро агента, подключить его к платформе BitGN через API и

9🔥6😁4

21 Feb 2026, 13:27 UTC≈8,120 views59 reactionsread 18 August 2026
Photo

Во-первых поздравляем всех с праздником масленицы! Во-вторых мы выпустили блогпост про геометрию многообразий внутри LLM: внутри красивые картинки, интересные фичи и интерактивные графики. Рекомендуем темп примерно один блин на главу, приятного аппетита!

❤‍🔥32👍16🔥11

6 Feb 2026, 11:18 UTC≈9,980 views50 reactionsread 18 August 2026

В следующий вторник (10 февраля) в 16:30 Никита @CapturedGenie из команды фундаментальных моделей расскажет про Engram от DeepSeek на Yandex Research Reading Group. Никита разберет недавнюю статью от DeepSeek о модификации Transformer архитектуры - обсудит добавление специального Engram модуля внутрь блоков для явного ретривала знаний и покажет, как такая архитектура достигает лучших результатов при сравнимом бюджет

🔥3012❤‍🔥5🐳1👍1🥴1

Showing the 12 most recent of 16 posts we hold for @zheltyi_ai. 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 — 185,939 of 1,548,671entries 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

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 1 registered channel — 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.

Т-Образование
@tbank_education · 111,513
Telegram ranks this channel #17 of 91 here — alongside 90 others — read 14 August 2026
Machinelearning
@ai_machinelearning_big_data · 285,314
Telegram ranks this channel #21 of 95 here — alongside 94 others — read 10 August 2026
эйай ньюз
@ai_newz · 96,072
Telegram ranks this channel #26 of 94 here — alongside 93 others — read 16 August 2026
Data Secrets
@data_secrets · 92,194
Telegram ranks this channel #32 of 98 here — alongside 97 others — read 17 August 2026
Young&&Yandex
@Young_and_Yandex · 110,454
Telegram ranks this channel #85 of 92 here — alongside 91 others — read 14 August 2026

This channel appears in 5 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 16 August 2026 — this entry's latest reading, not the date you are reading this.

“Жёлтый AI” (@zheltyi_ai), 9,383 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/zheltyi_ai.

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.