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
28 measurements spanning 36 days, net +214. 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 16,377–16,655 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 28
Measured (UTC)
Subscribers
Change
10 Sept 2026, 17:21
16,623
+18
7 Sept 2026, 13:17
16,605
+31
4 Sept 2026, 16:14
16,574
+13
3 Sept 2026, 01:29
16,561
+9
1 Sept 2026, 22:15
16,552
+5
31 Aug 2026, 21:26
16,547
+5
30 Aug 2026, 22:15
16,542
-4
29 Aug 2026, 20:24
16,546
+6
28 Aug 2026, 20:18
16,540
+10
27 Aug 2026, 23:34
16,530
+7
26 Aug 2026, 02:02
16,523
+18
25 Aug 2026, 03:47
16,505
+1
23 Aug 2026, 19:13
16,504
+15
22 Aug 2026, 03:28
16,489
+13
20 Aug 2026, 21:13
16,476
+8
19 Aug 2026, 22:02
16,468
+6
18 Aug 2026, 22:14
16,462
+5
17 Aug 2026, 20:16
16,457
+6
16 Aug 2026, 17:38
16,451
+12
15 Aug 2026, 00:54
16,439
first reading
Engagement
48 posts held, back to 10 July 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 56 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
29.0%
avg views ÷ 16,623 subscribers
Avg views / post
4,830
17 posts measured
Reaction rate
0.627%
reactions ÷ views · ER floor
Posts in window
23
of 48 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
Window
Rolling 30 days · latest post in window 2 September 2026
Posts held
48 (10 July 2026 – 2 September 2026)
Views total
82,090
Reactions total
515
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 09:49 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
1m 06s
Average length
22s
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
1,055 reactions across 34 posts, in 20 distinct kinds. The most used accounts for 26.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
275
26.1%
🔥
265
25.1%
😁
223
21.1%
👍
178
16.9%
🏆
16
1.52%
💅
16
1.52%
💯
16
1.52%
❤🔥
13
1.23%
🫡
9
0.853%
✍
8
0.758%
⚡
7
0.664%
🆒
7
0.664%
👏
6
0.569%
👌
5
0.474%
🤓
3
0.284%
😈
2
0.19%
🤗
2
0.19%
🦄
2
0.19%
🙏
1
0.095%
🥰
1
0.095%
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 35 of the 48 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 1,128 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 48 most recent posts we hold, published 10 July 2026 to 2 September 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
15
across the posts below
Posts paid on
7
of 35 we hold a reading for · 20%
Most on one post
5
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @dealerAI. 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 48 most recent posts we hold for this entry, published 10 July 2026 to 2 September 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.
Новые модальность LLM для квантовых вычислений и IoT революция с MCP.
Два шага к тотальной автоматизации. ИИ учится управлять и квантовым миром, и физическим. 😜
Сегодня случилось два события, которые лучше рассматривать вместе. Anthropic показала MHS - стандарт для подключения ИИ к любому оборудованию. А MIT-IBM выпустили работу, где LLM научились понимать квантовые операторы.
Кажется, ИИ-агенты наконец-то получаю…
Dealer.AI pinned «Метод отжига от зацикливания вашего агента 😗 Сижу я тут в одном чатике. И вижу сабж от @asboltenko: К слову хотел поделиться лайфхак, на случай если пропустили, если и когда агенты начинают тупить и топтаться на месте в решении какой-то задачи, им нужно сказать…»
Все компакты придуманы, все кодировки использованы. 🫤
Вот так бы можно было бы назвать видео беседы Гранта Сандерсона (создателя канала 3Blue1Brown) и Алока Пураника, исследователя из Jane Street. В нём они разбирают статью Алока о применении теории групп к позиционным кодировкам в трансформерах. 😐
Но не дай себя обмануть, тензорная алгебра и теория групп не так ужасна, тк ребята всегда славились наглядным разборо…
Dealer.AI pinned «Как тестировать AI агентов: от роутинга до траекторий и ответов. 🤖 Помню, как читал лекции по RAG и говорил, что агентные эвалы схожи, но более сложные из-за недетрменированности флоу. Теперь вышел достойный обзор, как можно это делать удобно. Ниже представлен…»
Неделя MCP от ВкусВилл.🤗
В эту неделю наше решение прям удостоилось широкого внимания, спасибо Вам за это. 🤗
Вот только несколько классных мнений и shorts. 😜 А их ещё больше!
На этой волне чуток поговорили с моим товарищем по ИИ цеху, и вот его взгляд на тенденции.
В общем, жду от Вас в комментариях, ваш опыт работы с нашим MCP и чтобы вы хотели ещё добавить?
👇👇👇
Метод отжига от зацикливания вашего агента 😗
Сижу я тут в одном чатике. И вижу сабж от @asboltenko:
К слову хотел поделиться лайфхак, на случай если пропустили, если и когда агенты начинают тупить и топтаться на месте в решении какой-то задачи, им нужно сказать "проведи исследование", "поищи информацию", тогда у них включается режим исследования и они выходят за рамки своих знаний и начинают искать и изучать информа…
Dealer.AI pinned «Скейлить веса недостаточно. Теперь не только слова от 📦 Scaling Law умер? Нет, он просто стал сложнее (с). Я очень много говорил о том, что недостаточно тупо скейлить веса. Также описывал возможные комбо текущих подходов, и тем самым, как делать прорывы…»
Как тестировать AI агентов: от роутинга до траекторий и ответов. 🤖
Помню, как читал лекции по RAG и говорил, что агентные эвалы схожи, но более сложные из-за недетрменированности флоу.
Теперь вышел достойный обзор, как можно это делать удобно. Ниже представлен разбор статьи инженера Postgres AI Hybrid Manager.
🔥 Проблема.
В RAG у вас ~четыре измерения: данные, кандидатогенератор, реранкер и ответ LLM. Но с агентами…
Недавно, я выступал на ML Urban от МТС, где рассказывал про агентную экономику, как будущее ИИ в ИТ ландшафте бизнеса: реклама, поиск, рекомендательные системы и покупки. Об этом многое есть в материалах: и в канале, и в подкастах. 😜
Материал с конфы, кстати, появился на Ict.moscow прям вместе с презой. 😎
Здорово, что коллеги по индустрии из MWS взяли следом на щит эту тему. Ведь, действительно, мы находимся в чист…
Dealer.AI pinned «Про культурный код моделей и соответствие каким-то ценностям LLM. Мне много стали присылать в лс такую новость. 😬 Тлдр. Модели AI будут проверять на соответствие культурным ценностям, для получения звания национальных или суверенных. 😐 Я очень много писал…»
Showing the 12 most recent of 48 posts we hold for @dealerAI. 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.
Posts edited after publishing
@dealerAI edited 4 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
13 August 2026
Most recent edit
29 August 2026
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 21 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.
LLM под капотом @llm_under_hood · 29,043 Telegram ranks this channel #5 of 96 here — alongside 95 others — read 9 September 2026
эйай ньюз @ai_newz · 96,790 Telegram ranks this channel #8 of 94 here — alongside 93 others — read 16 August 2026
Machinelearning @ai_machinelearning_big_data · 281,893 Telegram ranks this channel #9 of 95 here — alongside 94 others — read 10 August 2026
Dev & ML Connectable Jobs @dev_connectablejobs · 27,542 Telegram ranks this channel #10 of 94 here — alongside 93 others — read 10 September 2026
Сиолошная @seeallochnaya · 79,260 Telegram ranks this channel #10 of 97 here — alongside 96 others — read 19 August 2026
Sber AI @SberAIScience · 27,449 Telegram ranks this channel #11 of 95 here — alongside 94 others — read 10 September 2026
e/acc @cryptoEssay · 62,543 Telegram ranks this channel #11 of 96 here — alongside 95 others — read 21 August 2026
Data Secrets @data_secrets · 93,447 Telegram ranks this channel #11 of 98 here — alongside 97 others — read 17 August 2026
Время Валеры @cryptovalerii · 30,819 Telegram ranks this channel #12 of 94 here — alongside 93 others — read 6 September 2026
Machine learning Interview @machinelearning_interview · 30,274 Telegram ranks this channel #15 of 98 here — alongside 97 others — read 7 September 2026
XOR @xor_journal · 150,246 Telegram ranks this channel #16 of 93 here — alongside 92 others — read 12 August 2026
Denis Sexy IT 🤖 @denissexy · 136,970 Telegram ranks this channel #20 of 95 here — alongside 94 others — read 13 August 2026
Искусственный интеллект. Высокие технологии @vistehno · 71,699 Telegram ranks this channel #32 of 94 here — alongside 93 others — read 20 August 2026
Data Science @datascienceiot · 42,597 Telegram ranks this channel #38 of 76 here — alongside 75 others — read 29 August 2026
Анализ данных (Data analysis) @data_analysis_ml · 50,588 Telegram ranks this channel #44 of 93 here — alongside 92 others — read 25 August 2026
Метаверсище и ИИще @cgevent · 51,912 Telegram ranks this channel #45 of 92 here — alongside 91 others — read 25 August 2026
Поступашки - ШАД, Стажировки и Магистратура @postypashki_old · 44,959 Telegram ranks this channel #46 of 90 here — alongside 89 others — read 28 August 2026
Нейроскептик @neuroskep · 29,500 Telegram ranks this channel #47 of 90 here — alongside 89 others — read 8 September 2026
Neural Shit @NeuralShit · 53,230 Telegram ranks this channel #57 of 92 here — alongside 91 others — read 24 August 2026
Силиконовый Мешок @prompt_design · 85,163 Telegram ranks this channel #64 of 96 here — alongside 95 others — read 18 August 2026
Hacker News @hacker_news_feed · 29,586 Telegram ranks this channel #66 of 79 here — alongside 78 others — read 8 September 2026
Ai molodca @strangedalle · 46,881 Telegram ranks this channel #67 of 94 here — alongside 93 others — read 27 August 2026
kyrillic @kyrillic · 61,063 Telegram ranks this channel #80 of 96 here — alongside 95 others — read 22 August 2026
GigaChat @official_gigachat · 308,483 Telegram ranks this channel #83 of 92 here — alongside 91 others — read 10 August 2026
This channel appears in 24 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 10 September 2026 — this
entry's latest reading, not the date you are reading this.
“Dealer.AI” (@dealerAI), 16,623 subscribers as measured 10 September 2026. Telegram Register, tgregister.com/channel/dealerAI.
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.