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 12 September 2026 and assigned it the closest of 31 fixed categories, at 82% 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
14 measurements spanning 43 days, net +9. 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 5,675–5,701 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
18 Sept 2026, 23:16
5,691
+13
14 Sept 2026, 18:38
5,678
-5
11 Sept 2026, 07:16
5,683
-6
6 Sept 2026, 04:57
5,689
+1
2 Sept 2026, 00:36
5,688
-3
30 Aug 2026, 06:57
5,691
-6
27 Aug 2026, 15:45
5,697
-1
24 Aug 2026, 08:54
5,698
+8
20 Aug 2026, 14:15
5,690
+2
17 Aug 2026, 16:36
5,688
+2
14 Aug 2026, 18:24
5,686
+2
11 Aug 2026, 01:42
5,684
+5
7 Aug 2026, 18:31
5,679
-3
7 Aug 2026, 07:02
5,682
first reading
Engagement
6 posts held, back to 21 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 8 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 6 posts for this entry, the most recent from 6 August 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
166 reactions across 6 posts, in 13 distinct kinds. The most used accounts for 23.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🤯
39
23.5%
❤🔥
36
21.7%
❤
30
18.1%
😭
16
9.64%
🙈
9
5.42%
🏆
8
4.82%
👏
8
4.82%
😎
7
4.22%
🔥
4
2.41%
🤓
3
1.81%
🤩
3
1.81%
👍
2
1.20%
🤣
1
0.602%
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 6 of the 6 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 166 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 6 most recent posts we hold, published 21 July 2026 to 6 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.
Нейросети, объединяйтесь 🧠
Вероятность дефолта клиента зависит от множества факторов, поэтому в кредитном скоринге недостаточно опираться на один источник данных.
У нас за прогноз отвечают сразу несколько нейросетевых модулей, они анализируют транзакции, кредитную историю и связи между клиентами.
Но как объединить их в одну модель, чтобы получить более точный результат? Об этом рассказал 👨💻 Леонид Кулыгин, специ…
Выпустили 49 новых AI-продактов 🎓
У нас прошёл выпускной второго потока Школы AI-продактов. В этом году программу завершили 49 сотрудников.
Роль AI-продакта появилась в банке в конце 2024 года. Это специалисты, которые помогают внедрять искусственный интеллект в бизнес-процессы. Подробно рассказывали про роль в посте.
Таких специалистов на рынке пока единицы, поэтому мы решили развивать их внутри банка.
Три месяц…
Правила приличия для ИИ 🤖
В декабре 2025 года Банк России опубликовал Кодекс этики в сфере разработки и применения ИИ на финансовом рынке. К нему присоединился Альфа-Банк и другие крупнейшие банки страны.
Кодекс этики — набор принципов, которые помогают выстраивать безопасное и прозрачное взаимодействие человека с ИИ.
В его основе — пять принципов:
🤡 Человекоцентричность — ИИ должен улучшать качество и безопасност…
Как Kaggle помогает прокачивать навыки в Data Science? 🏆
Для многих Kaggle — это соревнования по машинному обучению. Но для наших специалистов ещё и возможность изучать новые предметные области, тестировать подходы и решать нестандартные задачи.
Поговорили с Кириллом Кривошеевым, старшим специалистом по интеллектуальному анализу данных, о том, как он пришел в Kaggle и какие решения помогли занять призовые места и з…
Как рекомендовать задания миллионам пользователей? 🎮
В играх Альфа-Банка пользователи выполняют задания ради игровых бонусов, а для банка это реальные действия: оплата ЖКУ, бронирование отелей, заказ карты и другие продуктовые сценарии.
Когда таких заданий стало много, появилась задача рекомендовать каждому пользователю именно те, которые он с большей вероятностью выполнит. Казалось, что логичным решением станет од…
❤🔥12🤓2😎2
Showing the 6 most recent of 6 posts we hold for @aaanalytics. 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 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.
Alfa Digital @alfadigital_jobs · 54,435 Telegram ranks this channel #6 of 99 here — alongside 98 others — read 24 August 2026
Карьера в Альфа-Банке @alfabank_career · 25,047 Telegram ranks this channel #10 of 95 here — alongside 94 others — read 15 September 2026
Альфа-Будущее @alfafuture · 49,504 Telegram ranks this channel #17 of 99 here — alongside 98 others — read 26 August 2026
Немалый бизнес @aaaa_business · 560,149 Telegram ranks this channel #20 of 76 here — alongside 75 others — read 9 August 2026
Альфа-Курс @alfa_course · 20,674 Telegram ranks this channel #38 of 88 here — alongside 87 others — read 26 September 2026
karpov.courses @KarpovCourses · 27,446 Telegram ranks this channel #52 of 96 here — alongside 95 others — read 10 September 2026
Код Желтый @kod_zheltyi · 33,101 Telegram ranks this channel #60 of 96 here — alongside 95 others — read 4 September 2026
МТС Банк | Карьера @mtsbankcareer · 30,329 Telegram ranks this channel #61 of 99 here — alongside 98 others — read 7 September 2026
Ozon Tech @ozon_tech · 29,768 Telegram ranks this channel #69 of 98 here — alongside 97 others — read 8 September 2026
This channel appears in 9 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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“Alfa Advanced Analytics” (@aaanalytics), 5,691 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/aaanalytics.
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.
