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 9 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
33 measurements spanning 42 days, net +174. 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 30,064–30,340 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
17 Sept 2026, 08:55
30,308
+16
15 Sept 2026, 05:17
30,292
+19
13 Sept 2026, 13:18
30,273
+6
11 Sept 2026, 14:57
30,267
-7
8 Sept 2026, 21:21
30,274
+10
5 Sept 2026, 15:42
30,264
+3
3 Sept 2026, 16:35
30,261
+15
2 Sept 2026, 08:58
30,246
+24
1 Sept 2026, 06:37
30,222
+20
31 Aug 2026, 03:43
30,202
+7
30 Aug 2026, 06:06
30,195
+1
29 Aug 2026, 03:28
30,194
-3
28 Aug 2026, 06:58
30,197
-7
27 Aug 2026, 10:34
30,204
+9
26 Aug 2026, 08:42
30,195
-5
25 Aug 2026, 06:27
30,200
+15
24 Aug 2026, 03:56
30,185
+33
22 Aug 2026, 08:15
30,152
+7
21 Aug 2026, 02:24
30,145
-1
20 Aug 2026, 00:12
30,146
first reading
Engagement
110 posts held, back to 26 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 77 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
9.54%
avg views ÷ 30,308 subscribers
Avg views / post
2,890
53 posts measured
Reaction rate
0.81%
reactions ÷ views · ER floor
Posts in window
53
of 110 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. It is computed over the 52 of 53 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 26 September 2026
Posts held
110 (26 July 2026 – 26 September 2026)
Views total
153,290
Reactions total
1,223
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
27 Sept 2026, 05:19 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
≈1,880
Videos
≈168
Links
≈1,310
Lifetime counters from Telegram’s own channel header, read 27 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
17m 18s
Average length
1m 20s
Measured directly from 13 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
2,899 reactions across 104 posts, in 44 distinct kinds. The most used accounts for 25.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
750
25.9%
❤
647
22.3%
🔥
586
20.2%
😁
409
14.1%
🤣
134
4.62%
💯
72
2.48%
🥰
44
1.52%
👏
36
1.24%
💊
33
1.14%
🥱
32
1.10%
🥴
26
0.897%
🤔
22
0.759%
😱
10
0.345%
🙈
9
0.31%
👀
8
0.276%
🌚
7
0.241%
😐
7
0.241%
🤪
6
0.207%
⚡
5
0.172%
🤩
5
0.172%
24 further kinds
51
1.76%
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 108 of the 110 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 2,968 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 110 most recent posts we hold, published 26 July 2026 to 26 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.
Advertising
Ad load
3.64%
4 of 110 posts carry an ad marker
Regulatory tokens
4
posts carrying an erid · 4 distinct tokens
Median views · ads
2,280
over 4 measured posts
Median views · rest
3,130
over 105 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
erid
Posts
First seen
Last seen
2VtzqxF9x3v
1
22 September 2026
22 September 2026
2W5zFGUKxVF
1
18 September 2026
18 September 2026
2W5zFKAbF53
1
22 September 2026
22 September 2026
CQH36pWzJqVJCbWwcsXL4MHgQs4xaEvn9ZDjZ1jckjmgft
1
2 September 2026
2 September 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 110 most recent posts we hold, published 26 July 2026 to 26 September 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.
🚀 LongCat-2.5-Preview: 1.6 трлн параметров и контекст на 1 млн токенов
Вышла LongCat-2.5-Preview - новая мультимодальная модель, заточенная под длинные агентные задачи.
Главное:
- 1.6T параметров
- около 48B активных параметров
- контекст до 1 млн токенов
- нативная мультимодальность
- фокус на long-horizon tasks
Модель рассчитана не только на чат, но и на работу через:
- терминал;
- браузер;
- GUI;
- таблицы;
-…
📋 Китай впервые обошёл США по числу ведущих ИИ-исследователей
По данным нового исследования Carnegie, в 2025 году 41% ведущих ИИ-специалистов работали в Китае, а 34% — в США.
Для сравнения, в 2022 году ситуация была обратной:
- США — 46%
- Китай — 27%
Ещё заметнее разрыв по происхождению специалистов: 57% ведущих ИИ-исследователей получили базовое образование в Китае, тогда как в США — 13%.
При этом 69% китайски…
Пользователь смотрит видео, листает клипы, ставит лайки — каждое действие становится сигналом для рекомендательной системы. Но важен не только отдельный сигнал, а его контекст и связь с другими событиями.
🎬 В новом ролике разбираем, как трансформер обрабатывает последовательность взаимодействий пользователя с контентом, определяет значимость событий и учитывает связи между видео, клипами и постами.
#AIVK #aivkhub #…
🛡️ Microsoft открыла skill для автоматического поиска рисков в AI-агентах
Новый run-assert-eval запускает полный цикл проверки агента из одного промпта в VS Code:
- Clarity ищет потенциальные failure modes;
- ASSERT превращает их в eval-тесты и измеряет нарушения;
- ACS генерирует runtime-политику для блокировки опасных действий;
- затем агент прогоняется на тех же тестах повторно, чтобы проверить, действительно ли…
✔️ OpenAI выпустила открытый бенчмарк для оценки ИИ в психологии
Набор Mental Health Bench проверяет, как модели ведут себя в диалогах от бытового стресса и проблем в отношениях до тяжёлого дистресса и экстренных кризисов. Сценарии охватывают взрослых, подростков, опекунов и врачей. Критерии написали более 80 психологов и психиатров из 22 стран.
Итоговый балл раскладывается на десять параметров поведения, среди них…
🤖 Microsoft открыла данные, на которых можно учить ИИ управлять роботом.
Датасет libero-data-for-rho содержит 282 тысячи записей из задач LIBERO: в каждой есть состояние робота, следующее действие, временная метка и номер задачи. Рядом Microsoft опубликовала 5-миллиардную модель `rho-libero` для роботизированных манипуляций.
Это материал для экспериментов с тем, как робот связывает наблюдение с движением. Лицензия …
Размер – не главное?
У небольшой AliceAI-Foundation-80B-A3B-Base от Яндекса уже появились первые надстройки-эксперименты от сообщества на Hugging Face. Компания называет новинку «экспериментальной основой для будущей единой рассуждающей модели», а в претрейне уже вшито рассуждение.
Решение обучено с нуля, без использования весов других моделей, и доступно под лицензией Apache 2.0.
Модель построена на архитектуре …
⚡️ inclusionAI открыла две модели для генерации и редактирования дизайна
Ming-Image-0.1-Design создаёт интерфейсы, дашборды, инфографику и постеры с разрешением до 2048×2048. Модель хорошо работает с текстом и умеет сразу генерировать изображения с прозрачным RGBA-фоном.
Ming-Image-0.1-Design-Layer решает обратную задачу: разбирает готовую картинку на отдельные редактируемые RGBA-слои, сохраняя исходные пропорции.
…
🎉 Маленькая LLM может тратить больше времени на загрузку, чем на ответ.
Google изучила запуск квантованных моделей от 270 млн до 3,8 млрд параметров на Cloud Run с CPU. При «холодном старте» 55–70% задержки приходилось на загрузку модели: прежде чем генерировать токены, нужно перенести её веса в память.
Нашёлся и менее очевидный эффект Cloud Run: переход с 4 на 8 ГиБ памяти в тестируемой конфигурации давал вдвое бо…
⚡️ Агенты научились улучшать собственную обвязку
RRSI оптимизирует не веса модели, а всю систему вокруг неё: промпты, инструменты, память, управление контекстом и логику выполнения задач.
Обычное самоулучшение быстро подгоняется под тесты. RRSI ограничивает число изменений, отсекает хаки под конкретный бенчмарк и удаляет дорогие или бесполезные компоненты.
Результат на 8 бенчмарках:
• до +14,1 пункта на обучающих…
Сразу три новых флагмана в гонке ИИ
За несколько недель рынок получил GPT-6 Astra, Grok 4.7 и Claude Opus 5.5.
• Grok 4.7 получил более крупную базовую модель, лучше работает с многочасовыми задачами и проверяет собственные результаты. Цена: $2 за вход и $6 за выход.
• Claude Opus 5.5 ориентирован на программирование и автономных агентов. По данным Anthropic, он на 30% быстрее Opus 5 и обходится на 40% дешевле в т…
😁14❤3🔥3👍2
Showing the 12 most recent of 110 posts we hold for @machinelearning_interview. 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 48 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 7 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
Data Science Jobs @datascienceml_jobs · 21,227 Telegram ranks this channel #3 of 97 here — alongside 96 others — read 24 September 2026
Анализ данных (Data analysis) @data_analysis_ml · 50,656 Telegram ranks this channel #3 of 93 here — alongside 92 others — read 25 August 2026
Python вопросы с собеседований @python_job_interview · 24,881 Telegram ranks this channel #4 of 90 here — alongside 89 others — read 15 September 2026
Data Science @datascienceiot · 42,634 Telegram ranks this channel #6 of 76 here — alongside 75 others — read 29 August 2026
Machinelearning @ai_machinelearning_big_data · 279,417 Telegram ranks this channel #6 of 95 here — alongside 94 others — read 10 August 2026
Data Science. SQL hub @sqlhub · 35,960 Telegram ranks this channel #7 of 87 here — alongside 86 others — read 2 September 2026
Искусственный интеллект. Высокие технологии @vistehno · 71,491 Telegram ranks this channel #9 of 94 here — alongside 93 others — read 20 August 2026
DevOps @DevOPSitsec · 23,684 Telegram ranks this channel #13 of 90 here — alongside 89 others — read 18 September 2026
Python/ django @pythonl · 58,866 Telegram ranks this channel #15 of 84 here — alongside 83 others — read 22 August 2026
Data Secrets @data_secrets · 94,138 Telegram ranks this channel #17 of 98 here — alongside 97 others — read 17 August 2026
Dev & ML Connectable Jobs @dev_connectablejobs · 27,959 Telegram ranks this channel #27 of 94 here — alongside 93 others — read 10 September 2026
XOR @xor_journal · 182,317 Telegram ranks this channel #27 of 93 here — alongside 92 others — read 12 August 2026
karpov.courses @KarpovCourses · 27,446 Telegram ranks this channel #31 of 96 here — alongside 95 others — read 10 September 2026
gonzo-обзоры ML статей @gonzo_ML · 24,323 Telegram ranks this channel #37 of 93 here — alongside 92 others — read 16 September 2026
Stepik – онлайн-курсы @stepik_courses · 24,454 Telegram ranks this channel #42 of 93 here — alongside 92 others — read 16 September 2026
Время Валеры @cryptovalerii · 30,808 Telegram ranks this channel #42 of 94 here — alongside 93 others — read 6 September 2026
LLM под капотом @llm_under_hood · 29,146 Telegram ranks this channel #44 of 96 here — alongside 95 others — read 9 September 2026
Job for Analysts & Data Scientists @foranalysts · 36,643 Telegram ranks this channel #44 of 96 here — alongside 95 others — read 1 September 2026
эйай ньюз @ai_newz · 96,889 Telegram ranks this channel #47 of 94 here — alongside 93 others — read 16 August 2026
Senior Python Developer @seniorpy · 39,864 Telegram ranks this channel #48 of 92 here — alongside 91 others — read 30 August 2026
Physics.Math.Code @physics_lib · 146,712 Telegram ranks this channel #48 of 72 here — alongside 71 others — read 13 August 2026
Linux Academy @linuxacademiya · 28,434 Telegram ranks this channel #51 of 84 here — alongside 83 others — read 9 September 2026
Поступашки - ШАД, Стажировки и Магистратура @postypashki_old · 45,531 Telegram ranks this channel #51 of 90 here — alongside 89 others — read 28 August 2026
Data Science Jobs @datasciencejobs · 22,137 Telegram ranks this channel #52 of 93 here — alongside 92 others — read 21 September 2026
This channel appears in 31 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. 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 17 September 2026 — this
entry's latest reading, not the date you are reading this.
“Machine learning Interview” (@machinelearning_interview), 30,308 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/machinelearning_interview.
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.