Telegram RegisterThe public register of Telegram

Channel

Уголок zanudamode

@zanudamode

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

477subscribers

+0 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001935573787
TypeChannel
Username@zanudamode
CreatedBetween 1 March 2023 and 31 October 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live7 August 2026
Measurements held2
Confirmed unchanged1 time, most recently 7 August 2026
On Telegramt.me/zanudamode

Growth

4777 Aug 2026, 05:02 — 477 subscribers7 Aug 2026, 07:57 — 477 subscribers7 Aug 2026, 05:027 Aug 2026, 07:57
2 measurements taken within a single day. 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 476–478 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
7 Aug 2026, 07:57477no change
7 Aug 2026, 05:02477first reading

Engagement

10 posts held, back to 4 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
18.9%
avg views ÷ 477 subscribers
Avg views / post
90.3
10 posts measured
Reaction rate
3.98%
reactions ÷ views · ER floor
Posts in window
10
of 10 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 6 of 10 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 6 August 2026
Posts held10 (4 August 20266 August 2026)
Views total903
Reactions total25
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 05:02 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.

Reaction mix

25 reactions across 6 posts, in 7 distinct kinds. The most used accounts for 32.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥832.0%
👍728.0%
520.0%
28.00%
❤‍🔥14.00%
👾14.00%
🤝14.00%

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 10 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 25reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 10 most recent posts we hold, published 4 August 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.

Recent posts

6 Aug 2026, 16:34 UTC57 views1 reactionsread 7 August 2026
Photo

Сегодня хочу рассказать вечную сказку про то, как вся микроэлектроника мира держится на любителях пива из двух немецких деревушек — Оберкохена и Вецлара, в каждой из которых живут не более 10000 человек. На самом деле они ничем не примечательны, кроме одного: там находится единственное в мире место, где умеют изготовить оптику литографического качества для EUV и High-NA EUV. И вот Zeiss SMT наконец-то открыл там пер

🤝1

Signed Aleksandr Grishkanich

6 Aug 2026, 09:48 UTC59 viewsread 7 August 2026
Forwarded from @oulenspiegel_channel

овать модель здесь: https://huggingface.co/spaces/kulibinai/cadena-stepwise-cad

Signed Aleksandr Grishkanich

6 Aug 2026, 09:48 UTC62 viewsread 7 August 2026
Forwarded from @kulibinaiVideo

А ещё мы сняли ролик для популяризации CADENA

Signed Aleksandr Grishkanich

6 Aug 2026, 09:21 UTC67 viewsread 7 August 2026
Forwarded from @Ivan_OseledetsVideo

Команда Антона Конушина и лаборатория Fusion Brain вместе с ребятами Сергея Маркова и Игоря Пасечника из SberAI опубликовали крутое обновление в нашей линейке моделей для реверс-инжиниринга! CADENA: Stepwise CAD Reverse Engineering Перешли на пошаговое предсказание — модель стала гибче, качество выросло, и чем сложнее датасет, тем больше отрыв. Плюс собрали CADENA-Bench: 3396 реальных механических деталей, разбиты

Signed Aleksandr Grishkanich

6 Aug 2026, 07:10 UTC85 views2 reactionsread 7 August 2026
Photo

Очень хорошая иллюстрация того, как Google строит новую финансовую экосистему. Проекты ИИ настолько тяжеловесны, что без сложной обвязки в виде финансово-инвестиционного конгломерата ни одна даже самая большая корпорация в мире такое не потянет. По большому счёту, это ответ на вопрос, как продать электроэнергию с наценкой в сто тысяч процентов — продавая с её помощью знания. И Google, и Anthropic как его дочка, это п

2

Signed Aleksandr Grishkanich

5 Aug 2026, 15:34 UTC87 viewsread 7 August 2026
Forwarded from @aisecuritylab

Одна из участниц команды лаборатории — Анна Решетняк — дебютировала со статьей на Хабр! 🔥 В материале Аня рассказывает об опыте ускорения Guard модели на основе NER‑классификатора. Модель была нетривиальной, отсюда её ускорение не влезало в классические пути. В статье протестировано 5 инструментов: TensorRT, NVIDIA Triton, vLLM, Ray Serve и отдельно — смена бэкбона на Flash DeBERTa. Такой зоопарк ценен сам по себе,

Signed Aleksandr Grishkanich

5 Aug 2026, 07:10 UTC165 views5 reactionsread 7 August 2026
Photo

Вышел новый OWASP Top 10 для LLM — версия 2026. Мир уже не тот, что два года назад, главная тема теперь — агенты и деньги, и как все это безопасно внедрить. Посмотреть гайд OWASP тут: Top 10 for LLM Applications 2026 1. Prompt Injection 2. Sensitive Information Disclosure 3. Excessive Agency 4. Supply Chain 5. Data and Model Poisoning 6. Unbounded Consumption 7. Misinformation 8. Hidden Context Exposure 9. Vector

👍5

Signed Aleksandr Grishkanich

5 Aug 2026, 04:34 UTC105 views3 reactionsread 7 August 2026
Photo

Смотришь на механики удержания в светлых приложениях любимых бигтехов — бесконечные ленты, пуши с отсчётом, лайки-подколы — и понимаешь: лудка с беттингом на фоне них выглядит как честный мимишный бизнес. Р. С. Темные паттерны и умные алгоритмы нас погубят. Эхх. Зато будем счастливыми).

🔥3

Signed Aleksandr Grishkanich

4 Aug 2026, 11:22 UTC111 views4 reactionsread 7 August 2026
File

Приятно видеть, что в Петербурге столько ребят делают роботов и автоматизацию. С многими из них довелось поработать — и они реально доказали, что могут поставлять уникальные решения, которые работают и не разваливаются через месяц. Когда в одном месте собирается такая концентрация мозгов, как в городе-миллионнике, качество растёт само по себе. Инженеры рождаются не в поле, а там, где есть среда. Наша задача — не меш

2👍1👾1

Signed Aleksandr Grishkanich

4 Aug 2026, 06:55 UTC105 views10 reactionsread 7 August 2026
Photo

Удалось мне на днях побывать на том самом знаменитом фестивале огурца в Суздале. И знаете, это не просто ярмарка солений под зелёным куполом храма. Это целый мир, где деревянное зодчество соседствует с огуречными коронами, где бабушки в платочках торгуют вареньем из огурцов, а дети бегают с солёными огурчиками вместо мороженого. Атмосфера абсурда и уюта одновременно. И в какой-то момент я понял: за этим безумием стои

🔥53❤‍🔥1👍1

Signed Aleksandr Grishkanich

Showing the 10 most recent of 10 posts we hold for @zanudamode. 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 — 672,634 of 1,169,250entries 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

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.

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 7 August 2026 — this entry's latest reading, not the date you are reading this.

“Уголок zanudamode” (@zanudamode), 477 subscribers as measured 7 August 2026. Telegram Register, tgregister.com/channel/zanudamode.

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.