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Channel

The Oleg 1337

@theoleg1337

On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry

590subscribers

-1 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-1002076209095
TypeChannel
Username@theoleg1337
CreatedBetween 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live15 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/theoleg1337

Growth

590591590.57 August 2026 — 591 subscribers8 August 2026 — 591 subscribers15 August 2026 — 590 subscribers7 August 202615 August 2026
3 measurements spanning 8 days, net -1. 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 590–591 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 06:08590-1
8 Aug 2026, 03:17591no change
7 Aug 2026, 16:53591first reading

Engagement

10 posts held, back to 29 April 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
63.1%
avg views ÷ 590 subscribers
Avg views / post
372
3 posts measured
Reaction rate
3.32%
reactions ÷ views · ER floor
Posts in window
3
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.

What these figures were computed from
WindowRolling 30 days · latest post in window 30 July 2026
Posts held10 (29 April 202630 July 2026)
Views total1,116
Reactions total37
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 03:17 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
21m 00s
Average length
21m 00s

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

246 reactions across 10 posts, in 9 distinct kinds. The most used accounts for 52.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥13052.8%
👍4618.7%
249.76%
👏156.10%
custom 5316784240996654202145.69%
custom 546521156335279250472.85%
custom 546312828946586752362.44%
custom 545869434132313605320.813%
🤔20.813%

Custom emoji. 4 of the rows above are Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them areTelegram’s.

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 10 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 246reactions 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 29 April 2026 to 30 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.

Telegram Stars

Stars received
9
across the posts below
Posts paid on
4
of 10 we hold a reading for · 40%
Most on one post
5
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @theoleg1337. 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 10 most recent posts we hold for this entry, published 29 April 2026 to 30 July 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.

Recent posts

30 Jul 2026, 06:14 UTC249 views14 reactionsread 8 August 2026

Ваш ИИ запомнил лишнее. Что делать компании? Можно ли удалить коммерческую тайну из уже обученной нейросети? На практике это одна из самых сложных задач современной ИИ-разработки. Почему модели плохо забывают знания и как бизнесу снизить этот риск — рассказал в материале РБК Про. [Ссылка] 🌐The Oleg

🔥6custom 54631282894658675236👍2

27 Jul 2026, 17:40 UTC319 views11 reactionsread 8 August 2026
Forwarded from @ved_vedomostiVideo message

Video message, posted without a caption

🔥82🤔1

27 Jul 2026, 12:33 UTC548 views12 reactionsread 8 August 2026

Безопасный ИИ. На Летней школе по искусственному интеллетку «Лето с AIRI» в рамках трека SafeAI одну из лекций читает Антон Дмитриевич Митрофанов, Исполнительный директор, Управление экспертизы кибербезопасности Сбера. Он расскажет про стратегию и подходы применения генеративного искусственного интеллекта в кибербезопасности. Антон Дмитриевич выступает сегодня в 16:45. Будет трансляция — можно посмотреть онлайн: [

👍7custom 53167842409966542023🔥2

4 Jun 2026, 06:41 UTC861 views47 reactions1 Starread 8 August 2026
Photo

ПМЭФ: Пределы использования ИИ в СМИ. В панельной дискуссии про ИИ и медиа обсудили, где сейчас находится граница между ИИ-контентом и журналистикой, и куда она движется. Фиксируется один и тот же сдвиг: ИИ уже не просто создаёт контент. Он конструирует среду, в которой контент формируется как естественный и правдоподобный. Технологии определяют, что аудитория сочтёт достоверным. А человек теперь не только автор ин

🔥328custom 54652115633527925047

29 May 2026, 14:34 UTC770 views31 reactionsread 8 August 2026
Photo

Визионерская сессия на Startup Village 2026. Компании всё чаще сталкиваются с кризисом долгосрочных ориентиров. Старые стратегии перестают работать, а новые слепые зоны появляются быстрее аналитических моделей. В BCG Gamma у нас был простой принцип: инструмент усиливает мышление, а не заменяет его. ✔️Основной вывод, который я предложил аудитории: ИИ становится полезным экзоскелетом только для тех, кто сначала научи

🔥19👏5👍4custom 545869434132313605321

23 May 2026, 15:04 UTC730 views17 reactionsread 8 August 2026
File

Игра в гифитацию. Сегодня на лекции обсуждали интересные вопросы по теме защиты агентных систем и refusal, и я решил запустить пробную задачу по стеганализу и криптоанализу "Cryptographic Steganalysis and the Geometry of Model Safety". ⚫️Выдаётся короткая гиф-анимация с лисом. На первый взгляд обычный файл, ничего примечательного. Но внутри записано сообщение, причём не в пикселях) Нужно оценить это поле блочным со

🔥10👏5custom 53167842409966542022

17 May 2026, 10:40 UTC712 views33 reactionsread 8 August 2026
Photo

Хороший, плохой, аблитерированный. Правда ли, что у open-weight LLM безопасность держится буквально на одном векторе? За пару лет безопасность чат-моделей переехала из жанра "уговори GPT" в строгий мехинтерп с градиентами и теоремами. Написал лонгрид после вчерашней лекции в ЦУ и вопросов. 1. GCG — с чего всё началось. Берёшь промпт, добавляешь к нему префикс, оптимизированный градиентом по дискретным токенам — и

👍24custom 53167842409966542026🔥3

1 May 2026, 14:33 UTC≈1,110 views45 reactions5 Starsread 8 August 2026
Video

СМИ будущего: редакция в эпоху искусственного интеллекта. Гость нового выпуска - Ольга Анисимова, исполнительный директор по внедрению технологий ИИ в редакционные процессы и руководитель дирекции производства видео медиагруппы «Россия сегодня». В разговоре — о том, как ИИ трансформирует работу современных редакций: какие процессы уже автоматизированы, как меняется роль журналиста в условиях растущего информационно

🔥35👏5custom 531678424099665420231🤔1

30 Apr 2026, 05:13 UTC655 views14 reactions2 Starsread 8 August 2026
Photo

Скальпель вместо молотка. С появлением больших языковых моделей возник новый класс угроз — атаки на выравнивание (alignment attacks). Выравнивание — процесс, с помощью которого модель учат следовать определенным правилам, например не генерировать запрещенный законодательством контент. Соответственно, такие атаки нацелены на то, чтобы заставить модель игнорировать эти ограничения. Сюда относятся джейлбрейки (jailbrea

🔥94👍1

29 Apr 2026, 16:35 UTC478 views22 reactions1 Starread 8 August 2026
Forwarded from @airi_research_institutePhoto

Разработали модель и набор данных для перевода надиктованных математических формул в текст Исследователи AIRI, Иннополиса, МТУСИ, НИУ ВШЭ и МГУ представили открытый датасет и модель искусственного интеллекта, благодаря которым можно преобразовать озвученные математические выражения и формулы в структурированный текст в формате LaTeX. В этой области долгое время отсутствовали качественные открытые данные для обучени

8👍8🔥6

Showing the 10 most recent of 10 posts we hold for @theoleg1337. 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.

Citation-graph rank

Citation-graph rank — 400,403 of 1,481,502entries 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.

Republishes

Channels on the register whose posts this channel has forwarded.

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.

Names

Channels on the register whose handles appear in this channel's posts.

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

“The Oleg 1337” (@theoleg1337), 590 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/theoleg1337.

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.