Job listings — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 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.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (6 of the pairs behind the counts below)
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 9 of the 14 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.
“Published first” means first in this corpus. We hold 23 comparable posts for this entry, running 2 August 2026 to 7 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Across the whole group of 2, the earliest publisher we hold is @udafrii. That is a statement about our reading window, not a claim of authorship.
Views per post sit far above this size band
19,200 average views per post against 18,133 subscribers — an engagement rate of 106.0%. Across the 23,171 registered channels in the same cohort — 10,000–31,616 subscribers, posting mainly in Russian — the middle half sit between 5.51% and 23.5%, with a median of 11.7%.
What this was computed from
Window
30 days (13 July 2026 – 12 August 2026)
Posts measured
41 of 41 published in the window (1 exact, 40 rounded by Telegram)
Views totalled
788,373
Mature posts only
111.6% over 37 posts read at least 24h after publication
When this is recorded. A channel is listed here only when its engagement rate sits at or above the 99th percentile of its cohort and is at least 3× away from that cohort’s median — above it — on both the all-readings figure and the mature-only figure. The percentile alone would be circular: a percentile cut puts the same share of every cohort in the tail whatever the data looks like. The distance from the median is what makes it a statement about this channel.
This is not a verdict, and the direction is not a quality signal.A low rate has many innocent causes — audiences that read in the Telegram app without opening the channel, a subscriber base built long before the current output, an audience in a different timezone from our reading. A high rate has innocent causes too: a post that travelled far beyond the channel’s own subscribers will do it. We publish the measurement and the distribution it sits in. The full cohort baselines are downloadable, so this comparison can be reproduced rather than trusted.
Recorded under the keys clone_copy · err_high, last confirmed 12 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
6 measurements spanning 5 days, net -36. 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 18,128–18,174 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 10:47
18,133
-10
11 Aug 2026, 11:38
18,143
-8
10 Aug 2026, 11:15
18,151
-9
9 Aug 2026, 12:02
18,160
-3
8 Aug 2026, 09:15
18,163
-6
7 Aug 2026, 09:32
18,169
first reading
Engagement
44 posts held, back to 2 August 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 17 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
103.0%
avg views ÷ 18,133 subscribers
Avg views / post
18,700
44 posts measured
Reaction rate
0.712%
reactions ÷ views · ER floor
Posts in window
44
of 44 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 12 August 2026
Posts held
44 (2 August 2026 – 12 August 2026)
Views total
821,730
Reactions total
5,850
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
13 Aug 2026, 00:15 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
≈65
Videos
≈8
Links
≈2,180
Lifetime counters from Telegram’s own channel header, read 13 August 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.
Reaction mix
5,850 reactions across 44 posts, in 8 distinct kinds. The most used accounts for 23.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
😁
1,354
23.1%
❤
1,335
22.8%
👍
1,273
21.8%
🔥
1,260
21.5%
😢
230
3.93%
😱
211
3.61%
👎
186
3.18%
👌
1
0.017%
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 44 of the 44 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 5,850reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 44 most recent posts we hold, published 2 August 2026 to 12 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.
Advertising
Ad load
4.55%
2 of 44 posts carry an ad marker
Regulatory tokens
0
none — marked by hashtag only
Median views · ads
18,700
over 2 measured posts
Median views · rest
19,800
over 42 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.
Measured over the 44 most recent posts we hold, published 2 August 2026 to 12 August 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.
#вакансия #видеомонтаж #motion #креатор
🎬 Креативный видеомонтажёр в MotionPulse
Проект ищет эдитора с сильным творческим мышлением и хорошим чувством визуальных эффектов.
Задачи:
— придумывать идеи для эффектов;
— делать динамичные выжимки по 5–10 секунд из фильмов и клипов;
— создавать эффекты базовыми инструментами монтажной программы;
— записывать скринкасты процесса;
— писать пошаговые сценарии создания эффе…
#вакансия #дизайнер #communicationdesign #удаленно
🎨 Senior коммуникационный дизайнер в СберЗдоровье
Команда СберЗдоровья проводит ребрендинг и ищет сильного коммуникационного дизайнера.
Задачи:
— участвовать в развитии нового визуального языка;
— создавать Key Visual;
— работать с digital-коммуникациями;
— поддерживать высокий уровень графического крафта;
— использовать нейросети в различных дизайн-пайплайнах;
—…
#вакансия #ассистент #сценарист #research #удаленно
🔎 Помощник руководителя в отдел маркетинга
EdTech-проект и научная консалтинговая компания ищут помощника с сильными навыками интернет-ресерча.
Задачи:
— глубоко искать и анализировать информацию;
— погружаться в тематику проекта;
— писать около 10 сценариев в неделю;
— участвовать в двух рабочих созвонах;
— помогать руководителю с текущими задачами.
Что важно:…
#вакансия #reels #видеомонтаж #удаленно
🎬 Reels-мейкер / монтажёр в проект психолога
Ищут специалиста для постоянной работы с большим объёмом коротких видео.
Объём — от 60 Reels ежемесячно.
Задачи:
— монтировать трендовые ролики;
— находить подходящие фрагменты фильмов, интервью и вирусных видео;
— работать по готовым идеям и сценариям;
— добавлять субтитры и ключевые фразы;
— использовать музыку, звуки и просты…
#вакансия #видеомонтаж #reels #удаленно
🎞 Монтажёр Reels в агентство
Агентство ищет монтажёра на стабильный большой объём вертикального контента.
Задачи:
— монтировать Reels по готовым ТЗ и референсам;
— работать с динамикой и ритмом;
— делать чистые склейки;
— добавлять анимированные субтитры;
— соблюдать сроки;
— вносить правки.
Объём:
— 30–60 роликов в месяц;
— гарантируют не менее 25 роликов при стабильной р…
#вакансия #графическийдизайнер #реклама #удаленно
🎨 Рекламный дизайнер в OJ Group
Рекламное агентство ищет опытного дизайнера с сильным портфолио крупных кампаний.
Задачи:
— разрабатывать Key Visual;
— создавать digital-материалы;
— работать с наружной рекламой и POSM;
— делать презентации и адаптации;
— участвовать в креативных штурмах;
— работать с крупными локальными и международными брендами.
Что важно:
— от…
#вакансия #edtech #методист #ai
🧠 Цифровой методист / AI Learning в команду Андрея Комиссарова
Ищут специалиста на стыке образования, технологий и искусственного интеллекта.
Задачи:
— координировать производство цифрового образовательного контента;
— организовывать работу экспертов и разработчиков;
— искать и внедрять AI-инструменты;
— готовить и публиковать учебные материалы;
— контролировать их качество;
— разв…
Май: +178.000 рублей
Июнь: +207.000 рублей
Июль: +213.000 рублей
Согласитесь, звучит как сказка. Но примерно так выглядит средняя зарплата Авитолога.
Суть работы: размещать объявления на Авито по готовым шаблонам. На старте новички зарабатывают 60.000 рублей, а опытные ребята от 140.000 рублей в месяц.
Легко совмещается с работой, учебой или декретом, ведь делов на 3-4ч в день. Всему научим — от вас лишь потребует…
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⚖️ SMM-специалист в LexGroup
Юридическая команда LexGroup ищет SMM-специалиста для развития социальных сетей компании.
Площадки:
— ВКонтакте;
— Telegram;
— Instagram;
— MAX.
Основная задача — вести контент с учётом специфики юридической сферы.
Важно внимательно работать с:
— нормативно-правовыми актами;
— судебной практикой;
— документами и источниками, на которых основаны пуб…
#вакансия #таргетолог #туризм #реклама
🎯 Таргетолог в туристическую компанию
Туристическая компания ищет таргетолога для развития рекламного направления.
Ищут человека, который будет не просто технически запускать рекламу, но и участвовать в продвижении проекта.
Задачи:
— запускать и вести рекламные кампании;
— предлагать идеи по продвижению;
— создавать рекламные креативы;
— анализировать результаты и искать во…
#вакансия #smm #контентмейкер #fashion
👗 SMM-специалист / Content Creator в бренд женской одежды
Бренд женской одежды развивает собственные социальные сети и ищет специалиста, который сможет стать частью команды и лицом проекта.
Задачи:
— создавать контент для Instagram, VK, TikTok и YouTube Shorts;
— снимать примерки, обзоры, распаковки и lifestyle-видео;
— придумывать идеи для коротких роликов;
— монтировать ко…
#вакансия #видеомонтаж #reels #удаленно
🎬 Монтажёр вертикальных видео / Reels
Ищут монтажёра для долгосрочной работы сразу с несколькими проектами.
Задачи:
— собирать Reels из готовых исходников;
— делать динамичную, аккуратную нарезку;
— добавлять субтитры, зумы, перебивки и графику;
— чистить речь и работать со звуком;
— при необходимости подбирать дополнительные кадры и мемы;
— монтировать как экспертный, так …
👍41🔥38❤33😁23😢7👎4😱4
Showing the 12 most recent of 44 posts we hold for @vakansii_0. 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 — 956,082 of 1,151,006entries 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.
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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
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.
