Education — 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 11 August 2026 and assigned it the closest of 31 fixed categories, at 96% 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. 2 of the 5 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 20 comparable posts for this entry, running 2 July 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 @smmau — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, last confirmed 7 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.
4 measurements spanning 6 days, net -3. 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 6,332–6,335 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 19:03
6,332
-1
9 Aug 2026, 15:32
6,333
-2
7 Aug 2026, 01:56
6,335
no change
6 Aug 2026, 22:00
6,335
first reading
Engagement
26 posts held, back to 2 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 11 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
2.26%
avg views ÷ 6,332 subscribers
Avg views / post
143
13 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
13
of 26 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 11 August 2026
Posts held
26 (2 July 2026 – 11 August 2026)
Views total
1,860
Reactions total
—
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 03:16 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
14s
Average length
14s
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.
Какую воду нельзя пить — #лекция Редакция.Наука
Качественная вода – это профессионально очищенная, полезная для человека вода. Одно дело – разобраться с понятием, и совершенно другое – понять, какая именно вода будет считаться качественной. Какую воду пить безопасно в наше время? В этом ролике вы узнаете, какой должна быть вода — бутилированной, минеральной или достаточно просто кипяченой, что содержится в водопрово…
Столица духовности и самопознания — #лекция Dameoz
Тибет — это загадочное и труднодоступное место, которое манит к себе миллионы искателей Духовности со всего мира. Вы узнаете про многомуежство, распространённое там, про то, как Китай оккупировал Тибет и про то, кто такой Далай Лама а также многое другое.
Смотреть лекцию: youtube.com/watch?v=_eEGjXFrkeM
Что такое Frontend в веб-разработке — #лекция Loftblog
Традиционно веб разработку делят на 2 большие сферы — Frontend и Backend. Эти направления появились неслучайно, ведь сегодня веб уже далеко не такой простой, каким был раньше. Сейчас вас на сайтах встречают красивые анимации, реклама подобрана под ваши запросы, а что-то непонятное можно уточнить у онлайн консультантов. Все это потребовало разделить процесс разра…
Самые странные явления, происходящие в небе — #урок AdMe
Вы слышали о потрясающем зрелище над Гавайями? Недавно произошло великолепное явление, которое заставило небо выглядеть как копия Млечного Пути. Наблюдатели звёзд получили невероятное удовольствие, увидев звёзды и галактику так ясно, что казалось, будто можно было протянуть руку и коснуться их. Это было как зеркальное отражение Млечного Пути прямо над островам…
Как исправить осанку у взрослого - #урок Сергея Бубновского
В этом уроке доктор медицинских наук рассказывает, как исправить осанку у взрослого человека с помощью специальных упражнений для осанки.
Смотреть лекцию: https://youtu.be/_OfC6EZ4eoM
@lektoruim20
Разрушитель миров Роберт Опенгеймер — #лекция от Veritasium
Роберт Оппенгеймер, «отец атомной бомбы», трижды выдвигался на Нобелевскую премию, но так и не получил её. Почему? Из этой лекции вы узнаете о жизни и работе одного из самых талантливых и неоднозначных физиков в истории. Вы поймёте, каким человеком был Опенгеймер, почему он не любил экспериментальную физику, каким образом получил знания, которые привели его…
Пластичность мозга. Мы правда можем невозможное — #лекция Нормана Дойджа
Наш мозг действительно уникален. Норман Дойдж предлагает революционный взгляд на человеческий мозг. Он рассказывает о блестящих ученых, продвигающих пока еще новую науку о нейропластичности, и о поразительных успехах людей, жизнь которых они изменили, - примеры выздоровления пациентов, перенесших инсульт; случай женщины, имевшей от рождения пол…
Как прокачать иммунитет — #лекция Александра Панчина
В этом ролике подробно поговорят иммунитете. Как он устроен и можно ли его «прокачать». Обязательно разберут как популярные мифы о вакцинации, так и общий подход противников вакцин к «анализу» источников (так называемый «пабмеддинг»).
Смотреть лекцию: youtube.com/watch?v=5fGNgGC87sA
Как внедрить хорошие привычки — #лекция Руслана Полякова
Тема внедрения новых привычек всегда будет актуальна, потому что привычки — неотъемлемая часть нашей повседневной жизни. Именно поэтому так важно знать все аспекты правильного внедрения новых привычек в вашу жизнь. К ним относятся множество вещей. От поиска правильного стимула для эффективного старта, до осознания необходимости отказаться от самопринуждения.
…
Как решать тест – 5 правил и 1 секретное оружие - #лекция Томаса Франка
В этом видео Томас рассказывает про ряд полезных и обоснованных приёмов, которые помогут вам получить высокую оценку за следующий тест с вариантами ответа.
Смотреть лекцию: youtube.com/watch?v=XQC-Z0bUwqc
Секреты правильного использования парфюмерии — #урок от Елены Гуровой
В этом уроке вы узнаете, как, куда и сколько именно нужно наносить парфюмерии, чтобы парфюм раскрывался правильно и не раздражал окружающих людей чрезмерностью. Также вы узнаете, как именно наносить парфюм, чтобы он держался долго и сохранялся на протяжении всего дня, а значит — расходовался экономно.
Смотреть урок: https://youtu.be/friDADvXS7A
Showing the 12 most recent of 26 posts we hold for @smmau. 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 — 268,405 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 44 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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@smmau named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@lektoruim20 named in 1 post, 9 August 2026 – 9 August 2026
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.
“SMM | TikTok” (@smmau), 6,332 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/smmau.
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.
