Other / unclassifiable — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 46% 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 -255. 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 19,635–19,966 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, 12:19
19,673
-5
15 Sept 2026, 14:20
19,678
-11
14 Sept 2026, 00:20
19,689
-37
12 Sept 2026, 06:41
19,726
-6
10 Sept 2026, 02:58
19,732
-44
6 Sept 2026, 20:38
19,776
-15
4 Sept 2026, 09:17
19,791
-14
2 Sept 2026, 22:03
19,805
-3
1 Sept 2026, 17:25
19,808
-23
31 Aug 2026, 14:24
19,831
-11
30 Aug 2026, 14:15
19,842
-8
29 Aug 2026, 15:07
19,850
-6
28 Aug 2026, 11:42
19,856
+9
27 Aug 2026, 09:15
19,847
-13
26 Aug 2026, 05:57
19,860
-1
25 Aug 2026, 08:42
19,861
-6
24 Aug 2026, 10:39
19,867
+12
22 Aug 2026, 19:18
19,855
+41
21 Aug 2026, 10:03
19,814
-6
20 Aug 2026, 11:42
19,820
first reading
Engagement
111 posts held, back to 5 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 57 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
3.35%
avg views ÷ 19,673 subscribers
Avg views / post
659
40 posts measured
Reaction rate
2.06%
reactions ÷ views · ER floor
Posts in window
40
of 111 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 2 September 2026
Posts held
111 (5 August 2026 – 2 September 2026)
Views total
26,375
Reactions total
544
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 01:00 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
8m 25s
Average length
39s
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
1,350 reactions across 105 posts, in 4 distinct kinds. The most used accounts for 39.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
531
39.3%
💘
381
28.2%
🥰
275
20.4%
🤩
163
12.1%
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 111 of the 111 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 1,396 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 111 most recent posts we hold, published 5 August 2026 to 2 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.
Кажется, я уже знаю, где красиво провожу это лето 💌
6 сентября планирую отправиться на открытый фестиваль радио Monte Carlo в аутлете Новая Рига - обещают атмосферу европейского курорта, музыку, шопинг и красивую светскую тусовку под открытым небом.
На главной сцене будут Telliodora, Willie Key, SOCRAT и МАРФА, а на территории откроется отдельное pop-up пространство Monte Carlo с одеждой и товарами для дома.
На Це…
А я тем временем продолжаю ходить в гости к прекрасным девушкам 🤍
На этот раз делала разбор гардероба для руководителя отдела маркетинга в социальных сетях. Первое, что мне сказали, когда я зашла в гардеробную: «насколько все плохо…»
Но я всегда говорю: «плохого гардероба не бывает, есть лишь незнание, какие ещё сочетания можно придумать со своими вещами».
У нас вышло 35+ готовых образов под разные жизненные сцена…
Сегодня была в своем любимом оранжевом домике Shape it 🧡
У ребят сейчас проходит супер классная коллаборация с брендом косметики myTAUI. Лимфодренажный массаж делают на спа-масле meraki с ароматом миндаля, авокадо, лемонграсса, лаванды и других приятных компонентов.
Когда мастер только нанесла его на тело, я сразу поняла, что хочу этот тюбик забрать себе домой… не поверите, но это было услышано - после визита меня …
Выработала для себя идеальную программу на утро!
Подъем в 7:30, приготовление вкусных блюд, прогулка с Ники, завтрак и тренировка в 10 утра в любимом GRAND PLIÉ.
Сегодня посетила новое для себя направление Body sculpt х Дарья Жук. За основу взят пилатес, к которому добавляются упражнения для более глубокой проработки мышц. Сегодня был акцент на ягодицы и пресс. Из инвентаря были утяжелители и мячик.
Я люблю такой …
Хочу/могу: Zarina выпустила модель, похожую на легендарные сумки Bottega Veneta👀
Нравится все: базовый оттенок, комфортная ручка и вместительность!
340 000 рублей vs 3 999 рублей
Мой любимый французский бренд sezane поделился съемкой лукбука осенне/зимней коллекцией 💔
Обратите внимание на то, какой здесь простой и понятный для глаза стайлинг. Это тот самый случай, когда мне очень красиво.
Акцент на цвета и фактуры - один из самых простых приемов! А ещё его можно реализовать со своим гардеробом.
Если будете в Париже, то обязательно загляните к ним в гости. Я урвала у ребят тренч за 250€ 😍
«Это носи. Это не носи. Это выкинь. А это — уже не в тренде, вот буквально со вчера, как только ты это купила.»
🔶Если вы тоже устали от этой модной свистопляски, рекомендую почитать Нарядный полушубок: про моду, бьюти и вообще всё девчачье, но через призму иронии и здравого смысла.
🍀 Посты для ознакомления:
— еженедельная рубрика с находками до 1000 или чуть больше (тут анималистичные чехлы)
— суперактуальная обу…
Моя цель - создать гардероб, где все будет сочетаться между собой, чтобы вы не зависели от готовых образов на Pinterest, не повторяли чужой стиль и не покупали вещи только потому, что они красиво смотрятся на ком-то другом.
Вы начнете понимать, что подходит именно вам, какие вещи действительно нужны вашему гардеробу и как собирать десятки разных сочетаний.
Заполняй анкету, и я назначу время для звонка 📞
На моем личном менторстве вы:
💌 будете выполнять упражнения, которые помогут прокачать стиль с нуля
💌 получите готовые формулы образов для разных сценариев жизни
💌 поймете, какие вещи действительно нужны именно вашему гардеробу, а какие покупки можно смело пропустить
💌 научитесь понимать, почему один образ выглядит собранным, а другой - «чего-то не хватает»
💌 получите гардероб, который будет работать на вашу реальну…
Здесь не встает вопрос о модном гардеробе, я поднимаю тему того, что умею одеваться для себя и продумывать аутфит так, чтобы он украшал меня и раскрывал достоинства!
Можно знать все тренды сезона, скупать вещи горами, но все равно каждое утро стоять перед шкафом.
Гораздо важнее разбираться в моде для себя, чтобы понимать, как я могу улучшить свой образ и при этом не покупать новую вещь 💔
Наслаждаемся образами стильных mommies на первое сентября 💌
Мне особенно нравится, что здесь нет попытки выглядеть слишком нарядно. Каждый аутфит собран под реальную жизнь, но при этом с характером и своей модной изюминкой!
Ольга Масельскене в lesyanebo
Дарья Крыжановская в 12storeez
Ирина Логвинова в ateliermaru
Елена Вакуленко в moschino
🔥7🤩3💘1
Showing the 12 most recent of 111 posts we hold for @stylewithmary. 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.
Polls
The 3 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.
Сколько времени я потратила, чтобы составить 8 разных образов?
15 минут23%
20 минут19%
30 минут19%
40 минут18%
Час20%
Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 111 most recent posts we hold, published 5 August 2026 to 2 September 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
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 10 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.
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.
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.
Русская мода @russianmoda · 194,533 Telegram ranks this channel #6 of 82 here — alongside 81 others — read 11 August 2026
WYSH МОДА @wyshmoda · 36,471 Telegram ranks this channel #7 of 60 here — alongside 59 others — read 1 September 2026
didi_pro_ali @didi_pro_ali · 5 Telegram ranks this channel #8 of 46 here — alongside 45 others — read 5 September 2026
Dresses @dresses · 50,325 Telegram ranks this channel #9 of 64 here — alongside 63 others — read 25 August 2026
the girl who @girl_who · 79,891 Telegram ranks this channel #9 of 66 here — alongside 65 others — read 18 August 2026
ЛОСКУТОВА МАРИЯ @loskutova_maria · 93,129 Telegram ranks this channel #14 of 72 here — alongside 71 others — read 16 August 2026
Стильный стилист стилизует @leushinastylist · 47,193 Telegram ranks this channel #16 of 73 here — alongside 72 others — read 27 August 2026
Культура одеваться @kulturaodevatsa · 32,596 Telegram ranks this channel #17 of 72 here — alongside 71 others — read 5 September 2026
Мода быть собой @welcometoyourselfff · 33,588 Telegram ranks this channel #21 of 63 here — alongside 62 others — read 4 September 2026
Модный маркетолог @moda_marketingg · 31,284 Telegram ranks this channel #22 of 74 here — alongside 73 others — read 6 September 2026
Стильный Сырник @mariemets · 23,398 Telegram ranks this channel #23 of 71 here — alongside 70 others — read 18 September 2026
Outfits @outfits · 211,199 Telegram ranks this channel #24 of 81 here — alongside 80 others — read 11 August 2026
Зашиваюсь, но пишу @zakharova_studio · 23,699 Telegram ranks this channel #25 of 90 here — alongside 89 others — read 17 September 2026
Птичка не дремлет @bird_doesnt_doze · 24,103 Telegram ranks this channel #27 of 66 here — alongside 65 others — read 16 September 2026
Azalia L/ stylist @azalial · 42,554 Telegram ranks this channel #27 of 69 here — alongside 68 others — read 29 August 2026
Доступный стиль by Tsvetaeva @korolevatsvetaeva · 27,766 Telegram ranks this channel #29 of 65 here — alongside 64 others — read 10 September 2026
Д Р Е С С - К О Д (ex. Dressed to Kill) @dressedto · 41,678 Telegram ranks this channel #29 of 71 here — alongside 70 others — read 29 August 2026
Fashion dnevnik @fashiondnevnik · 26,418 Telegram ranks this channel #33 of 74 here — alongside 73 others — read 12 September 2026
GLAMORAMA @glamoramablog · 28,837 Telegram ranks this channel #34 of 75 here — alongside 74 others — read 9 September 2026
Хот Кутюр @hotcouture · 23,007 Telegram ranks this channel #40 of 79 here — alongside 78 others — read 19 September 2026
Стилист Оксана Самарина @OksanaSamarina · 31,078 Telegram ranks this channel #40 of 53 here — alongside 52 others — read 6 September 2026
Lavrova Pro Style @lavrovaprostyle · 26,929 Telegram ranks this channel #42 of 70 here — alongside 69 others — read 11 September 2026
Дом Моды @fashioninn · 24,732 Telegram ranks this channel #43 of 74 here — alongside 73 others — read 15 September 2026
Купи, молись, люби @buy_pray_love · 46,084 Telegram ranks this channel #46 of 72 here — alongside 71 others — read 27 August 2026
This channel appears in 38 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.
“На полном обвесе с Мари” (@stylewithmary), 19,673 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/stylewithmary.
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.