Ecommerce storefront — 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 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.
Growth
32 measurements spanning 39 days, net +417. 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 25,649–26,454 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)
Subscribers
Change
15 Sept 2026, 19:16
26,159
-118
14 Sept 2026, 02:40
26,277
-33
12 Sept 2026, 08:16
26,310
-51
10 Sept 2026, 07:01
26,361
+87
7 Sept 2026, 02:16
26,274
-16
4 Sept 2026, 10:39
26,290
+78
3 Sept 2026, 01:25
26,212
+1
2 Sept 2026, 01:57
26,211
-15
1 Sept 2026, 00:24
26,226
-5
31 Aug 2026, 02:13
26,231
-9
30 Aug 2026, 04:24
26,240
-26
29 Aug 2026, 02:08
26,266
-14
28 Aug 2026, 01:34
26,280
+8
27 Aug 2026, 02:53
26,272
+7
26 Aug 2026, 04:35
26,265
+2
25 Aug 2026, 05:07
26,263
-20
24 Aug 2026, 01:48
26,283
+126
22 Aug 2026, 09:26
26,157
-20
20 Aug 2026, 20:52
26,177
+37
19 Aug 2026, 19:17
26,140
first reading
Engagement
29 posts held, back to 31 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 57 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.62%
avg views ÷ 26,159 subscribers
Avg views / post
1,730
18 posts measured
Reaction rate
2.63%
reactions ÷ views · ER floor
Posts in window
18
of 29 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 3 September 2026
Posts held
29 (31 July 2026 – 3 September 2026)
Views total
31,154
Reactions total
818
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 10:09 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
1m 41s
Average length
25s
Measured directly from 4 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
1,592 reactions across 29 posts, in 11 distinct kinds. The most used accounts for 55.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
885
55.6%
🔥
436
27.4%
🤩
116
7.29%
💘
57
3.58%
☃
44
2.76%
🙏
17
1.07%
🤗
16
1.01%
custom 5328224019533548825
10
0.628%
custom 5397842858126353661
5
0.314%
🎉
5
0.314%
😁
1
0.063%
Custom emoji. 2 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 are Telegram’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 29 of the 29 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 1,592 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 29 most recent posts we hold, published 31 July 2026 to 3 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.
рекомендация на осень - купите мех! 🐊
Не знаю как у вас, а у меня слюни текут, когда я вижу меховые изделия) Тут можно рассмотреть и кардиганы, и пальто, и меховые шарфы. Можно найти вариант на любой бюджет!
Такие вставки из меха выглядят очень элитарно!
🔥 Берите на заметку! А прошлую часть ищите тут
Из базы в яркий стиль 💥
Знакомьтесь, это Анастасия! И её история начинается не с «мне нечего носить» 😅
Наоборот - вещи есть, всё достаточно базовое, нормальное, сочетаемое… но СКУЧНО 😬
Хотелось смотреть на себя и видеть не просто «хорошо одетую девушку», а себя - яркую, интересную, со своей изюминкой!
При этом было совершенно непонятно, какие детали, цвета и сочетания действительно раскроют ЕЁ внешность, а какие б…
Самый ценный навык, который даёт менторство по стилю
Уже не первый раз выкладываю кусочки нашего созвона с Аней, уж очень много классного она сказала😁❤️
☝️Тут Аня поделилась новой «суперспособностью», которую дала ей наша работа, а именно способность при первом взгляде на вещи сразу видеть, что ей подходит, а что нет! Своего рода супер-зрение 👀
Аня назвала это датчиком)) ⚠️
Что дает этот датчик?
🔸Прежде всего он п…
новая порция тарелочек!
Ну что, я демон еды ещё тот))) сейчас у меня бзик на твороге. Ем творог 5% с греческим йогуртом каждый день! 🐊
Что на тарелочках:
- боулы с творогом и йогрутом с инжиром, персиком и голубикой
- драники с соусом дзадзыки и семгой (мне еще учиться и учиться их готовить 📚😭)
- запеченая форель в кокосовом молоке с бататом и морковью на подушке из взбитой феты
- паста с гребешками с обволакивающе…
🎙Хочу выглядеть по-другому, но всё равно ношу одно и то же 🥺
Что делать, если:
⏺хочется выглядеть женственнее / ярче / интереснее, но всё равно возвращаетесь к привычному;
⏺нашли пару вещей, которые сочетаются - и не вылезаете из них;
⏺на других нравятся совершенно разные образы, а на себе всё новое кажется «как будто не я»;
⏺покупаете красивые необычные вещи, но потом они остаются пылиться в шкафу;
⏺уже не первый г…
Из спорта в женственность 🌸
Новое преображение прекрасной Марии!! Она пришла ко мне на менторство с запросом уйти от привычного спортивного стиля и добавить больше женственности и нежности 🌸
Проблемы Марии:
🔸хочется выглядеть женственнее, но постоянно возвращается к привычному спортивному стилю 🥲
🔸женственные образы нравятся на других, а на себе - что-то не то
🔸красивые вещи появляются в шкафу, но в итоге снова поб…
Какую обувь купить на осень?
На самом деле все очень просто! Ваша обувь должна делиться на 2 основные категории - плоский ход и каблук. В зависимости от образа жизни меняйте кол-во обуви в этих группах.
Что предлагаю на осень?
Плоский ход:
- замшевые кроссовки
- бордовые или коричневые кожаные лоферы
- биркенштоки
Каблук:
- ботильоны леопард
- казаки на небольшом каблуке
Это прямо классные не банальные варианты,…
Подумываю в сентябре набрать несколько новых девушек к себе в работу… 🤔
Что-то меня стали много спрашивать про следующий большой набор на работу со мной… 🤔💭
Поэтому думаю в начале сентября снова публично дать анкету и выбрать новых девушек на менторство по стилю и анализ внешности, что думаете? ❓
🔥- да, хотим!
Я столько знаю про стиль, но всё равно не понимаю, что носить 🤬🥦
Сейчас информации про стиль больше, чем когда-либо. Можно определить типаж, найти свои цвета, сохранить 300 референсов в Pinterest, посмотреть разборы стилистов.
Можно даже открыть ChatGPT и попросить: «Подбери мне стиль и составь образы». Казалось бы, ну всё! Информации вагон - бери и становись стильной 😅
Но почему тогда вы всё равно открываете шкаф…
❤14🔥7custom 53282240195335488254
Showing the 12 most recent of 29 posts we hold for @soffashion. 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.
Posts edited after publishing
@soffashion edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
21 August 2026
Most recent edit
21 August 2026
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 12 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
annesoul про питание @annesoul1 · 45,316 Telegram ranks this channel #43 of 74 here — alongside 73 others — read 28 August 2026
Easy English @english_master_academy · 72,589 Telegram ranks this channel #75 of 75 here — alongside 74 others — read 19 August 2026
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. 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 15 September 2026 — this
entry's latest reading, not the date you are reading this.
“Софья Вострилова” (@soffashion), 26,159 subscribers as measured 15 September 2026. Telegram Register, tgregister.com/channel/soffashion.
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.