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 66% 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 43 days, net -82. 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 18,422–18,697 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)
Subscribers
Change
19 Sept 2026, 17:17
18,541
+21
17 Sept 2026, 06:21
18,520
-18
15 Sept 2026, 09:59
18,538
-10
13 Sept 2026, 15:57
18,548
+27
11 Sept 2026, 17:40
18,521
+67
9 Sept 2026, 07:20
18,454
-13
5 Sept 2026, 23:18
18,467
-20
3 Sept 2026, 17:36
18,487
-12
2 Sept 2026, 11:38
18,499
-2
1 Sept 2026, 13:54
18,501
-8
31 Aug 2026, 10:38
18,509
-13
30 Aug 2026, 08:35
18,522
-11
29 Aug 2026, 05:12
18,533
-6
28 Aug 2026, 07:12
18,539
-10
27 Aug 2026, 05:24
18,549
-17
26 Aug 2026, 03:04
18,566
+1
25 Aug 2026, 00:57
18,565
-14
23 Aug 2026, 10:47
18,579
-11
21 Aug 2026, 20:18
18,590
-8
20 Aug 2026, 13:22
18,598
first reading
Engagement
15 posts held, back to 24 June 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 54 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
1.62%
avg views ÷ 18,541 subscribers
Avg views / post
300
1 post measured
Reaction rate
2.67%
reactions ÷ views · ER floor
Posts in window
1
of 15 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
15 (24 June 2026 – 3 September 2026)
Views total
300
Reactions total
8
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 08:50 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
10m 24s
Average length
1m 29s
Measured directly from 7 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
346 reactions across 14 posts, in 5 distinct kinds. The most used accounts for 42.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
147
42.5%
🔥
124
35.8%
👍
65
18.8%
😍
6
1.73%
🥰
4
1.16%
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 14 of the 15 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 346 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 15 most recent posts we hold, published 24 June 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.
Не перестану его рекомендовать….
Консилер, который как ластик ✏️ стирает мои синяки под глазами❤️
Мне кажется я рассказываю о нем уже 100000 раз, хотя почему-то никто из бьюти блогеров о нем не говорит. А он лично для меня превзошел большинство вариантов из люкса.
Девочки, он прям плотный и поэтому не для всех. Но еслм у вас синяки под глазами или какие-то несовершенства кожи, о которых вы хотите забыть на весь де…
ПРОБЛЕМА ПОЧТИ ВСЕХ МАМ ПЕРЕД ШКОЛОЙ 👇🏻
Покупаем много вещей → тратим много денег → половину ребёнок не носит → вещи между собой не сочетаются.
РЕШЕНИЕ: покупать не больше, а правильнее.
Я уже собрала за вас 7 готовых школьных капсул, где минимум вещей = максимум образов 🤍
✓ 4 капсулы для девочек
✓ 3 капсулы для мальчиков
✓ разные цветовые сочетания
✓ готовые формулы образов
✓ одежда + обувь + рюкзаки + аксессуар…
Девочки, сегодня раскрываю вам секрет моей увлажнённой и сияющей кожи ✨
Вы очень часто спрашиваете меня про мой уход и чем я сейчас пользуюсь.
Во-первых, я практически полностью ушла от плотных тональных основ. А сейчас помимо корейского ухода всё чаще выбираю ещё и корейскую декоративную косметику.
Из последних находок ❤️ тональный крем KoreLab.
Мне очень нравится, какое покрытие он даёт. Кожа выглядит ровной, г…
Девочки, две очень бюджетные, но классные находки для ухода 🖤
1️⃣Крем-бальзам от трещин ImunoSkin
Изначально для пяточек, но благодаря мочевине, ланолину и пантенолу отлично работает на любой сухой коже. Руки, локти, стопы - смягчает, восстанавливает и помогает справиться с трещинками. За свою цену просто находка.
2️⃣Масло для кутикулы ImunoSkin
Его мне посоветовала моя мастер по маникюру и постоянно наносила после…
Девочки, снова заглянула в FINN FLARE - и опять вышла оттуда с отличной капсулой 🤍
В этот раз собрала вещи, которые легко миксуются между собой и дают огромное количество совершенно разных образов - от расслабленных на каждый день до более элегантных.
И за что я особенно люблю FINN FLARE - качество и составы. Здесь действительно умеют делать вещи, которые классно выглядят, приятно ощущаются и при этом остаются актуа…
Вчера отмечали День Рождения 🎁 Ангелинки🤩
И теперь очень многие подписчики просят меня рассказать как сделать такой же REELS
Итак поехали👇
1️⃣ в Nano Banana загружайте фото ребёнка и пишите этот промпт👇
( в комментариях)
2️⃣ далее переходите в Kling 3.0 , прикрепляете уже сгенерированное фото со львом , качество 4К, 10 сек, и пишите второй промпт👇
Нажимаем сгенерировать 🖤
Вот и все📸 пробуйте)
Постоянно получаю вопросы , как я делаю такую укладку!
Девочки, это проще простого, а главное занимает пару минут!
Вам понадобится: вода, расческа, гель для волос! Это все🤩
Весь процесс оставила в REELS☝️
❤️можно делать сразу после душа на мокрые волосы!
А для отдыха это вообще лучший лайфхак👌
Showing the 12 most recent of 15 posts we hold for @sabina_scandihome. 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.
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
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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“Sabina_scandihome” (@sabina_scandihome), 18,541 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/sabina_scandihome.
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.