Personal blog — 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 29 September 2026 and assigned it the closest of 31 fixed categories, at 63% 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
4 measurements spanning 53 days, net -2. 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 47–49 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
29 Sept 2026, 15:42
47
-1
16 Aug 2026, 05:59
48
-1
10 Aug 2026, 12:30
49
no change
7 Aug 2026, 17:00
49
first reading
Engagement
6 posts held, back to 15 October 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 6 posts for this entry, the most recent from 28 December 2025. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
31s
Average length
16s
Measured directly from 2 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
166 reactions across 6 posts, in 7 distinct kinds. The most used accounts for 32.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
54
32.5%
🥰
38
22.9%
🔥
34
20.5%
🎉
18
10.8%
🙏
10
6.02%
👍
9
5.42%
👏
3
1.81%
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 6 of the 6 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 166 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 6 most recent posts we hold, published 15 October 2025 to 28 December 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
ловите шаблон «итоги 2025 года» в Cap Cut
Идеальный reels для окончания года, словно фильм, о котором вы можете рассказать ✨
кликабельны ссылка на шаблон — ссылка 🔗
как использовать?
• скачайте приложение CapCut (если ещё нет)
• включите впн
• открой шаблон по ссылке
• перейдите в приложение
• добавляйте ваше видео
чтобы видео было без водяного знака, то сохраняйте через тик-ток (потом просто закрываете приложен…
Вчера с мужем ездили в spa на 4 часа 🧖♀️
Сначала было джакузи на улице с тепленькой водой, и тепленькой мной после шампанского 😂
Потом нас пригласили в хамам. Там парились, затем один скраб, второй скраб, помыли голову, какой-то эликсир нанесли на волосы и бальзам. Ещё попарились.
Бальзам какой-то нереальный, волосы аж рассыпаются, такие нежные. Я его потом нашла в ЗЯ. 4 тыс стоит, надо брать, скину вам потом ссы…
Даже не думала, что он на меня сядет так идеально 🤩 теперь нужно срочно выгулять этот пиджак. Принимаю приглашения (от девочек 😅) попить кофээ или выпить бокальчик ☕️🍷
Артикул на пиджак ВБ - 561248798
Фото с прошлой уличной фотосессии так и не опубликовала 🤪 точнее опубликовала, но не здесь. Чаще я бываю в нельзяграм ) сейчас готовлю большой сторитейлинг на тему переезда в Москву, как это было, как выживала первый год. Выставлю сегодня в сториз и закреплю. Там будет очень интересно) приходите почитайте 😎
❤14🔥7👍5👏3
Signed Kabko Olga
Showing the 6 most recent of 6 posts we hold for @olgakabko. 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.
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 29 September 2026 — this
entry's latest reading, not the date you are reading this.
“Ольга Кабко рассказывает!” (@olgakabko), 47 subscribers as measured 29 September 2026. Telegram Register, tgregister.com/channel/olgakabko.
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.
