Channel
@seenbyher
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
161subscribers
+4 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002614158905 |
|---|---|
| Type | Channel |
| Username | @seenbyher |
| Created | Between 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 14 September 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 14 September 2026 |
| On Telegram | t.me/seenbyher |
Art & design — 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 25 September 2026 and assigned it the closest of 31 fixed categories, at 52% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Sept 2026, 04:01 | 161 | +1 |
| 5 Sept 2026, 18:58 | 160 | -1 |
| 29 Aug 2026, 15:49 | 161 | -1 |
| 23 Aug 2026, 12:17 | 162 | +4 |
| 15 Aug 2026, 20:17 | 158 | +1 |
| 8 Aug 2026, 18:15 | 157 | no change |
| 7 Aug 2026, 18:02 | 157 | first reading |
9 posts held, back to 29 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 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 9 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
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.
30 reactions across 8 posts, in 4 distinct kinds. The most used accounts for 53.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 16 | 53.3% | |
| ❤🔥 | 6 | 20.0% | |
| 👍 | 5 | 16.7% | |
| 💯 | 3 | 10.0% |
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 8 of the 9 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 30 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 9 most recent posts we hold, published 29 July 2026 to 7 August 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.
И вот здесь особенно хорошо видно, зачем нужен хороший хоумстейджер. Он умеет не просто красиво оформить квартиру, а найти недорогие вещи, которые действительно меняют восприятие пространства и хорошо работают в кадре. Например, здесь Женя нашла: — комплект столиков у дивана — 6 т.р — клетчатый ковёр — 5т.р — скатерть на стол — 1,4т.р — торшер у дивана — 4 т.р — две прикроватные лампы — 5 т.р. за обе. Крупную мебел…
💯3
Покажу сегодня вам чистый ХОУМСТЕЙДЖИНГ без ремонта! Квартира на продажу в Екатеринбурге, ЖК «Александровский квартал». Идея, стиль и хоумстейджинг — от Евгении Родыгиной. Я немного добавила декор, подобрала ракурсы — и вуаля! 🤌🏻 Небольшие изменения — и совсем другое настроение пространства. Что сделала Женя в этом проекте: — добавила обои за кроватью; — обновила стулья; — добавила освещение, текстиль и декор. В…
❤1👍1
Квартира в долгосрочную аренду по проекту Томы 🤎 Цифры сроки, вы просили, я сделала) В комментариях делюсь заметками Томы по этой квартире. Бюджет 400т.р.🔥
❤🔥6
Следом я снимала второй объект Ксении Луганской. 🌿 И здесь мы тоже сделали фотостейджинг вместе с Катей Маркиной: подобрали текстиль, декор и добавили те самые детали, которые создают ощущение дома. После преображения и новых фотографий просмотров объекта стало заметно больше. И это лучшее подтверждение того, что визуальная подготовка действительно работает. ❤️ Кстати, цветы для съемки мы купили накануне в студии …
❤5
Флип-проект Ксения Луганская Фотостейджинг от меня и Кати Маркиной✨ В комментариях показываю наглядно как меняется пространство🤌🏻
❤4👍1
Перед публикацией эта квартира прошла фотостейджинг — подготовку к съемке. ✨ Вместе с моей напарницей Екатериной Маркиной мы подобрали текстиль и декор: часть привезли из своей коллекции, а некоторые предметы купили специально для этого проекта и оставили в квартире. Главная задача показать проект в самом выгодным свете и его сильные стороны. Это отличное решение, если у вас нет времени заниматься деталями самосто…
❤5
Вы спрашивали, я расскажу) Флиппинг — это когда квартиру в старом фонде покупают по выгодной цене, полностью преображают и продают значительно дороже! Главная ценность здесь — увидеть потенциал там, где другие видят только старый ремонт🤌🏻 Грамотная планировка, современный интерьер и внимание к деталям могут увеличить стоимость объекта существенно. Это не просто ремонт, а создание жилья, в которое хочется переехать с…
👍3
и живая история дома вместе с Ксенией✨
Инвест-квартира на продажу🌸 Флип-проект Ксения Луганская
❤1
Showing the 9 most recent of 9 posts we hold for @seenbyher. 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.
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.
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 14 September 2026 — this entry's latest reading, not the date you are reading this.
“Лена Попова интерьерный фотограф” (@seenbyher), 161 subscribers as measured 14 September 2026. Telegram Register, tgregister.com/channel/seenbyher.
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.