Music — 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 15 September 2026 and assigned it the closest of 31 fixed categories, at 45% 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
12 measurements spanning 42 days, net +13. 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 2,604–2,632 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
17 Sept 2026, 17:17
2,622
-7
9 Sept 2026, 23:36
2,629
+6
4 Sept 2026, 08:00
2,623
+15
31 Aug 2026, 12:33
2,608
+1
28 Aug 2026, 03:42
2,607
-7
25 Aug 2026, 03:51
2,614
-4
18 Aug 2026, 17:42
2,618
+2
15 Aug 2026, 18:52
2,616
+5
12 Aug 2026, 17:27
2,611
+4
9 Aug 2026, 12:33
2,607
-1
6 Aug 2026, 22:20
2,608
-1
6 Aug 2026, 19:31
2,609
first reading
Engagement
8 posts held, back to 1 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 2 pages 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 8 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
51s
Average length
13s
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
216 reactions across 7 posts, in 5 distinct kinds. The most used accounts for 57.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
custom 5312458358526058606
124
57.4%
🤩
29
13.4%
custom 5312174912159359755
28
13.0%
custom 5253790286378721175
19
8.80%
custom 5312008662565267016
16
7.41%
Custom emoji. 4 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 7 of the 8 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 216 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 8 most recent posts we hold, published 1 August 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.
Telegram Stars
Stars received
4
across the posts below
Posts paid on
2
of 7 we hold a reading for · 29%
Most on one post
3
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @fortepianostory. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 8 most recent posts we hold for this entry, published 1 August 2026 to 7 August 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Как нейросеть заставила меня исполнить большую мечту
Я всегда смотрела на дирижеров с замиранием сердца. Учась в Консерватории, я записалась на факультатив по симфоническому дирижированию и ходила «дышать этим воздухом». Занималась увлеченно, репетировала жесты у каждого зеркала
Знакомство с Теодором Курентзисом вообще перевернуло многое в моей жизни: я начала просто гореть дирижированием
Но дирижировать на сцене …
🔤стория как я однажды упустила Париж
Люди часто жалеют о том, что сделали. А я расскажу о том, что я не сделала и жалею
В 22 года я выиграла очень серьезный международный конкурс. Он был посвящен французской пианистке Вере Лотар-Шевченко, там были совершенно звездные пианисты-участники (Станислав Корчагин, Константин Хачикян, Сергей Белявский, Марсель Тодокоро и другие), но сейчас речь не об этом…
Председателем жю…
🔤onjour, дорогие друзья!
Меня зовут Ольга Иванова, и я пианистка, композитор, педагог Московской консерватории и руководитель камерного оркестра Musette
Многие меня знают, потому что я автор блога «Записки Пианистки» на YouTube
Сейчас вы в моём telegram-канале
Что он вам даст?
▪️ Закулисная жизнь творческой леди Москвы. Вы стоите около сценических занавесок и можете подсмотреть, что за ними 🎭
▫️ Мысли об искусс…
Моя мечта с 16 лет, которая пока не сбылась
Своих студентов я учу быть счастливыми и мечтать -
Но важно помнить, что мечты сбываются не всегда сразу
Расскажу историю из моего детства и юности
Мой папа занимался бизнесом в сфере капитального строительства. Он был ген.директором компании. В числе его объектов была знаменитая высотка Radisson и не только. Финансово папа мог себе позволить многое - и полностью вклады…
🔤елых семь лет я слышала слово "НЕТ"
Мне часто говорили, что у меня «нерояльные» руки
Моя история пианистки началась в 9 лет, когда я попала на концерт учеников Центральной музыкальной школы. На сцену выходили дети - некоторые даже были младше меня, но играли они так, что я сидела в зале совершенно ошеломлённая. В тот вечер я чётко поняла две вещи: во-первых, мне до их уровня как до небес, во-вторых, хочу быть здесь…
Showing the 8 most recent of 8 posts we hold for @fortepianostory. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Mentions
Named by 3 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.
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.
“Записки Пианистки” (@fortepianostory), 2,622 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/fortepianostory.
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.
