Education — 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 21 September 2026 and assigned it the closest of 31 fixed categories, at 83% 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
7 measurements spanning 41 days, net +66. 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 385–482 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
18 Sept 2026, 10:16
464
-7
10 Sept 2026, 15:39
471
+5
31 Aug 2026, 16:48
466
+58
24 Aug 2026, 07:09
408
+12
15 Aug 2026, 15:14
396
-2
8 Aug 2026, 05:50
398
no change
7 Aug 2026, 22:36
398
first reading
Engagement
3 posts held, back to 21 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 3 posts for this entry, the most recent from 5 August 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
31 reactions across 3 posts, in 5 distinct kinds. The most used accounts for 41.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
13
41.9%
custom 5377399144509887716
8
25.8%
custom 5244817485517057665
6
19.4%
👏
3
9.68%
❤🔥
1
3.23%
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 3 of the 3 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 31 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 3 most recent posts we hold, published 21 July 2026 to 5 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.
🌸Кружево снова в тренде, и не только в fashion. Сейчас его всё чаще можно увидеть в айдентике, оформлении соцсетей, упаковке, афишах и других носителях, поэтому мы подготовили небольшой туториал, как создать собственные кружевные элементы прямо в ChatGPT без долгой отрисовки вручную.
Промт:
Коллекция изящных белых рамок, созданных в эстетике винтажного кружева и тонкой ручной вышивки. Каждая рамка показана строго а…
Всем привет! Сегодня на связи Ангелина 😎
Недавно Юля уже поделилась работой нашей ученицы Эльнары — серией рекламных визуалов для бренда косметики 🍒🧡
На мой взгляд, эта работа — отличный пример того, как с помощью нейросетей можно создать визуал, который выглядит как настоящая коммерческая съемка.
▶️ Но за красивыми картинками всегда стоит большая работа
Поэтому сегодня предлагаем заглянуть чуть глубже и разобрат…
Работа выпускницы курса Эльнары Аблязизовой @ElnaraBelyal 🧡
Эльнара пришла на обучение будучи начинающим дизайнером и хотела научиться разрабатывать сильные и продуманные визуалы для брендов для роста в профессии!
В рамках курса она обучилась работе с текстовыми и визуальными нейросетями, а также научилась создавать полноценные видео🙌🏻 А также Эльнара научилась выстраивать кадр, работать с композицией, светом и дру…
Showing the 3 most recent of 3 posts we hold for @neuromadenrai. 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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“Нейросети | Madenrai School” (@neuromadenrai), 464 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/neuromadenrai.
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.
