Technology — 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 20 September 2026 and assigned it the closest of 31 fixed categories, at 93% 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
9 measurements spanning 63 days, net -24. 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 653–685 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Oct 2026, 04:01
657
-4
29 Sept 2026, 12:20
661
-4
15 Sept 2026, 01:17
665
-3
6 Sept 2026, 11:20
668
-4
29 Aug 2026, 18:13
672
+1
23 Aug 2026, 02:24
671
-7
15 Aug 2026, 16:09
678
-3
8 Aug 2026, 02:31
681
no change
7 Aug 2026, 16:36
681
first reading
Engagement
5 posts held, back to 20 April 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 5 posts for this entry, the most recent from 19 July 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
16s
Average length
16s
Measured directly from 1 video 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
132 reactions across 5 posts, in 17 distinct kinds. The most used accounts for 26.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
35
26.5%
❤
31
23.5%
👍
19
14.4%
👏
7
5.30%
🎉
6
4.55%
💯
6
4.55%
custom 5395357957552609518
5
3.79%
❤🔥
5
3.79%
🆒
5
3.79%
custom 5339220291157500400
3
2.27%
🏆
2
1.52%
😍
2
1.52%
🥰
2
1.52%
custom 5190660975866437599
1
0.758%
custom 5332696592317160796
1
0.758%
custom 5472178859300363509
1
0.758%
🙏
1
0.758%
Custom emoji. 5 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 5 of the 5 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 132 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 5 most recent posts we hold, published 20 April 2026 to 19 July 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.
Как идея «по фану» принесла мне 688 подписчиков и 230 тыс.
просмотров за две недели 🔥
Пока я в мини-отпуске, мои нейроэксперименты продолжают набирать обороты 😄
Недавно одним томным вечером я смотрела YouTube, и мне попалась смешная переозвучка моего любимого аниме «Монолог фармацевта» от SharoH 😀
И тут в голову пришла очередная идея: а что, если сделать персонажей аниме в виде реальных людей и сгенерировать с ним…
ChatGPT - не только про одну красивую картинку, ещё и про развитие целой визуальной серии
Решила протестировать в ChatGPT одну идею на тему глобального потепления ☀ Началось всё с первого плаката: девушка в шубе лежит на тающем кубике льда посреди пляжа 🧊
В теме глобального потепления легко уйти в очевидное: горящая планета, трещины на земле, дым и грустный белый медведь. Я решила зайти через другую метафору: что б…
У нейросети Krea вышло новое обновление 🔥
Протестировала новую версию нейросети Krea — Krea 2, и первое ощущение: изображения стали заметно реалистичнее🤩 Не просто «красивее», а именно ближе к фотографии: больше живых текстур, меньше пластика, меньше ощущения сильной нейросеточной ретуши.
Для сравнения взяла один и тот же промпт и прогнала его в двух нейросетях:
1 изображение — Krea 2
2 изображение — MidJourney
И …
🔥 Новая версия ChatGPT Image — вот это уже вау
Решила протестировать обновление, но не просто создавая рандомную картинку, а работая с готовым исходником — сначала сделала генерацию в Midjourney и пошла тестить ChatGPT на сохранение исходных черт, смену ракурсов, эмоций и прочего.
И это ой как хорошо.
Теперь ChatGPT очень точно держит консистентность:
✔️ сохраняет черты лица
✔️ не меняет макияж (даже сложный!)
✔️ …
🔥 Как избавиться от «пластиковой» кожи за 2 минуты (и не убить реализм)
Недавно делала одну генерацию в Midjourney и словила классическую проблему:
Вроде красиво…
но кожа — идеально гладкая, как у куклы 🫠
— нет пор
— глаза слегка «мыльные»
— волосы как будто прорисованы кистью
Смотришь и сразу в голове проскакивает:
«ага, нейросеть»
Решила быстро это поправить, без сложных схем 👇
👉 Беру эту же картинку
и закиды…
🔥9👍7❤5🎉2👏2custom 53953579575526095182❤🔥1💯1
Showing the 5 most recent of 5 posts we hold for @neu_ronka. 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 10 October 2026 — this
entry's latest reading, not the date you are reading this.
“НЕЙРОНКА” (@neu_ronka), 657 subscribers as measured 10 October 2026. Telegram Register, tgregister.com/channel/neu_ronka.
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.