Channel

Фиджитал Фэшн Лаб

@lekalnya3d

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

90subscribers

+3 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID	-1002260850423
Type	Channel
Username	@lekalnya3d
Created	Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded	9 August 2026
Last confirmed live	14 August 2026
Measurements held	3
Confirmed unchanged	1 time, most recently 14 August 2026
On Telegram	t.me/lekalnya3d

Growth

3 measurements spanning 8 days, net +3. 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 87–90 and does not start at zero.

Measurement log — every subscribers count we have recorded
Measured (UTC)	Subscribers	Change
14 Aug 2026, 13:27	90	+3
9 Aug 2026, 15:47	87	no change
6 Aug 2026, 04:48	87	first reading

Engagement

5 posts held, back to 15 May 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 pageof 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 26 June 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime: 2m 05s
Average length: 31s

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

15 reactions across 5 posts, in 2 distinct kinds. The most used accounts for 93.3% of them.

Every reaction kind recorded on the sample, most used first
Reaction	Count	Share	Share, drawn
custom 5278677628994210330	14	93.3%
custom 5251206730406184212	1	6.67%

Custom emoji. Every row above is a 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 areTelegram’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 15reactions 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 15 May 2026 to 26 June 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.

Recent posts

26 Jun 2026, 06:37 UTC62 views3 reactionsread 9 August 2026

Video

3D печать из био🪴материалов Дизайнер из Португалии Danit Peleg напечатала у себя в студии коллекцию платьев на 3D-принтере из полностью биоразлагаемых материалов В коллаборации с Algenesis Labs и ColorFabb она работает с биофиламентом на основе касторового масла, конопли и водорослей 🍃 Без микропластика 🍃 Без отходов 🍃 Изделие в конце жизненного цикла можно компостировать или переработать А процесс от дизайна до п…

custom 52786776289942103303

19 Jun 2026, 08:14 UTC68 views1 reactionsread 9 August 2026

🔝 3D примерка в производственном пайплайне: наглядный кейс Проработка изделия в 3D позволяет не только согласовать пропорции и дизайн, но и заранее выявить и скорректировать конструктивные нюансы посадки ещё до пошива физического образца А дополнительная обработка 3D рендеров с помощью нейросетей имитирует реальную съёмку и делает виртуальную примерку ещё более наглядной для заказчика 🌿

custom 52512067304061842121

19 Jun 2026, 08:14 UTC60 views3 reactionsread 9 August 2026

Forwarded from @factory_basePhoto

Давно хотела рассказать про наш мерч для Газпром медиа на ПМЭФ. Нашей задачей было одеть персонал для стенда и произвести мерч. Но! в рекордно короткие сроки. У нас было около 3 недель для разработки сложных коктейльных платьев с нуля, на подбор материалов, отработку образцов и индивидуальный пошив. Spoiler: мы справились!) Начали мы с мудборда. Нужно было нащупать то, что нравится заказчику. Потом был не легкий …

custom 52786776289942103303

1 Jun 2026, 08:42 UTC71 views4 reactionsread 9 August 2026

Video

👩🏻‍🎓 Повышаю квалификацию Недавно прошла курс Марии Шевченко по методу каркасного построения жакетов в Clo3D Инструмент 3D pen 🖊 появился в программе уже довольно давно, а в последних версиях появилась возможность рисовать не только по поверхности аватара, но и вокруг него 🤩 Это требует определённой сноровки, но, главное, предлагает совершенно иную логику, чем привычные методики, базирующиеся на расчётных формулах…

custom 52786776289942103304

15 May 2026, 13:48 UTC86 views4 reactionsread 9 August 2026

Photo

🚂 Отправляемся назад в будущее В музее моды MoMu в Антверпене создали digital twin исторического платья XVIII века robe à l'anglaise 🤯 Причём это не просто 3D-скан, а полноценная цифровая реконструкция изделия! 📍Разработана в Clo3D и Blender Платье можно буквально "разобрать" и изучить по частям: 🤎 покрутить модель, 🤎 рассмотреть конструкцию, 🤎 изучить материалы, 🤎 и даже скачать лекала 🤩 Проект выполнен командой…

custom 52786776289942103304

Showing the 5 most recent of 5 posts we hold for @lekalnya3d. 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.

Citation-graph rank

Citation-graph rank — 204,724 of 1,481,217entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Republishes

Channels on the register whose posts this channel has forwarded.

Фэктори Бейс-с-с🤫. Закулисье
@factory_base · 3,1921 post

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 1 registered channel — 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.

Jobpower Jobseekers – база творческих фрилансеров, резюме
@jpjobseekers · 3,3281 post

Names

Channels on the register whose handles appear in this channel's posts.

3dcouture
@couture3d · 6,3961 post

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 14 August 2026 — this entry's latest reading, not the date you are reading this.

“Фиджитал Фэшн Лаб” (@lekalnya3d), 90 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/lekalnya3d.

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.