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Channel

STRUCT

@struct_pro

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

141subscribers

-1 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-1001321316350
TypeChannel
Username@struct_pro
CreatedBetween 1 March 2018 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live20 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 20 August 2026
On Telegramt.me/struct_pro

Growth

1411431426 August 2026 — 142 subscribers8 August 2026 — 142 subscribers13 August 2026 — 143 subscribers20 August 2026 — 141 subscribers6 August 202620 August 2026
4 measurements spanning 14 days, net -1. 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 141–143 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
20 Aug 2026, 22:47141-2
13 Aug 2026, 13:04143+1
8 Aug 2026, 21:46142no change
6 Aug 2026, 12:16142first reading

Engagement

19 posts held, back to 20 April 2026the 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.

ERR · 30 days
4.96%
avg views ÷ 141 subscribers
Avg views / post
7.0
1 post measured
Reaction rate
14.3%
reactions ÷ views · ER floor
Posts in window
1
of 19 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.

What these figures were computed from
WindowRolling 30 days · latest post in window 30 July 2026
Posts held19 (20 April 202630 July 2026)
Views total7
Reactions total1
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 21:46 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

Reaction mix

3 reactions across 2 posts, in 1 kind.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥3100.0%

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 2 of the 19 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 3reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 20 April 2026 to 30 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.

Recent posts

30 Jul 2026, 16:21 UTC7 views1 reactionsread 8 August 2026
Forwarded from @apgraderPhoto

Для программы Executive MBA прорабатываем тему Культурные коды стран Существует теория, согласно которой начинать вычисление культурного кода следует с размера территории государства (есть и иные факторы, о них скажу отдельно). В расчёт берётся площадь территории страны, приведённая к кругу. По радиусу круга страны делятся на семь условных типов: 1. 0-54 км: Дом (Сингапур) 2. 55-160 км: Область (Армения) 3. 160-400

🔥1

29 Apr 2026, 11:46 UTC23 viewsread 8 August 2026
Photo

MedTech part 8. Работаем с MedTech-проектом по стратегии и инвестициям. Масштаб — только в РФ сейчас 550 000 врачей, которые непосредственно диагностируют и лечат Продукт — co-pilot для врачей. Делает моментальный research строго по медицинским данным и даёт решения под конкретного пациента. В перспективе — полноценная медицинская ОС с архитектурой AI-first, ориентированная на точность принятия клинических решений

29 Apr 2026, 11:43 UTC20 viewsread 8 August 2026
Photo

MedTech part 7. Клинреки. Впервые услышал это слово и представил реку в Швейцарии, но речь про медицину. Продолжаю работать с MedTech-проектом, формируем стратегию, pitchdeck и инвестиционный раунд Не хотел бы быть врачом — в 2026 году он (она) меж двух огней. Даже четырёх огней: 1. Минздрав ввёл обязательные Клинические Рекомендации (клинреки, КР), их порядка 700 и реестр пополняется. За отклонение может следовать

29 Apr 2026, 11:41 UTC15 viewsread 8 August 2026
Photo

MedTech part 6. Главный плюс проекта в том, что есть конкурент — прямой аналог в США OpenEvidence — AI-софт для врачей, ARR $150 млн, инвестиции $250 млн по оценке $12 млрд. В США они охватили 40% врачей и применили продукт для 100 млн американцев Умный поиск работает на лицензированных мед. данных и RAG-архитектуре с привязкой цитат, не выходит в open-web. Может анализировать 100-200 статей параллельно и составить

29 Apr 2026, 11:39 UTC14 viewsread 8 August 2026
Photo

MedTech part 5. Карта метаболизма. Чтобы похудеть и уменьшить % жира в теле, нужны 103 биохимические реакции (липолиз, активация, транспорт, β-окисление, цикл Кребса, дыхание – 12 типов реакций проходят в 5 этапов) Продолжаю работу с MedTech-проектом по стратегии и Pitch Deck — увидев огромную схему, осознал уровень сложности и кол-во переменных. Сколько же параметров нужно учесть врачу, чтобы принять клиническое ре

29 Apr 2026, 11:35 UTC11 viewsread 8 August 2026
Photo

MedTech part 4. Структурирую MedTech-проект, разбираем индустрию. Что, если нагло представить возможный сценарий будущего? 2040 год. Входишь в кабинет врача, он сразу формулирует точный по смыслу запрос в Мировую Медицинскую Систему. Там уже есть все твои биомаркеры, ДНК, рост, вес, сон, спорт, история болезней, травм и психологических потрясений. Система анализирует 5000 лет мирового опыта, статьи, клинические случ

29 Apr 2026, 11:23 UTC11 viewsread 8 August 2026
Photo

MedTech part 3. Big Data в медицине. Готовлю MedTech-проект к масштабированию и инвестраунду, уже рассказал о рынке объёмом 12 трлн $ и бездне сценариев диагностики (их порядка 1 млрд и это консервативная оценка) В чём проблема с данными в медицине? Выделим две проблемы: 1. Преемственность: данные анализов, болезней, процедур, аллергий, травм теряются. Между разными больницами, стационарами, частными клиниками и л

29 Apr 2026, 11:17 UTC12 viewsread 8 August 2026
Photo

MedTech part 2. 10 млрд сценариев и 4 измерения. Продолжаю рассказ о MedTech-проекте, вторым этапом для Pitch Deck мы углубились в проблематику. Вам врач ставил ложный диагноз? По оценкам, в США 800 000 пациентов умирают либо получают инвалидность из-за диагностических ошибок. Пропускают травмы и онкологию, а когда 10 лет болит спина и плохо со сном — говорят что-то в духе “ничего страшного, само пройдёт” В защиту

29 Apr 2026, 11:14 UTC16 viewsread 8 August 2026
Photo

12 трлн. Сейчас работаю по стратегии и инвестициям с проектом в сфере MedTech — впечатлён размером отрасли, на которую можно влиять Рынок медицины разбит на три сегмента: — 9,3 трлн $ медицинские услуги и сервисы; — 1,9 трлн $ лекарства; — 0,7 трлн $ оборудование Общий объём порядка 11,9 трлн $ Проект как раз в сегменте медицинских услуг и их цифровизации, но логично говорить о всём рынке, т.к. всё связано и взаи

Showing the 12 most recent of 19 posts we hold for @struct_pro. 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.

Forward network

Republishes

Channels on the register whose posts this channel has forwarded.

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

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.

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

“STRUCT” (@struct_pro), 141 subscribers as measured 20 August 2026. Telegram Register, tgregister.com/channel/struct_pro.

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.