2 measurements taken within a single day. 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 303–305 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
8 Aug 2026, 07:50
304
no change
8 Aug 2026, 04:35
304
first reading
Engagement
8 posts held, back to 22 November 2024 — 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.
ERR · 30 days
123.7%
avg views ÷ 304 subscribers
Avg views / post
376
1 post measured
Reaction rate
9.57%
reactions ÷ views · ER floor
Posts in window
1
of 8 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
Window
Rolling 30 days · latest post in window 31 July 2026
Posts held
8 (22 November 2024 – 31 July 2026)
Views total
376
Reactions total
36
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
8 Aug 2026, 07:50 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.
What this channel posts
Photos
37
Videos
4
Links
5
Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Video runtime
1m 48s
Average length
36s
Measured directly from 3 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
152 reactions across 7 posts, in 10 distinct kinds. The most used accounts for 38.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🔥
59
38.8%
❤
49
32.2%
👍
12
7.89%
🤯
8
5.26%
🌚
6
3.95%
🤷♀
6
3.95%
😁
4
2.63%
🦄
4
2.63%
🗿
2
1.32%
🤓
2
1.32%
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 152reactions 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 22 November 2024 to 31 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.
Telegram Stars
Stars received
5
across the posts below
Posts paid on
1
of 8 we hold a reading for · 13%
Most on one post
5
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @contextmachine. 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 22 November 2024 to 31 July 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.
teptep: открываем бета-тестирование 🖤
Когда-то мы рассказывали про teptep – инструмент для автоматизации расчёта проектных метрик и анализа 3D-моделей. Сегодня наконец открываем его тестирование!
Что уже умеет teptep:
→ Шаблон для работы с массингами
→ Расчёт GEA, а также GFA, NFA и SA по коэффициентам
→ Автоматическая проверка инсоляции
→ Спецификации прямо из 3D-модели
→ Синхронизация со SketchUp – обновляйте мо…
Внимание, важный вопрос! На одной картинке реальные планировки из датасета, на другой полностью сгенерированные. На какой картинке генерация? (снизу опрос будет)
История заточения в башне или о том, как появилось CXM сопровождение
Все началось, когда мы оказались в "башне" с "горящим проектом", и, казалось, простого выхода для нас никто не предусмотрел. Стройка была в разгаре: несколько отдельных инженерных команд со своими пайплайнами и инструментами, криволинейная поверхность, уникальные элементы, фасадная многослойная конструкция. Все начали догадываться, что без нового п…
У нас открыта вакансия: специалист со знанием grasshopper
Кого мы ищем:
В команду сопровождения проектов ищем нескольких специалистов со знанием Rhinoceros + Grasshopper, Revit+Rhino.Inside.
Ты нам подходишь, если:
- У тебя хорошее объемно-пространственное мышление, ты готов решать задачи со сложными геометрическими построениями и участвовать в комплексных проектах с большим количеством взаимосвязей и сущностей.
- …
Черчение камней
Постигая программирование, мы обнаружили, что существует множество алгоритмов способных решать повседневные проектные задачи.
Вот классный пример:
Будем честны, все мы заимствовали элементы с чужих чертежей - мебель, растения, людей. Особенно при работе над какой-то художественной подачей или конкурсом. Мы тратили кучу времени на обчерчивание красивых элементов с картинок или попытки найти подходящий…
Искусство в сердце города – проект Deus Particula.
Сегодня продолжим рассказ о нашем проекте, который уже находится на финальной стадии реализации — Монументальное панно Deus Particula в лобби Moscow Towers. Этот проект демонстрирует, как искусство может стать неотъемлемой частью делового сити, где технологии служат инструментом для точной реализации творческого замысла.
На стройке особенно заметно, как усилия множ…
Посадил ты значит нейросеть программировать за тебя, платишь за токены, пришел, а он фотки смотрит вместо работы.
Even while we were recording demonstrations of computer use for today’s launch, we encountered some amusing errors. In one, Claude accidentally clicked to stop a long-running screen recording, causing all footage to be lost. In another, Claude suddenly took a break from our coding demo and began to perus…
❤6🤷♀6😁4
Signed Svetlana Vasilenko
Showing the 8 most recent of 8 posts we hold for @contextmachine. 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.
Citation-graph rank
Citation-graph rank — 684,073 of 1,183,361entries 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
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Declared links
2 URLs this entry has put in its own public description, shown exactly as written there. A declaration, not a verified association— we measured that the description contained this text, and nothing more. It is not a claim that this entry owns, runs or is connected to whatever the link resolves to; a description is text anyone controls, and anyone can put anyone else’s URL into it. How this is measured.
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 8 August 2026 — this
entry's latest reading, not the date you are reading this.
“cxm” (@contextmachine), 304 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/contextmachine.
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.
