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 10 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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
33 measurements spanning 44 days, net +128. 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 15,954–16,123 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
19 Sept 2026, 06:21
16,101
-2
16 Sept 2026, 21:19
16,103
+13
14 Sept 2026, 22:58
16,090
+1
11 Sept 2026, 13:41
16,089
-1
8 Sept 2026, 22:21
16,090
-5
5 Sept 2026, 13:38
16,095
-2
3 Sept 2026, 15:38
16,097
+1
2 Sept 2026, 08:42
16,096
+2
1 Sept 2026, 11:47
16,094
+11
31 Aug 2026, 09:35
16,083
+16
30 Aug 2026, 08:37
16,067
+13
29 Aug 2026, 09:27
16,054
+9
28 Aug 2026, 07:37
16,045
+6
27 Aug 2026, 05:17
16,039
+13
26 Aug 2026, 03:04
16,026
+3
25 Aug 2026, 04:07
16,023
+12
23 Aug 2026, 23:28
16,011
-4
20 Aug 2026, 23:43
16,015
-5
19 Aug 2026, 20:36
16,020
+1
18 Aug 2026, 23:32
16,019
first reading
Engagement
53 posts held, back to 20 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 54 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
27.2%
avg views ÷ 16,101 subscribers
Avg views / post
4,380
10 posts measured
Reaction rate
1.27%
reactions ÷ views · ER floor
Posts in window
10
of 53 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 3 September 2026
Posts held
53 (20 July 2026 – 3 September 2026)
Views total
43,790
Reactions total
554
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 10:09 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
Video runtime
1m 38s
Average length
25s
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
2,946 reactions across 51 posts, in 25 distinct kinds. The most used accounts for 16.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
🤔
476
16.2%
👍
431
14.6%
🤬
412
14.0%
🔥
386
13.1%
❤
334
11.3%
💯
319
10.8%
🤯
136
4.62%
😱
117
3.97%
🤩
86
2.92%
👏
68
2.31%
🤷♂
47
1.60%
✍
42
1.43%
😎
27
0.916%
🤝
17
0.577%
👌
14
0.475%
🌚
6
0.204%
🍾
6
0.204%
🤷♀
5
0.17%
🙏
4
0.136%
🏆
3
0.102%
5 further kinds
10
0.339%
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 53 of the 53 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 3,102 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 53 most recent posts we hold, published 20 July 2026 to 3 September 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 53 we hold a reading for · 2%
Most on one post
5
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @darpaandcia. 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 53 most recent posts we hold for this entry, published 20 July 2026 to 3 September 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.
Гонка кремниевых архитектур: как DARPA выбирает между транзисторами и конденсаторами для боевого ИИ
С 2023 года DARPA реализует программу OPTIMA с общим бюджетом в 78 миллионов долларов. Её основная задача — преодолеть фундаментальный кризис современной вычислительной техники и создать сверхэффективные ИИ-ускорители на базе вычислений в массивах памяти. Однако вместо того чтобы вкладываться в единый стандарт, Пентаг…
Новая архитектура OpenAI ставит под вопрос контроль над ИИ
OpenAI, компания-создатель ChatGPT, готовится к релизу своей новой модели Astra. По предварительным тестам она показывает выдающиеся результаты во многих задачах. Но главная новизна — не в производительности, а в применённой архитектуре, так называемой рекуррентной глубине. Это метод, при котором текст многократно прогоняется через одни и те же слои нейросет…
Россия может "катастрофически отстать" как страна, развивающая ИИ, если откажется от использования открытых ИИ-моделей — член правления "Яндекса" Тигран Худавердян, ТАСС
@markettwits
Дизайнер из Берлина подобрал узоры футболки, чтобы CNN ряда камер не могли распознать [носителя] как человека.
Правда, эффект оказался хрупким, и новая версия YOLO смогла его распознать, но уверенность нейросети упала с обычных для композиции на видео 85% до 25%, что говорит о том, что метод работает, и на местности с растительностью наверняка сильнее.
Это хороший аргумент для чайников, которые считают ерундой раскра…
Война в энергосетях
Ещё в 2015 году DARPA запустило программу RADICS. Её цель — обеспечить быстрое восстановление энергосистемы США после масштабной кибератаки, которая вывела бы из строя ключевые элементы управления. Программа сосредоточена на сценарии «чёрного старта» — запуске энергосистемы с нуля, когда она полностью обесточена.
В рамках программы разрабатывали технологии для:
-Быстрого обнаружения атак на эне…
Анекдот дня: британская разведка MI5 заявила, что исключает прием кандидатов из числа белого населения, брать на работу будут только представителей меньшинств.
Теперь британских шпионов распознать будет гораздо проще.
@geonrgru
Экология по-британски: нано-сенсоры, шпионаж и взлом мозга в одном флаконе
Британское агентство ARIA запустило новую программу под названием «Повсеместное профилирование частиц». Они хотят разработать дешёвые портативные сенсоры, чтобы следить за качеством воздуха и спасать климат. Но если вчитаться в техническое задание, картина меняется.
Своим документом британские исследователи подтвердили сразу несколько предпо…
-Академические исследования отмечают, что крупные финансовые кризисы (1929 и 2008 гг.) исторически следовали за масштабными военными конфликтами или предшествовали им.
-Существуют данные, что в 2008 году аналитические центры США рассматривали "войну как способ выхода из кризиса" вместо траты $700 млрд на спасение экономики.
🔒DARPA&CIA
Иран в очередной раз показывает пример того, как нужно вести себя с врагами своего народа. Пока коалиция Эпштейна вместе со своими семьями не будет на постоянной основе ездить в машинах для перевозки мусора, как это уже делал Трамп, опасаясь за своё жалкое существование, она не остановится.
🔒DARPA&CIA
Загадочный визит директора ЦРУ в Москву
Пока нет официальных заявлений, на Западе гадают, зачем директор ЦРУ прилетал в Москву на Boeing C-17. Наиболее распространённая версия, которую приводит The Wall Street Journal со ссылкой на бывшего заместителя директора Национальной разведки США Бет Саннер, — он приезжал якобы для того, чтобы предупредить.
Суть их предупреждения: американская разведка располагает данными о …
Генеративный ИИ и цифровой вирус сознания: патент Фреда Коэна и автоматизация когнитивных войн
Генеративный ИИ ускоряет разработки во многих областях. Но наука алгоритмического управления обществом интересует военных и разведку США ничуть не меньше, чем программирование.
В продолжение темы когнитивных войн разберём ещё один недавно опубликованный патент. Его автор — фигура легендарная. Это Фред Коэн, человек, котор…
30 секунд, которые меняют ваше мнение: эра когнитивного управления
На днях компания Veriphix, занимающаяся, по сути, когнитивными войнами, получила патент, который раскрывает механику их флагманской платформы Belief3.
Чтобы понять, о чём речь: с этой платформой они выиграли конкурс НАТО в 2021 году под названием «Противодействие когнитивной войне». Их создатель хвастался, что система уже использовалась в Польше, в …
🤔25🔥17💯7👏4❤3👍3😎2😱1
Showing the 12 most recent of 53 posts we hold for @darpaandcia. 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.
Posts edited after publishing
@darpaandcia edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
18 August 2026
Most recent edit
18 August 2026
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.
Mentions
Named by 53 registered channels — 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“Секреты DARPA и ЦРУ | DARPA&CIA” (@darpaandcia), 16,101 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/darpaandcia.
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.
