Telegram RegisterThe public register of Telegram

Channel

Онто.

@ontonet

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

315subscribers

+0 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-1001815633163
TypeChannel
Username@ontonet
DescriptionОнто - платформа для моделирования и анализа сложных систем. Сайт: https://ontonet.ru/ Платформа: https://app.ontonet.ru/ Документация: https://ontonet.ru/info Учебный центр: https://ontonet.ru/learning Сообщество: https://t.me/+utYGnhBi0JQ2Mjcy
CreatedBetween 1 October 2022 and 30 September 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live7 August 2026
Measurements held2
On Telegramt.me/ontonet

Growth

3156 Aug 2026, 20:04 — 315 subscribers6 Aug 2026, 22:17 — 315 subscribers6 Aug 2026, 20:046 Aug 2026, 22:17
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 314–316 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
6 Aug 2026, 22:17315no change
6 Aug 2026, 20:04315first reading

Engagement

18 posts held, back to 28 June 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
51.3%
avg views ÷ 315 subscribers
Avg views / post
162
11 posts measured
Reaction rate
1.67%
reactions ÷ views · ER floor
Posts in window
11
of 18 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. It is computed over the 10 of 11 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 4 August 2026
Posts held18 (28 June 20264 August 2026)
Views total1,778
Reactions total27
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Aug 2026, 22:17 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
189
Videos
97
Links
162

Lifetime counters from Telegram’s own channel header, read 6 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.

Reaction mix

47 reactions across 15 posts, in 10 distinct kinds. The most used accounts for 27.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍1327.7%
🔥1327.7%
1123.4%
💯48.51%
👎12.13%
🙏12.13%
🤔12.13%
🤣12.13%
🤨12.13%
🥰12.13%

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 15 of the 18 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 47reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 18 most recent posts we hold, published 28 June 2026 to 4 August 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

4 Aug 2026, 06:34 UTC80 views1 reactionsread 6 August 2026
Photo

Большой промпт может однажды превратить универсальную языковую модель в убедительного бизнес-ассистента. Но в следующей сессии всё приходится собирать заново: объяснять устройство компании, перечислять ограничения и надеяться, что роль будет интерпретирована так же, как в прошлый раз. В предыдущей статье я писал о том, почему ассистенту полезно дать внешнюю модель бизнеса. Теперь перехожу к практике: как на основе э

🔥1

3 Aug 2026, 06:54 UTC91 views2 reactionsread 6 August 2026
Photo

Большинство бизнес-ассистентов начинают работу с восстановления контекста: читают документы, выделяют сущности и заново выясняют, как связаны проекты, договоры, люди и решения. Даже если все данные переданы, при следующем запуске эту картину приходится собирать заново — и она может немного отличаться от предыдущей. В Онто мы проверили другую конструкцию. Ассистента можно один раз учредить как постоянную роль и помес

🔥2

30 Jul 2026, 09:11 UTC90 views2 reactionsread 6 August 2026
Forwarded from @pochtibereza

Я снова программирую! Или нет?! Сразу и не разберешь. Скорее наблюдаю, как программирует команда ИИ-агентов. ИИ агенты — уже не экзотика: они пишут код, анализируют рынок, готовят материалы и помогают управлять продуктом. Но довольно быстро возникает следующий вопрос: как превратить несколько сильных помощников из разных чатов в устойчивую команду, которая не теряет контекст между запусками и умеет работать вместе

2

29 Jul 2026, 12:21 UTC270 views3 reactionsread 6 August 2026
Photo

Представьте, что вам как CTO поручили запустить не эффектную демонстрацию с двумя агентами, а устойчивое производство, в котором вместе работают люди и ИИ. Без Онто вам пришлось бы собрать настоящий зоопарк: реестр агентов и ролей, базу знаний и RAG, граф зависимостей, хранилище памяти, движок процессов, систему прав, журнал решений, средства аудита, оркестратор и отдельный слой для передачи работы между людьми и аге

🔥2👍1

28 Jul 2026, 09:38 UTC119 views1 reactionsread 6 August 2026
Photo

Если перестать рассчитывать, что опытный руководитель «на месте разберётся», что останется от процесса? Работа с агентами превращает этот вопрос из методологического в практический. Агент не компенсирует плохую инструкцию знанием компании, не догадывается об устных договорённостях и не восстанавливает сам границы ответственности. Чтобы он мог действовать автономно, приходится явно описывать цель, контекст решений, к

🔥1

24 Jul 2026, 09:32 UTC157 views5 reactionsread 6 August 2026
Photo

Как выглядит механизм онбординга нового исполнителя который уже опубликован в общем пространстве

2👍1🤣1🤨1

23 Jul 2026, 08:44 UTC166 viewsread 6 August 2026

Ну вот и место Онто определилось ))

22 Jul 2026, 08:51 UTC306 views3 reactionsread 6 August 2026
Photo

Вышла вторая часть моего эксперимента с отделяемыми агентами. Один универсальный агент удобен ровно до тех пор, пока не начинает сам формулировать задачу, выполнять её и принимать собственный результат. Поэтому вместо очередного большого промпта я поселил в пространстве «Платформа Онто» первое ядро из восьми специалистов: регистратора дефектов, аналитика, оркестратора, разработчиков, двух разных QA и хранителя правил

1👍1👎1

20 Jul 2026, 06:41 UTC164 views3 reactionsread 6 August 2026
Photo

В новой статье я описал появившийся в Онто механизм отделяемых микроагентов. Обычно агент остаётся частью конкретной сессии или внешнего сценария, поэтому после завершения работы его специализация недоступна остальным. В Онто такого агента можно отделить от исходного запуска и поселить в пространстве как самостоятельного резидента. Так в пространстве может появиться регистратор дефектов или диагност конкретного узла

1👍1🙏1

16 Jul 2026, 11:26 UTC159 views2 reactionsread 6 August 2026

Важное изменение в развитии AI-возможностей Онто. Мы постепенно отказываемся от OntoAIGPT и встроенных AI-возможностей в чате объекта. Это не отказ от работы с ИИ — наоборот, мы переносим её в более универсальный и управляемый контур через Onto MCP. Встроенный чат был полезен как первый способ познакомить пользователей с работой AI внутри модели. Но сегодня у большинства уже есть собственные агенты в Claude, ChatGP

👍2

11 Jul 2026, 16:35 UTC227 viewsread 6 August 2026
Photo

Bootstrap-as-Memory: когда объект получает право возражать Большинство агентных систем строится вокруг функций. Появляются агенты склада, закупок, диагностики, планирования, разработки и контроля качества. Каждый из них умеет выполнять определённый класс операций над множеством объектов. Но в такой архитектуре обычно отсутствует тот, кто представляет интересы одного конкретного объекта. И вот как агентская память

Showing the 12 most recent of 18 posts we hold for @ontonet. 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 — 162,924 of 1,169,250entries 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.

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.

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

“Онто.” (@ontonet), 315 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/ontonet.

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.