Politics & activism — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 11 August 2026 and assigned it the closest of 31 fixed categories, at 48% 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.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Views per post sit far above this size band
5,370 average views per post against 2,242 subscribers — an engagement rate of 239.3%. Across the 54,164 registered channels in the same cohort — 1,000–3,162 subscribers, posting mainly in Russian — the middle half sit between 10.5% and 35.6%, with a median of 20.1%.
What this was computed from
Window
30 days (13 July 2026 – 12 August 2026)
Posts measured
16 of 16 published in the window (1 exact, 15 rounded by Telegram)
Views totalled
85,843
Mature posts only
239.3% over 16 posts read at least 24h after publication
When this is recorded. A channel is listed here only when its engagement rate sits at or above the 99th percentile of its cohort and is at least 3× away from that cohort’s median — above it — on both the all-readings figure and the mature-only figure. The percentile alone would be circular: a percentile cut puts the same share of every cohort in the tail whatever the data looks like. The distance from the median is what makes it a statement about this channel.
This is not a verdict, and the direction is not a quality signal.A low rate has many innocent causes — audiences that read in the Telegram app without opening the channel, a subscriber base built long before the current output, an audience in a different timezone from our reading. A high rate has innocent causes too: a post that travelled far beyond the channel’s own subscribers will do it. We publish the measurement and the distribution it sits in. The full cohort baselines are downloadable, so this comparison can be reproduced rather than trusted.
Recorded under the key err_high, last confirmed 12 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
3 measurements spanning 4 days, net -995. 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 2,093–3,386 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
11 Aug 2026, 07:03
2,242
-990
7 Aug 2026, 22:35
3,232
-5
7 Aug 2026, 15:17
3,237
first reading
Engagement
16 posts held, back to 21 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 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
239.3%
avg views ÷ 2,242 subscribers
Avg views / post
5,370
16 posts measured
Reaction rate
1.06%
reactions ÷ views · ER floor
Posts in window
16
of 16 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 6 August 2026
Posts held
16 (21 July 2026 – 6 August 2026)
Views total
85,843
Reactions total
911
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 23:40 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
7m 53s
Average length
47s
Measured directly from 10 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
911 reactions across 15 posts, in 6 distinct kinds. The most used accounts for 23.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
217
23.8%
🎉
183
20.1%
❤
172
18.9%
🔥
170
18.7%
😁
168
18.4%
🤝
1
0.11%
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 16 of the 16 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 911reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 21 July 2026 to 6 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.
2,5 тысячи волонтёров из 72 регионов — гуманитарные миссии Народного фронта продолжаются с активным участием регионов ЮФО!
Уже более трех лет волонтёры работают в госпиталях и на складах, ликвидируют последствия обстрелов и оказывают адресную помощь. За это время в гуманитарных миссиях побывали около 2500 человек из 72 регионов России.
Активное участие в этой работе принимают и команды из Южного федерального округа…
Два года на передовой не прошли даром — Найда встретила Народный фронт на позиции ПВО и сразу показала характер.
Когда представители Народного фронта приехали с очередной гуманитарной помощью на позиции ПВО в Севастополе, первой их встретила Найда. Сразу было видно — собака серьёзная. Она не лаяла просто так, а встала поперёк проезда и не пускала машину, пока не убедилась, что свои.
Бойцы рассказали: первые два год…
70 бисерных икон: тепло рук и вера севастопольских женщин.
Живая, деятельная, неутомимая. Жительница Севастополя, Татьяна Сохань заряжает энергией всех вокруг. За плечами больше 15 лет борьбы с онкологией, но это лишь фон для её яркого характера. Она превращает каждый день в возможность помочь ближнему.
Вместе с сёстрами Татьяна помогает севастопольским бойцам: собирает гуманитарную помощь, вяжет носки, но главное …
Студенты и молодёжь из южных регионов России ездят в десятки гуманитарных миссий с Народным фронтом.
В регионах Южного федерального округа продолжают масштабировать лучшие молодежные практики. Проекты, которые доказали свою эффективность в одном регионе, получают развитие у соседей, помогая вовлекать в полезные инициативы все больше молодых людей.
Сегодня команды Молодёжки Народного фронта из регионов ЮФО вместе уч…
Видео с передовой: как 810-я бригада морской пехоты работает на курском направлении.
Бойцы 810-й бригады морской пехоты прислали нам эксклюзивное видео с курского направления. На этих кадрах хорошо видно, как наши воины наносят удары по вражеским позициям и технике. Это реальные кадры боевой работы, которые наглядно демонстрируют, что происходит на передовой.
Морпехи специально поделились этим видео, чтобы мы видел…
✅ Севастопольские волонтеры создали проект по социализации и реабилитации детей из новых регионов России.
Он называется "Детская комната знаний "Снегирь".
Об этом в пресс-центре РИА Новости Крым в рамках видеомоста "Симферополь - Москва - Краснодар - Севастополь - Волгоград - Ростов - Астрахань" сообщил руководитель регионального отделения Народного фронта в Севастополе Антон Ломберг.
"В рамках проекта проводят за…
Волонтеры активно участвовали в спасении полотна Панорамы "Оборона Севастополя" после пожара.
Об этом в пресс-центре РИА Новости Крым в рамках видеомоста "Симферополь - Москва - Краснодар - Севастополь - Волгоград - Ростов - Астрахань" рассказала координатор Молодежки Народного фронта в городе-герое Марина Ивахинова.
"Мы с первых дней подключились и подготовили полотно к химчистке и дальнейшему восстановлению".
10…
📕 Как молодёжные проекты объединили тысячи студентов Краснодарского края, Крыма и Севастополя!
Сначала — гуманитарные миссии, затем занятия по первой помощи, выезды после чрезвычайных ситуаций, обучение управлению БПЛА и новые добровольческие проекты. Именно так сегодня проходит работа многих молодых людей в Крыму, Севастополе и Краснодарском крае.
Одно из самых крупных направлений — обучение первой помощи и такти…
Инженерные решения на передовой: севастопольский боец создаёт дефицитные детали для БПЛА своими руками.
На передовой важны не только беспилотники, но и возможность быстро ремонтировать технику и находить решения там, где нужных деталей просто нет. Благодаря оборудованию для лаборатории БПЛА, переданному Народным фронтом, бойцы севастопольского зенитного ракетного полка ремонтируют дроны и разрабатывают необходимые к…
Ещё два автомобиля помогут севастопольским бойцам выполнять задачи на херсонском направлении.
На передовой легковой автомобиль — это не просто транспорт. Это возможность быстрее доставить оборудование и гуманитарный груз, оперативно добраться до позиции, вывезти раненого или решить десятки ежедневных задач, от которых зависит безопасность бойцов. Именно поэтому каждая исправная машина сегодня действительно нужна на …
Как звучит и выглядит флот? Народный фронт и нейросети поздравляют военных моряков.
В День Военно-Морского Флота Народный фронт решил соединить вечные морские традиции с языком современных технологий. Все иллюстрации, которые вы видите, сгенерированы искусственным интеллектом, а звуки создают атмосферу полного погружения.
С праздником, моряки, ветераны и курсанты! Крепкого здоровья, верных друзей, спокойной вахты и…
Что желают курсантам севастопольцы и гости города в День ВМФ.
Севастополь — колыбель черноморских моряков, и для нас День ВМФ является одним из главных праздников. Народный фронт обратился к жителям и гостям города-героя с просьбой поздравить будущих защитников морских рубежей. Их пожелания сошлись в главном: чтобы за спиной всегда был надёжный тыл, а в сердце — вера в свои силы и счастливое будущее.
С Днём ВМФ!
П…
🎉14🔥12❤11👍10😁9
Showing the 12 most recent of 16 posts we hold for @lomaonf. 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 11 August 2026 — this
entry's latest reading, not the date you are reading this.
“Антон Ломберг ОНФ Севастополь” (@lomaonf), 2,242 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/lomaonf.
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.
