Education — 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 19 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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
8 measurements spanning 60 days, net -18. 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 972–996 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
6 Oct 2026, 16:35
975
-6
26 Sept 2026, 08:41
981
-2
14 Sept 2026, 03:42
983
-1
5 Sept 2026, 14:18
984
-2
21 Aug 2026, 14:06
986
-3
15 Aug 2026, 03:13
989
-4
8 Aug 2026, 00:34
993
no change
7 Aug 2026, 20:32
993
first reading
Engagement
18 posts held, back to 15 April 2025 — 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 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 18 posts for this entry, the most recent from 2 October 2025. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
4m 17s
Average length
1m 04s
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
250 reactions across 15 posts, in 10 distinct kinds. The most used accounts for 40.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
100
40.0%
👍
62
24.8%
👏
43
17.2%
🔥
17
6.80%
🙏
13
5.20%
⚡
8
3.20%
🫡
3
1.20%
🥰
2
0.8%
❤🔥
1
0.4%
💯
1
0.4%
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 250 reactions 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 15 April 2025 to 2 October 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🚨 Galera, acabei de soltar um vídeo novo no canal!
Dessa vez eu trouxe uma questão da FCC (TRT-2/SP – Agente de Polícia Judicial) sobre Taxa Falsa de Rejeição (FRR) – um tema que parece simples, mas que derruba MUITA gente na prova.
👉 No vídeo eu explico de forma clara, com exemplos práticos, como esse conceito funciona e como a banca pode te enganar na hora da questão.
Se você quer ficar um passo à frente e não c…
Vou colcoar Iniciei um projeto aqui no canal onde vou comentar questões de concursos anteriores, focando nos temas mais relevantes da área de segurança institucional e polícia judicial.
E pra começar com tudo, no primeiro vídeo eu analiso uma questão da FCC cobrada no concurso do TRT1 (2025).
💡 O vídeo está bem explicativo! Falei sobre:
- A função do Mosca (ou ASP B);
- As formações na escolta a pé;
- Conceitos com…
🚨 Comandantes, atenção à missão:
Meu canal no YouTube está no ar!
🎥 Lá vou trazer conteúdos essenciais sobre Segurança Pública Institucional (SPI) — temas técnicos, concursos, legislação e muito mais.
🔗 Acesse agora, inscreva-se e ative o sininho: https://www.youtube.com/watch?v=BNpjhTvi7EI&t=113s
Conto com vocês nessa jornada!
Bons ventos e mares tranquilos.
🎯 Ninguém passa em concurso só nos dias bons.
Você vai vencer quando estudar cansado.
Quando sentar sem vontade.
Quando transformar a disciplina em rotina.
Foi por isso que criamos a HBC:
Um espaço pra quem leva aprovação a sério.
💺 Aqui é bunda na cadeira.
📈 Aqui é hora virando resultado.
Se você tá pronto, a vaga é sua.
https://www.instagram.com/reel/DKfto7COgbM/?igsh=MWplb2h6ZXV3dHdoMQ==
🚨 VARREDURA: você sabe realmente o que é e como ela se diferencia de uma vistoria ou busca? 🤔
Se você está se preparando para o concurso de Policial Institucional do MPU, esse é um conceito essencial que cai no edital e pode fazer a diferença na sua aprovação! 💡
📘 Preparei um e-book gratuito explicando tudo de forma simples e direta, pra você dominar o assunto e sair na frente!
Pessoal, aproveitem para tirar dúvidas na live de agora
👍2⚡1❤1
Showing the 12 most recent of 18 posts we hold for @canaldoprofessorfernandomarinha. 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.
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 October 2026 — this
entry's latest reading, not the date you are reading this.
“Professor Fernando Marinha” (@canaldoprofessorfernandomarinha), 975 subscribers as measured 6 October 2026. Telegram Register, tgregister.com/channel/canaldoprofessorfernandomarinha.
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.