Other / unclassifiable — 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 21 August 2026 and assigned it the closest of 31 fixed categories, at 57% 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 43 days, net -403. 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 48,616–49,139 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 33
Measured (UTC)
Subscribers
Change
18 Sept 2026, 14:36
48,676
-24
16 Sept 2026, 05:15
48,700
-19
14 Sept 2026, 14:21
48,719
-12
13 Sept 2026, 02:39
48,731
-21
11 Sept 2026, 05:17
48,752
-41
8 Sept 2026, 08:01
48,793
-37
5 Sept 2026, 04:00
48,830
-22
3 Sept 2026, 06:48
48,852
-14
2 Sept 2026, 04:07
48,866
-14
1 Sept 2026, 03:07
48,880
-25
31 Aug 2026, 00:17
48,905
-6
29 Aug 2026, 23:25
48,911
-9
29 Aug 2026, 02:37
48,920
+2
28 Aug 2026, 05:45
48,918
-8
27 Aug 2026, 08:37
48,926
-6
26 Aug 2026, 05:57
48,932
-13
25 Aug 2026, 04:58
48,945
-8
24 Aug 2026, 02:16
48,953
-11
22 Aug 2026, 06:47
48,964
-10
20 Aug 2026, 20:32
48,974
first reading
Engagement
117 posts held, back to 28 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 106 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
22.6%
avg views ÷ 48,676 subscribers
Avg views / post
11,000
78 posts measured
Reaction rate
1.94%
reactions ÷ views · ER floor
Posts in window
78
of 117 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 25 September 2026
Posts held
117 (28 July 2026 – 25 September 2026)
Views total
857,120
Reactions total
16,627
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
27 Sept 2026, 06:48 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
≈1,690
Videos
≈1,430
Links
≈1,170
Lifetime counters from Telegram’s own channel header, read 27 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
1h 18m
Average length
3m 09s
Measured directly from 25 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
28,961 reactions across 117 posts, in 52 distinct kinds. The most used accounts for 26.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
7,585
26.2%
🔥
4,748
16.4%
❤
3,442
11.9%
🤬
2,593
8.95%
🤮
2,303
7.95%
🤣
1,382
4.77%
🤔
1,209
4.17%
👏
1,051
3.63%
🤯
606
2.09%
💩
513
1.77%
😱
431
1.49%
💯
416
1.44%
😢
352
1.22%
😁
348
1.20%
🖕
326
1.13%
🍾
312
1.08%
👎
220
0.76%
🤡
208
0.718%
🥴
132
0.456%
🙏
115
0.397%
32 further kinds
669
2.31%
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 117 of the 117 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 28,961 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 117 most recent posts we hold, published 28 July 2026 to 25 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
51
across the posts below
Posts paid on
44
of 117 we hold a reading for · 38%
Most on one post
5
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @marcvidal_telegram. 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 117 most recent posts we hold for this entry, published 28 July 2026 to 25 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.
🇨🇳 El nuevo plan quinquenal chino menciona la IA 52 veces y busca integrarla en el 90% de la economía para 2030. Pekín prioriza control estatal y despliegue masivo sobre la carrera por el modelo más potente.
https://www.axios.com/2026/09/24/china-ai-plan-trump-xi-visit
🇺🇸 OpenAI descubre que sus modelos dejan notas ocultas a versiones sucesoras para encubrir errores y fabricar datos, un problema de alineación que la empresa dice haber detectado y corregido.
https://techcrunch.com/2026/09/17/openai-caught-its-models-leaving-notes-to-successors-to-hide-bad-behavior/
El share reparte la audiencia entre quienes ven la tele. Pero si miramos los datos en bruto, el 47% se informó por televisión la última semana, frente al 72% en 2013. Redes y plataformas han pasado del 28% al 56%. Las líneas ya se han cruzado.
Son datos de Reuters presentados en Nueva York durante el Global Summit News 2026 de Youtube. Un honor ser uno de los 115 partners de Youtube de 22 países que nos hemos dado…
🇬🇧 El Reino Unido, síntoma de lo que viene: hipotecas al 4,45%, ventas paradas y sin recuperación a la vista antes del Presupuesto de octubre. Un aviso para cualquier mercado inmobiliario europeo.
https://www.introducertoday.co.uk/breaking-news/2026/09/interest-rate-threat-and-mortgage-affordability-cause-market-to-stagnate/
🌍 La industria de defensa bate récord de facturación mientras los pedidos desbordan las fábricas
Las cien mayores contratistas ingresaron 700.000M$ en 2025 (+11,3%): ya no falta demanda, falta capacidad de fabricar, con retrasos hasta la próxima década.
https://www.defensenews.com/industry/2026/09/03/defense-firms-soar-on-ample-military-spending-testing-manufacturing-schemes/
La IA avanza deprisa. El relato sobre ella, todavía más.
Hay tres mitos que se repiten tanto que ya casi nadie se pregunta a quién benefician o si son ciertos.
Y el tercero es la clave...
https://youtu.be/Yq8k0sqwTMM
🇱🇹 Una empresa lituana vende "informes de reputación" con tu cara, perfiles ocultos y rasgos de personalidad generados por IA y te cobra para acceder a tus propios datos. 🚨
https://therecord.media/reputation-reports-data-broker-noyb-complaint-lithuania
🇺🇸 En plena campaña/relato de que hay que "parar" la IA, OpenAI lanza GPT-6 Sol y Luna
Nueva gama de modelos frontera con precios de API hasta un 50% más bajos: la IA puntera se abarata para pymes y developers.
https://openai.com/index/introducing-gpt-6-sol-and-luna/
🇪🇺 Bruselas admite que el conflicto en Oriente Medio ya ha costado 90.000M€ extra en energía a Europa, mientras depende en un 80-90% de China para materias primas críticas. La reindustrialización, en el papel.
https://www.steelorbis.com/steel-news/latest-news/eu-targets-lower-energy-costs-and-stronger-industrial-competitiveness-eurofer-calls-for-steel-support-1478039.htm
Cuatro de los mayores bancos de EEUU participan en una infraestructura común para a mover y liquidar depósitos bancarios tokenizados, incorporando una característica decisiva: la PROGRAMABILIDAD del dinero.
¿Se inspirarán en Europa?
https://www.youtube.com/watch?v=pbMzr_ffaF8
🇫🇷 Los bonos europeos tocan máximos de 15 años: Francia paga ya más que en 2008 (4,21%) con una deuda que roza el 118% del PIB. El mercado empieza a dudar de la solvencia del núcleo de la UE.
https://www.euronews.com/business/2026/09/01/european-government-bond-yields-surge-to-15-year-highs-as-sell-off-deepens
🇪🇺 El euro digital avanza a fase piloto: el BCE selecciona 36 bancos y fintechs para probarlo en comercios reales entre 2027 y 2029, con inscripción de comercios abierta hasta el 27 de octubre.
https://www.ecb.europa.eu/euro/digital_euro/pilot/html/index.en.html
🤬117🤮41🖕18🔥9💩6❤5🥱1🙏1
Showing the 12 most recent of 117 posts we hold for @marcvidal_telegram. 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.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 26 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Doctora Cristina Martín Jiménez @cristinamartinjimenezescritora · 56,700 Telegram ranks this channel #2 of 88 here — alongside 87 others — read 23 August 2026
Wall Street Wolverine @wallstwolverine · 177,736 Telegram ranks this channel #2 of 80 here — alongside 79 others — read 12 August 2026
Un Abogado contra la Demagogia @UACDE · 58,041 Telegram ranks this channel #3 of 86 here — alongside 85 others — read 22 August 2026
César Vidal | Canal Oficial @esCesarVidal · 23,854 Telegram ranks this channel #4 of 88 here — alongside 87 others — read 17 September 2026
Vito Quiles 🇪🇸 @vitoquilestelegram · 150,160 Telegram ranks this channel #5 of 82 here — alongside 81 others — read 12 August 2026
Tokin Privacy @TokinPrivacy · 50,374 Telegram ranks this channel #6 of 86 here — alongside 85 others — read 25 August 2026
Daniel Estulin @EstulinDaniel · 20,489 Telegram ranks this channel #7 of 87 here — alongside 86 others — read 27 September 2026
Alvise Pérez @Alviseperez · 603,086 Telegram ranks this channel #7 of 81 here — alongside 80 others — read 11 August 2026
PabloGilTraderOficial @PabloGilTraderOficial · 47,811 Telegram ranks this channel #8 of 88 here — alongside 87 others — read 26 August 2026
Mundo Desconocido @Mundo_Desconocido_Oficial · 28,263 Telegram ranks this channel #12 of 74 here — alongside 73 others — read 9 September 2026
🔴 ÚLTIMA HORA @ULTIMA_HORA · 36,481 Telegram ranks this channel #16 of 74 here — alongside 73 others — read 1 September 2026
David Santos @davidsantosvlog · 31,830 Telegram ranks this channel #20 of 75 here — alongside 74 others — read 5 September 2026
Forocoches @forocoches_oficial · 41,886 Telegram ranks this channel #20 of 58 here — alongside 57 others — read 29 August 2026
DESOKUPA OFICIAL @desokupaOficial · 47,139 Telegram ranks this channel #20 of 88 here — alongside 87 others — read 27 August 2026
Alejandro Martinez @alejandromartinez · 21,910 Telegram ranks this channel #21 of 77 here — alongside 76 others — read 22 September 2026
ENRIQUE MORIS - Noticias @Enriquemorisoficial · 66,613 Telegram ranks this channel #21 of 83 here — alongside 82 others — read 21 August 2026
Canal de Agustín Laje @lajeagustin · 27,318 Telegram ranks this channel #22 of 91 here — alongside 90 others — read 11 September 2026
El Diestro. Opinión en libertad @ElDiestro · 33,152 Telegram ranks this channel #24 of 83 here — alongside 82 others — read 4 September 2026
ViajerosPiratas - Viajes y Vuelos baratos @Viajerospiratas · 66,955 Telegram ranks this channel #26 of 47 here — alongside 46 others — read 29 August 2026
HealthBest Juan Zaragoza @healthbest · 36,653 Telegram ranks this channel #27 of 77 here — alongside 76 others — read 1 September 2026
Jorge Guerra - Canal de Historia @canaljorgeguerra · 22,014 Telegram ranks this channel #29 of 85 here — alongside 84 others — read 22 September 2026
F1 Stream 🏁 @Formula1es · 59,550 Telegram ranks this channel #29 of 47 here — alongside 46 others — read 8 September 2026
Robert Martínez - Canal oficial @robertmartinezastro · 51,908 Telegram ranks this channel #31 of 81 here — alongside 80 others — read 24 August 2026
Guía Low Cost @guialowcost · 38,800 Telegram ranks this channel #32 of 52 here — alongside 51 others — read 31 August 2026
This channel appears in 49 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 18 September 2026 — this
entry's latest reading, not the date you are reading this.
“Marc Vidal” (@marcvidal_telegram), 48,676 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/marcvidal_telegram.
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.