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@machinelearningall

On this record: Topic · Observations · Channels linked by matching posts · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Handles named that no longer answer · Cite this entry

50,946subscribers

+10,785 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001852705142
TypeChannel
Username@machinelearningall
DescriptionБот для генераций t.me/veoseebot Сайт для Генераций Neuroplace.ai Тех поддержка @neuroplace_support Чат сообщества t.me/Neiroprof Админ @tbzrb
CreatedBetween 1 October 2022 and 30 September 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live10 November 2026
Measurements held64
Confirmed unchanged1 time, most recently 10 November 2026
On Telegramt.me/machinelearningall

Topic

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 9 September 2026 and assigned it the closest of 31 fixed categories, at 95% 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 the other topics.

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.

Content that also appears on other registered channels

Posts published here appear in near-verbatim form on 1 other registered channel. This entry also forms a group of 2 channels linked by matching posts. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (5 of the pairs behind the counts below)
Posted firstThenOverlapGap
@machinelearningall/6626 · this entry13 Aug 2026, 16:00 UTC@premium_promts/64115 Aug 2026, 08:34 UTC1.0041 hours
@machinelearningall/7523 · this entry15 Oct 2026, 15:07 UTC@premium_promts/69515 Oct 2026, 19:07 UTC1.004.0 hours
@machinelearningall/7594 · this entry21 Oct 2026, 09:45 UTC@premium_promts/70621 Oct 2026, 10:02 UTC1.0016 minutes
@machinelearningall/7586 · this entry20 Oct 2026, 08:16 UTC@premium_promts/70922 Oct 2026, 16:39 UTC1.002 days
@machinelearningall/7636 · this entry23 Oct 2026, 10:56 UTC@premium_promts/71023 Oct 2026, 10:58 UTC1.002 minutes
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
ChannelMatching postsText overlapTypical gapPublished first
@premium_promts15 (8/8 hand-verifiable sample passed)1.006.5 hoursthis entry (15–0)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 0 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.

“Published first” means first in this corpus. We hold 769 comparable posts for this entry, running 6 August 2026 to 10 November 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Of the group of 2 channels, the one whose matching posts we hold earliest is @machinelearningall — which is this entry. That is a statement about our reading window, not a claim of authorship.

Recorded under the key clone_source, last confirmed 11 November 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.

Channels linked by matching posts

This channel is in a group of 2 registered channels linked by matching posts. Links are found by comparing text fingerprints across every channel on the register.

Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim 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.

Growth

40,16152,14546,15350,9467 August 202610 November 2026
64 measurements spanning 95 days, net +10,785. 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 38,363–53,943 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 64
Measured (UTC)SubscribersChange
10 Nov 2026, 14:4450,946-97
9 Nov 2026, 02:2951,043-57
7 Nov 2026, 22:3951,100-53
6 Nov 2026, 23:3451,153-40
5 Nov 2026, 23:1051,193-17
4 Nov 2026, 20:2151,210-58
3 Nov 2026, 18:0751,268-81
2 Nov 2026, 15:3651,349-100
1 Nov 2026, 15:3051,449-65
31 Oct 2026, 16:1851,514-54
30 Oct 2026, 17:2851,568-74
29 Oct 2026, 17:4651,642-76
28 Oct 2026, 18:4951,718-36
27 Oct 2026, 18:4951,754-55
26 Oct 2026, 19:2951,809-41
25 Oct 2026, 18:1751,850-58
24 Oct 2026, 19:1851,908-66
23 Oct 2026, 19:2651,974-67
22 Oct 2026, 21:2852,041-61
21 Oct 2026, 21:0152,102first reading

Engagement

796 posts held, back to 6 August 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 187 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
3.67%
avg views ÷ 50,946 subscribers
Avg views / post
1,870
242 posts measured
Reaction rate
0.541%
reactions ÷ views · ER floor
Posts in window
245
of 796 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
WindowRolling 30 days · latest post in window 10 November 2026
Posts held796 (6 August 2026 – 10 November 2026)
Views total451,919
Reactions total2,447
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Nov 2026, 07:42 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
≈5,400
Videos
≈1,760
Links
≈3,180

Lifetime counters from Telegram’s own channel header, read 11 November 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
2h 17m
Average length
16s

Measured directly from 499 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

10,060 reactions across 788 posts, in 20 distinct kinds. The most used accounts for 42.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥4,22742.0%
❤4,19341.7%
👍7097.05%
😁2672.65%
🤣2472.46%
👏770.765%
🥰630.626%
🎉410.408%
😍380.378%
❤‍🔥360.358%
🤔330.328%
👌310.308%
🥱260.258%
🥴210.209%
🌚190.189%
💯180.179%
🙏60.06%
🌭30.03%
🕊30.03%
🐳20.02%

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 788 of the 796 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 10,060 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 796 most recent posts we hold, published 6 August 2026 to 10 November 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
118
across the posts below
Posts paid on
17
of 788 we hold a reading for · 2%
Most on one post
100
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @machinelearningall. 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 796 most recent posts we hold for this entry, published 6 August 2026 to 10 November 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.

Recent posts

Telegram’s own ‘edited’ marker: 35 of 245 posts held from the last 30 days (14.3%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 15:51 UTC≈1,460 views14 reactionsread 11 November 2026
Video

🔤🔤🔤🔤🔤🔤🔤🔤 🔤🔤🔤🔤🔤 ♐️ ✨ Энергичные, свободолюбивые и всегда в центре внимания! Если вы родились под знаком Стрельца, самое время заявить о себе в новом тренде🔥 Показываем, как повторить такое видео👇🏻 ⬇️⬇️⬇️ Заходим в VeoSeeBot → раздел «Идеи» → выбираете шаблон ♐ СТРЕЛЬЦЫ, ВАШ ВЫХОД! 🔥 → загружаете туда свое фото в хорошем качестве (как фото на паспорт) 🚨ПОВТОРИТЬ ТРЕНД🚨 ❤️ Наберём 20 лайков — и опубликуем промп…

❤12🔥1🥱1

10 Nov 2026, 11:24 UTC≈1,650 views6 reactionsread 11 November 2026
Video

🔤🔤🔤 🔤🔤🔤🔤🔤 🔥 Не успели отойти от тренда «МОИ СЛЕЗЫ», как на подходе новый тренд на миллионы просмотров! Самое время повторить этот тренд со своими персонажами! Показываем, как повторить такое видео👇🏻 ⬇️⬇️⬇️ Заходим в VeoSeeBot → раздел «Идеи» → выбираете шаблон ТРЕНД У ЗЕРКАЛА 🔥 → загружаете туда свои карточки персонажей и ваше видео готово ✅ 🚨ПОВТОРИТЬ ТРЕНД🚨 ⬆️ Жми «Повторить тренд», чтобы создать такое тре

🔥5❤1

10 Nov 2026, 09:33 UTC≈1,600 views8 reactionsread 11 November 2026
Photo

🔤🔤🔤 🔤🔤🔤🔤🔤🔤 Показываем, как повторить 👇🏻 1️⃣ Заходим в VeoSeeBot 2️⃣ Переходим в раздел “AI Картинка”➡️Nano Banana 2.1 ❗️Загружаете свои фотографии в хорошем качестве и вставляем этот промпт: ПРОМПТ В КОММЕНТАРИЯХ ⬇️⬇️⬇️ 🔥Оставьте реакцию, если было интересно :)

❤5❤‍🔥1👍1🔥1

10 Nov 2026, 08:18 UTC≈1,710 views8 reactionsread 11 November 2026
Video

🔤🔤🔤🔤🔤🔤🔤🔤 🔤🔤🔤🔤🔤 🎂 🔥 ДЕНЬ РОЖДЕНИЯ ДЛЯ НАСТОЯЩИХ МУЖЧИН! Никакой скромности — только мужской характер, уверенность, стиль и размах! Показываем, как повторить такое видео👇🏻 ⬇️⬇️⬇️ Заходим в VeoSeeBot → раздел «Идеи» → выбираете шаблон 🔥 ДЕНЬ РОЖДЕНИЯ ПО-МУЖСКИ → загружаете туда свое фото в хорошем качестве (как фото на паспорт) или как карточку персонажа 🚨ПОВТОРИТЬ ТРЕНД🚨 ⬆️ Жми «Повторить тренд», чтобы создат…

🔥5❤2🥱1

10 Nov 2026, 06:59 UTC≈1,780 views24 reactionsread 11 November 2026
Photo

🔤🔤🔤 🔤🔤🔤🔤🔤🔤 В ОБЪЯТИЯХ МАКОВ 🌺 Нежная эстетика, гигантские цветы, тёплое золотистое сияние и хрупкая бабочка создают невероятную атмосферу сказочного цветочного мира. 🦋 ✨ Как повторить такой кадр? ✅ Достаточно загрузить своё фото в хорошем качестве (желательно как на паспорт) 🚨 ПОВТОРИТЬ ФОТО🚨 Давайте наберем 20 лайков ❤️ - и мы опубликуем промпт в комментариях!

❤13👍8🔥3

9 Nov 2026, 20:39 UTC≈2,140 views54 reactionsread 11 November 2026
Video

🔤🔤🔤 🔤🔤🔤🔤🔤 Давайте 70🔥 и мы опубликуем туториал и промпт на этот тренд ⬇️⬇️⬇️

🔥52❤2

9 Nov 2026, 17:19 UTC≈2,280 views12 reactionsread 11 November 2026
Photo

🔤🔤🔤 🔤🔤🔤🔤🔤🔤 🔥 Создаём эффектный рекламный ролик вашего товара по сториборду! Для создания используем мощную связку GPT Image 2.5 + Kling 4.0! ⬇️⬇️⬇️ ШАГ №1 1️⃣ Заходим в VeoSeeBot 2️⃣ Открываем раздел “AI Картинка” ➡️и выбираем там GPT Image 2.5 ✅ Загружаем фото нашего продукта и вставляем такой промпт: Сделай рекламный сториборд загруженного товара, покадрово для анимации рекламы. сетка 2×2, 4 последовательны…

❤8👍2😍2

9 Nov 2026, 15:32 UTC≈2,200 views7 reactionsread 11 November 2026
Photo

🔤🔤🔤🔤🔤🔤🔤🔤 🔤🔤🔤🔤🔤🔤 🎂 Показываем, как повторить 👇🏻 1️⃣ Заходим в VeoSeeBot 2️⃣ Переходим в раздел “AI Картинка”➡️Nano Banana 2 ❗️Загружаете свое фото в хорошем качестве и вставляем этот промпт: ПРОМПТЫ В КОММЕНТАРИЯХ ⬇️⬇️⬇️ 🔥Оставьте реакцию, если было интересно :)

🔥6❤1

9 Nov 2026, 13:01 UTC≈2,260 views36 reactionsread 11 November 2026
File

🔤🔤🔤 VeoSeeBot + Claude✔️ Уважаемые друзья, рады сообщить вам что дропнули MCP VeoSeeBot И теперь вы можете подключится напрямую с Claude и генерировать видео прям там используя VeoSeeBot⚡️ Прилагаем инструкцию для подключения. С уважением команда VeoSeeBot 🔥

🔥18❤5👍5🤣4🥴3🥱1

9 Nov 2026, 11:06 UTC≈2,290 views4 reactionsread 11 November 2026
Video

🔤🔤🔤 🔤🔤🔤🔤🔤 🧸 Милейший тренд этой осени — с вашим ребёнком! 🥹❤️ Повторите этот очаровательный ролик со своим маленьким героем! Показываем, как повторить такое видео👇🏻 ⬇️⬇️⬇️ Заходим в VeoSeeBot → раздел «Идеи» → выбираете шаблон 🧸 МАЛЕНЬКАЯ ЗВЕЗДА → загружаете туда карточку персонажа своего ребенка ✅ СОЗДАТЬ КАРТОЧКУ ПЕРСОНАЖА МОЖНО ➡️ ТУТ 🚨ПОВТОРИТЬ ТРЕНД🚨 ⬆️ Жми «Повторить тренд», чтобы создать такое трендо

❤4

9 Nov 2026, 09:33 UTC≈2,170 views23 reactionsread 11 November 2026
Video

🔤🔤🔤 🔤🔤🔤🔤🔤 Из пепла — в огонь, из разрушения — к новой жизни! 🔥 Эффектное превращение, в котором ваш персонаж проходит путь от отчаяния до величественного возрождения в образе огненного феникса. Показываем, как повторить такое видео👇🏻 ⬇️⬇️⬇️ Заходим в VeoSeeBot → раздел «Идеи» → выбираете шаблон 🔥 ФЕНИКС: ВОЗРОЖДЕНИЕ → загружаете туда свое фото в хорошем качестве (как фото на паспорт) 🚨ПОВТОРИТЬ ТРЕНД🚨 ❤️ Наб…

❤17🔥6

Showing the 12 most recent of 796 posts we hold for @machinelearningall. 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

@machinelearningall edited 11 posts 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
17 August 2026
Most recent edit
9 November 2026

Posts recorded as deleted after publishing

We recorded 1 post on @machinelearningall as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
17 August 2026
Most recent seen missing
17 August 2026
Median time before seen missing
22 hours
Seen missing in the last 30 days
0

Forward network

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 4 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.

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 10 November 2026 — this entry's latest reading, not the date you are reading this.

“VeoSeeBot | Промпты ИИ” (@machinelearningall), 50,946 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/machinelearningall.

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.