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Channel

DH Academy AI

@ai_praktik_hub

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

151subscribers

+0 since we began measuring on 3 November 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1003765934738
TypeChannel
Username@ai_praktik_hub
CreatedBetween 1 February 2026 and 31 July 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded3 November 2026
Last confirmed live3 November 2026
Measurements held2
Confirmed unchanged1 time, most recently 3 November 2026
On Telegramt.me/ai_praktik_hub

Growth

1513 Nov 2026, 11:22 — 151 subscribers3 Nov 2026, 14:56 — 151 subscribers3 Nov 2026, 11:223 Nov 2026, 14:56
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 150–152 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
3 Nov 2026, 14:56151no change
3 Nov 2026, 11:22151first reading

Engagement

20 posts held, back to 21 September 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 1 page of Telegram’s post history, 20 posts per page.

ERR · 30 days
41.7%
avg views ÷ 151 subscribers
Avg views / post
62.9
10 posts measured
Reaction rate
2.57%
reactions ÷ views · ER floor
Posts in window
13
of 20 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 4 of 10 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 22 October 2026
Posts held20 (21 September 2026 – 22 October 2026)
Views total629
Reactions total7
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Nov 2026, 11:22 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
3m 57s
Average length
30s

Measured directly from 8 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 reactions across 6 posts, in 5 distinct kinds. The most used accounts for 40.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥440.0%
❤220.0%
👍220.0%
😁110.0%
🤣110.0%

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 7 of the 20 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 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 21 September 2026 to 22 October 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

Telegram’s own ‘edited’ marker: 0 of 13 posts held from the last 30 days (0%) 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).

22 Oct 2026, 12:17 UTC40 views1 reactionsread 3 November 2026
Video

Самолёт падает. Боб умеет им управлять. Но есть одна маленькая проблема… 🤖 Кто ещё кому помощник? Вымышленная сцена, созданная с помощью AI. #нейросети #ии #aiпрактик #юмор

😁1

21 Oct 2026, 20:56 UTC45 viewsread 3 November 2026
Video

Даже 300 лет опыта не спасли от нейросетей 👻🤖 Боб встретил призрака — и назвал его устаревшей моделью. Кажется, конкуренция добралась даже до потустороннего мира 😅 Как думаешь, какую профессию ИИ точно не заменит? AI-комедия. Создано с помощью нейросетей, сюжет вымышленный.

20 Oct 2026, 22:25 UTC43 viewsread 3 November 2026
Video

Он может спасти тебя ценой себя. А может — пойти искать нового хозяина. Как думаешь, что AI выберет на самом деле? «Выбор» — вымышленная история, созданная с помощью AI. #нейросети #искусственныйинтеллект

19 Oct 2026, 22:24 UTC57 viewsread 3 November 2026
Video

Париж через умные очки 🤖 Попросил робота всё исправить. Он решил, что достаточно поменять картинку. Сделать реальность красивее на экране — не значит решить настоящую проблему. AI-комедия, созданная с помощью нейросетей. Сюжет вымышленный, это не хроника реальных событий.

18 Oct 2026, 01:03 UTC61 views1 reactionsread 3 November 2026
Video

👻 Убрать призрака с фотографии? Для ИИ — секундное дело. А вот куда он денется после удаления — это, оказывается, стоило прописать в запросе 😅 Новая короткая AI-комедия с роботом: задача выполнена, но спокойнее не стало. Создано с помощью нейросетей. Сюжет вымышленный, не реальные события. Как бы вы переписали запрос, чтобы робот точно понял задачу?

🤣1

17 Oct 2026, 17:42 UTC62 viewsread 3 November 2026
Video

🚀 Пробки победили. С парковкой пока работаем. Белый спорткар взлетает над мокрой улицей, кофе и вещи начинают жить своей жизнью, а посадка превращается в очень неловкое знакомство с владельцем Lamborghini 😅 Небольшая автомобильная комедия, созданная с помощью нейросетей. Всё происходящее — AI-фантазия, не реальная съёмка и не демонстрация технологии. Как считаете, летающие машины уменьшат хаос на дорогах — или про…

17 Oct 2026, 00:21 UTC59 views1 reactionsread 3 November 2026
Video

Кнопка, которую хочется нажать в каждой пробке 🚀 В этом AI-ролике водитель выбирает короткий путь: BOOST → полёт → невесомость. А вот кофе и посадка в план не вписались ☕😅 Нейросети позволяют превратить абсурдную идею в маленькую автомобильную комедию. Это вымышленная сцена, не реальное происшествие. Вы бы выбрали полёт над пробкой или кофе без приключений?

👍1

17 Oct 2026, 00:08 UTC51 viewsread 3 November 2026
Video

🚗 Летающая Tesla: будущее наступило, бытовые проблемы остались Покормить кота на дереве — легко. Облететь пробку — запросто. Сбежать от эвакуатора? А вот тут технологии не помогли 😂 В этом AI-ролике даже рыбалка проходит, не выходя из машины. Нейросети позволяют показать целый фантастический мир за 18 секунд — и сделать его узнаваемым через обычные житейские ситуации. Какой режим ты бы включил первым: «над пробкам…

10 Oct 2026, 19:18 UTCviews —

DH Academy AI pinned «Привет! Это канал школы DH Academy AI. Здесь — закулисье серий «Я и мой ИИ-напарник» (те самые «Тупик» и «Бомба»): как они сделаны, какие промпты сработали, что ушло в брак и сколько стоят генерации. Что будет в канале: — разборы новых серий и промпты к…»

10 Oct 2026, 19:17 UTC112 views4 reactionsread 3 November 2026

Привет! Это канал школы DH Academy AI. Здесь — закулисье серий «Я и мой ИИ-напарник» (те самые «Тупик» и «Бомба»): как они сделаны, какие промпты сработали, что ушло в брак и сколько стоят генерации. Что будет в канале: — разборы новых серий и промпты к ним; — анонсы живых интенсивов — ближайший в воскресенье, 18 октября, в 16:00 UTC (19:00 по Москве и Киеву); — честно про профессию AI-креатора, без обещаний «зараб…

🔥4

Showing the 12 most recent of 20 posts we hold for @ai_praktik_hub. 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.

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

“DH Academy AI” (@ai_praktik_hub), 151 subscribers as measured 3 November 2026. Telegram Register, tgregister.com/channel/ai_praktik_hub.

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.