Memes & entertainment — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 84% 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 2 other registered channels. 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 (6 of the pairs behind the counts below)
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
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. 4 of the 5 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.
“Published first” means first in this corpus. We hold 350 comparable posts for this entry, running 5 August 2026 to 8 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 @PyatInteresnixFaktov. That is a statement about our reading window, not a claim of authorship.
Recorded under the keys clone_copy · clone_source, last confirmed 13 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.
64 measurements spanning 98 days, net -650. 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 27,647–28,492 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 64
Measured (UTC)
Subscribers
Change
12 Nov 2026, 07:56
27,744
-12
10 Nov 2026, 08:45
27,756
-12
8 Nov 2026, 22:16
27,768
-13
7 Nov 2026, 13:21
27,781
-9
6 Nov 2026, 13:57
27,790
-9
5 Nov 2026, 12:09
27,799
-6
4 Nov 2026, 13:36
27,805
-4
3 Nov 2026, 13:05
27,809
-4
2 Nov 2026, 10:17
27,813
-5
1 Nov 2026, 07:40
27,818
-7
31 Oct 2026, 04:31
27,825
-5
30 Oct 2026, 06:51
27,830
-5
29 Oct 2026, 03:37
27,835
-5
28 Oct 2026, 05:09
27,840
-7
27 Oct 2026, 03:17
27,847
-6
26 Oct 2026, 06:46
27,853
-7
25 Oct 2026, 09:26
27,860
-10
24 Oct 2026, 11:29
27,870
-4
23 Oct 2026, 09:05
27,874
-12
22 Oct 2026, 09:26
27,886
first reading
Engagement
438 posts held, back to 5 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 159 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
6.10%
avg views ÷ 27,744 subscribers
Avg views / post
1,690
5 posts measured
Reaction rate
0.367%
reactions ÷ views · ER floor
Posts in window
5
of 438 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 8 November 2026
Posts held
438 (5 August 2026 – 8 November 2026)
Views total
8,458
Reactions total
31
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
13 Nov 2026, 14:57 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
≈8,220
Videos
≈13,700
Links
≈1,340
Lifetime counters from Telegram’s own channel header, read 13 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
30s
Measured directly from 278 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
1,904 reactions across 351 posts, in 21 distinct kinds. The most used accounts for 35.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
683
35.9%
👍
420
22.1%
😁
277
14.5%
🔥
130
6.83%
👏
72
3.78%
🥰
65
3.41%
😱
63
3.31%
🤔
63
3.31%
🤯
26
1.37%
😢
18
0.945%
🙏
17
0.893%
🤣
16
0.84%
👎
13
0.683%
🗿
11
0.578%
😭
9
0.473%
👌
7
0.368%
💯
7
0.368%
⚡
4
0.21%
💔
1
0.053%
🤷
1
0.053%
1 further kind
1
0.053%
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 356 of the 438 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 1,904 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 438 most recent posts we hold, published 5 August 2026 to 8 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
2
across the posts below
Posts paid on
1
of 438 we hold a reading for · 0.2%
Most on one post
2
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @greenhoody. 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 438 most recent posts we hold for this entry, published 5 August 2026 to 8 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: 0 of 5 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).
Во-первых, это красиво. Во-вторых, скорее всего частично фейк. В-третьих, читай пункт один.
В Штатах появилась инициатива OccupySaaS. Суть прекрасна: программисты, в том числе пострадавшие от сокращений в IT, с помощью искусственного интеллекта создают бесплатные альтернативы коммерческим программным продуктам.
Тем самым продуктам, на которых технологические корпорации зарабатывают огромные деньги.
Причём никакого…
Если вы также как и я заебались тратить время и деньги на бесконечные правки в анимации видео в ИИ, то вот вам решение, как сильно сэкономить. Сначала делайте 3D-анимацию ваших видео. И используйте ее как референс в ИИ при генерации видео. Так вы сможете делать идеальное видео гораздо быстрее и дешевле. Вот этот пример вышел у меня за 15 минут. Уметь в 3D вообще не нужно. За вас все сделает ИИ!
Как сделать также htt…
Телец говорит: «Я человек простой». А потом выясняется, что ему нужен конкретный столик, конкретная еда и чтобы никто, блядь, не менял планы. Попробуй сказать, что тебе неудобно, — он выслушает и всё равно сделает по-своему. Особенно если уже устроился. Телец способен терпеть многое, но ради тебя вставать с дивана второй раз за вечер? Нет, это уже какие-то нездоровые отношения.
Овен может наорать на тебя, хлопнуть дверью, а через десять минут написать: «Ну чё, закажем пиццу?» И он не притворяется — он правда уже всё забыл. Проблема в том, что ты ещё нет. Для Овна ссора закончилась, когда он выговорился. А тебе теперь надо как-то объяснить, почему после «да пошёл ты нахуй» не хочется выбирать начинку.
🔻 Чемпионат по гусиному гоготу прошёл в Лондоне.
Соревнования подразумевает самое громкое подражание гусям — с шипением, взмахами «крыльев» и характерными звуками.
Победителем стал участник по имени Константинос, который забрал домой медаль, статуэтку гуся и главный приз — 2 кг соуса хойсин.
@greenhoody позитив каждый день
В 1830-х годах моряки в Швеции посадили 300 000 дубов. По задумке военно-морского ведомства, эти дубы в далеком будущем должны были использоваться для производства кораблей.
После окончания опустошительных наполеоновских войн, шведский король отправил делегацию на поиски идеальных мест для посадки дубов. Шведы еще в те годы сажали дубы с прицелом на XX век, заботясь о судьбе будущих поколений.
Посадить целый дубо…
🔻
Никогда не сдавайся: встралийская спортсменка опорожнилась во время гонки на выносливость
Девушка не прервала соревнование и всё равно победила.
Потом все препятствия пришлось закрыть на дезинфекцию, но она, по словам других спортсменов, не очень помогла.
@greenhoody позитив каждый день
🤯14🗿4❤1🤔1
Showing the 12 most recent of 438 posts we hold for @greenhoody. 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
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.
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.
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 10 September 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.
MIMI_VIDEO Животные @mimi_video · 29,434 Telegram ranks this channel #28 of 81 here — alongside 80 others — read 7 September 2026
Юмор FM 📻 @humorfmru · 41,316 Telegram ranks this channel #39 of 77 here — alongside 76 others — read 29 August 2026
This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. 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 12 November 2026 — this
entry's latest reading, not the date you are reading this.
“Приколюшечки - позитив каждый день” (@greenhoody), 27,744 subscribers as measured 12 November 2026. Telegram Register, tgregister.com/channel/greenhoody.
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.
