Literature — 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 11 August 2026 and assigned it the closest of 31 fixed categories, at 99% 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.
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 word for word on 7 other registered channels. 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 (3 of the pairs behind the counts below)
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 1 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 18 comparable posts for this entry, running 18 June 2025 to 6 June 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).
Across the whole group of 8, the earliest publisher we hold is @thefoggymoon. That is a statement about our reading window, not a claim of authorship.
Recorded under the keys clone_copy · clone_source, last confirmed 7 August 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.
Also posting the same content
This channel’s posts match, word for word or near enough, posts on 7 other registered channels, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word 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.
3 measurements spanning 3 days, net -2. 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 3,799–3,801 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
10 Aug 2026, 14:22
3,799
-2
7 Aug 2026, 23:18
3,801
no change
7 Aug 2026, 10:31
3,801
first reading
Engagement
20 posts held, back to 18 June 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 4 pagesof 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 20 posts for this entry, the most recent from 6 June 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Video runtime
15s
Average length
15s
Measured directly from 1 video 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
188 reactions across 20 posts, in 2 distinct kinds. The most used accounts for 99.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
187
99.5%
👍
1
0.532%
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 20 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 188reactions 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 18 June 2025 to 6 June 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.
📚 رمان کنج آغوش او
✍️ به قلم بنفشه و نگار
📝 خلاصه
نیلی پزشک یک درمانگاه عمومیه، کسی که راز های زیادی داره و بخاطر مسایلی تو گذشته خودشو متمرکز به درس کرده. اون با شیطنت سه خواهر، با خان دایی بدخلق دخترها رو به رو میشه. خان دایی که به اصرار خواهر زاده هاش مجبور میشه به نیلی کمک کنه اما خبر نداره رفتارش قراره یه نفرو خیلی عصبانی کنه… یه نفر که به گذشته نیلی پیوند خورده و میخواد تو آینده هم باشه…
این داستان ما…
بچه ها کانال رمان #هم_فام منو که اون روانی ها ریپورت کرده بودن دوباره آزاد شد.اما تلگرام همه اعضا رو ریمو کرد.
اگر داشتی اونجا رمانو میخوندی دوباره جوین شو👇👇👇💜
https://t.me/+_Np0ncii73Y2YTM0
بچه ها اینبرنامه باغ استور 👆👆👆👆👆 نصبش رایگانه،مخصوص خوندن کتاب و رمانه، کل رمان های کامل و در حال نگارش من تو این برنامه هست.
راستی نسخه اپل هم داره راهنمای نصب اینجا پین شده👇👇👇👇
@baghstore_app
جدیدترین نسخه اپلیکیشن باغ استور
سیستم عامل: اندروید (Android)
مختص موبایل های سامسونگ،شیائومی،هوآوی و ...
تاریخ انتشار: 01 آبان 1403
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لزوما نیازی به حذف برنامه قبلی نیست، بصورت عادی می توان این فایل را دانلود و با کلیک بر روی آن شروع به بروزرسانی کنید.
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سوابق کتابخانۀ شما محفوظ است و با پاک شدن برنامه یا حتی صدمه به موبایل شما، اتفاقی برای کتاب های خریداری شده نخواهد افتاد؛ شما کافیست سیمکارت ثبت نامی را دا…
بچه ها کانال رمان #هم_فام منو که اون روانی ها ریپورت کرده بودن دوباره آزاد شد.اما تلگرام همه اعضا رو ریمو کرد.
اگر داشتی اونجا رمانو میخوندی دوباره جوین شو👇👇👇💜
https://t.me/+_Np0ncii73Y2YTM0
📚 رمان پرنیان شفق
🔗 جلد دوم رمان پرنیان شب
✍️ به قلم پونه سعیدی
📝 خلاصه
پرونده های پایگاه محافظین نشان میداد خاندان آفرید هیچ فرزند دختری ندارد!
اما او آنجا بود، دختری با خون خالص این خاندان!
دختری که خونش هیچ بویی نداشت اما افیونش همه را اسیر تب خون کرده بود!
کسی که شاید مثل فرشته مرگ نمیکشت! اما زندگی بخش هم نبود!
🔘 #رمان_افسانهای #رمان_فانتزی #رمان_عاشقانه #دانلود_رمان #رمان
🌀این رمان از نوع فروشیست…
بچه ها تا آخر شب وقت دارید رمان های ما رو با قیمت قبل از باغ استور بخرید (بنفشه، پونه ، نگار ، آرام ، سارا ،رعنا، ساحل و ...)
از اول آبان همه اصلاح قیمت دارن
الان انگار عملا تو تخفیف هستند
فرصت از دست ندین
@baghstore_app
#در_پناه_طوفان
#هم_فام
#تب_آلود
#کومه_پنهان
#مست_از_تو
#افرای_ابلق
#نامستور
#انتهاج
و ...
اینجا قراره روزی یک پارت از آتش محبوس بخونیم
پارت اول پین شده
دوست داشتی عضو شو
https://t.me/atashmahbous
به قلم بنفشه و نگار
واقعی
با پایانخوش
❤12
Showing the 12 most recent of 20 posts we hold for @yellow_heart_novel. 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
Republishes
Channels on the register whose posts this channel has forwarded.
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
Names
Channels on the register whose handles appear in this channel's posts.
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 August 2026 — this
entry's latest reading, not the date you are reading this.
“دانلود رمان شوکای من ( شوکا و مازیار)” (@yellow_heart_novel), 3,799 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/yellow_heart_novel.
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.
