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 10 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 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 word for word on 1 other registered channel. 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)
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 4 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 20 comparable posts for this entry, running 8 July 2026 to 1 August 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 2, the earliest publisher we hold is @kss1009 — 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 8 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 1 other registered channel, 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.
59 measurements spanning 92 days, net +57. 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 20,534–20,753 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 59
Measured (UTC)
Subscribers
Change
6 Nov 2026, 11:14
20,616
-4
5 Nov 2026, 08:55
20,620
-12
4 Nov 2026, 09:30
20,632
-5
3 Nov 2026, 10:57
20,637
-6
2 Nov 2026, 11:56
20,643
+9
1 Nov 2026, 14:12
20,634
-6
31 Oct 2026, 14:40
20,640
+3
30 Oct 2026, 11:30
20,637
+3
29 Oct 2026, 14:31
20,634
-8
28 Oct 2026, 16:20
20,642
-7
27 Oct 2026, 16:47
20,649
-15
25 Oct 2026, 12:26
20,664
+16
24 Oct 2026, 09:27
20,648
+3
23 Oct 2026, 08:58
20,645
-7
22 Oct 2026, 11:51
20,652
-23
21 Oct 2026, 02:39
20,675
-2
20 Oct 2026, 00:38
20,677
+9
19 Oct 2026, 00:47
20,668
+12
17 Oct 2026, 22:28
20,656
+7
17 Oct 2026, 00:18
20,649
first reading
Engagement
60 posts held, back to 8 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 66 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
9.13%
avg views ÷ 20,616 subscribers
Avg views / post
1,880
25 posts measured
Reaction rate
1.63%
reactions ÷ views · ER floor
Posts in window
25
of 60 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 6 November 2026
Posts held
60 (8 July 2026 – 6 November 2026)
Views total
47,056
Reactions total
766
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
6 Nov 2026, 15:07 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
83
Videos
3
Links
840
Lifetime counters from Telegram’s own channel header, read 6 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
2,026 reactions across 60 posts, in 10 distinct kinds. The most used accounts for 66.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
1,355
66.9%
😢
349
17.2%
👏
118
5.82%
🔥
57
2.81%
😁
53
2.62%
❤🔥
29
1.43%
🥰
21
1.04%
🤩
17
0.839%
👀
15
0.74%
🙈
12
0.592%
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 60 of the 60 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 2,026 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 60 most recent posts we hold, published 8 July 2026 to 6 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.
Recent posts
Telegram’s own ‘edited’ marker: 2 of 25 posts held from the last 30 days (8.00%) 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).
من صبية
جوزي فيه عادة بيحط صوره علواتس والفيس
بيقيم صورة وبيحط صورة ..فرحان بحالو ...المهم فقت الصبح بعد ما تيسر على شغلو ..شربت قهوتي على رواق ..فتحت الفيس وبلشت اتصفح ..
لاحظت إنو جوزي مغير صورة الغلاف تبعو ...بس النت كان بطيئ عندي والصورة ما فتحت ..
قلت لحالي يا بنت علقيلو وخليكي أول وحدة ... مشان تعرف العالم قديش بحبو...
والله ما كذبت خبر ...وعلقتلو *(ما تبلى يا روحي منور الفيس بطلتك ..من شابه أباه ما ظلم ..…
قصة إسماعيل عزيزي من تنزانيا هي قصة غريبة ومأساوية في نفس الوقت. يُقال إن هذا الرجل قد مات وعاد إلى الحياة ست مرات، وقد أدت هذه الأحداث الغامضة إلى نبذه من قِبل مجتمعه وعائلته.
ماذا حدث لإسماعيل عزيزي
تُشير التقارير إلى أن أول "موت" لإسماعيل كان بعد حادث عمل خطير. نُقل إلى المستشفى وأُعلن عن وفاته، وبدأت الاستعدادات لدفنه، لكنه استيقظ فجأة في المشرحة وخرج منها.
تكررت هذه الحادثة عدة مرات، في كل مرة يواجه فيها إسما…
ما هذا الرجل الإنسان ؟؟؟
( قعدتك حلوة أوي يا عمي )
قراءته للقرآن كانت تهز روحي
كان دوماً يتابع عملي يعرف أني أعشق الحوار فكان يحاورني
ويعرف حنان محمد فكان يربت كتفه ويشاركه في إعداد الأطعمة
لكن ...
حدث شئ جوهري !!
رؤي عادت غاضبة من زوجها إلي بيت عمي
زوجها كان بخيل جداً وعصبي جداً جداً
عمي لم ينفعل ، وكان دوماً مسلم أمره لربه
تم الطلاق بعد أن تنازلت حتي عن الشبكة !!
لكن شئ ما في صدري تجاه رؤي
شعرت أني يجب أن أتقدم إلي…
( الفرق بين أبي وعمي في التربية )
قصة حقيقية على لسان شاب يقول :
إسمي أحمد ، وعمري الآن 33 عام ،قصتي تبدأ من بيت أبي في شبرا حيث كنت دوما أسأل نفسي :
ما الفارق بين أبي وعمي الذي يسكن معنا بنفس العمارة ؟؟
أبي شخص عصبي ودائماً منفعل علي زوجته وأبنائه ( أنا وأخي محمد )
أبي دوماً مشدود الأعصاب وأمي تتلقي عصبيته ثم تفرغها فينا بالصراخ والغضب
كان أبي وعمي يعلماننا ركوب الدراجات
أبي يضرب ويقول يا غبي هنشيل السنادات يا جباا…
"كان شرطي الوحيد للقبول بالزواج منه، هو العيش معه مع حقي الكامل في الرفض متى أشاء، أن أقول لا، متى أريد، أقولها و يستجيب لها.
مشكلتي إنني أجيد قول لا و لكن لا أحد يستمع لها، و هذا ما لن أسمح بتكراره معه أبداً.
تزوجت في العشرينات من عمري، أنجبت بنت وحيدة، انفصلت بعد سبع سنوات، تجربة سيئة أبقتني وحيدة بعدها عدة سنوات حتى جاء هو.
أول مرة رآني كان في تمرين ابنتي، بدأ في تقربه مني الذي قابلته أنا بالهرب، لم تتوقف محاولات…
رجل فقير أراد أن يسافر بواسطة الباخرة وكانت الرحلة تستغرق عشرة أيام ، ذهب الرجل ليشتري التذكرة قبل بيوم وكان في حسبانه أن سعرها 500$ وعندما وقف في الطابور وتحمل الزحمة ، وبعد طول إنـتظار وصل دوره وعندما أشترى التذكرة وإذا سعرها 1500$ فوجئ بالسعر لأن إمكانياته متواضعة ،ولكنه لم يجد مفر من الدفع وأخذ بـينه وبين نفسه يلومهم ويتهمهم بالإحتيال ويعرف أنه لا فائدة من الإستفسار ، وأنه يجب أن يدفع هذا السعر ال…
يُحكى أن ملكًا عادلًا أراد أن يقيم لأهل مدينته مأدبةً عظيمة، فأمر بوضع حوضٍ حجري ضخم في وسط الميدان، ثم أرسل المنادين في أرجاء المدينة:
««ليأتِ كل واحدٍ منكم ليلًا، ويصبّ في الحوض كوبًا من أجود اللبن الذي يملكه، حتى نجتمع يوم العيد ونشرب منه جميعًا.»»
حلّ الليل، وبدأ أهل المدينة يتوافدون في الظلام، يحمل كل واحدٍ منهم كوبه.
وكان من بينهم رجلٌ يحمل كوبًا من الماء، وحين اقترب من الحوض، قال في نفسه:
«الحوض ضخم، والنا…
أخبرتها أنني قلت ذلك على سبيل المزاح، ثم قلت لها إنني لن أسامحها على ما فعلته في الإجازة، وأقصد بذلك خيانتها لثقتي، واستغلالها لمحبتي، وكشفها لأسراري.
سألتني: «ماذا فعلت؟».
فأجبتها بأنني لا أريد فتح صفحة قديمة من جديد.
لكنها عادت إلى الشتائم والإهانات، وكأن كل ما حدث بيننا لم يكن كافيًا. عندها حظرتها مرة أخرى، وقررت أن أتركها وشأنها، مهما حاولت استفزازي.
وحتى اليوم، ما زالت تحاول استفزازي أحيانًا، لكنني لم أعد أر…
درس علّمتني إياه صداقة لم تكن كما ظننت
قبل ثلاث سنوات، كانت لديّ صديقتان، لكنني لم أكن أشعر تجاههما بالمحبة التي كنت أشعر بها تجاه فتاة أخرى، كنت أحبها كثيرًا، رغم أنها لم تكن صديقة جيدة، وكذلك صديقتها. حتى إنها اعترفت لي ذات مرة بأن جنّيًا قد دخل فيها.
في العام التالي، سافرت تلك الفتاة إلى مصر، بينما بقيت الفتاة التي أحبها في المدرسة معي. وفي أثناء الإجازة، اعترفت لها بأنني أحبها، ومنذ ذلك الوقت بدأت صداقتنا.
كنت…
من صبية
أنا تزوجت واحد أصغر مني بـ10 سنين، ولما تزوجته كان لسه طالب بالجامعة، وكنت مفكرة إني رح أكون سند إله ونبني حياتنا سوا، بس للأسف اللي صار معي ما كنت أتخيله.
أنا اللي دفعت المهر من مصرياتي، واستأجرتلنا بيت، واشتريتله سيارة، وحتى فتحتله محل على حسابي، وخسرت فيه حوالي 40 ألف، لأنه كان يترك المحل مسكر طول النهار ويطلع يلعب مع أصحابه، وأنا اللي تحملت الخسارة كلها.
ولما تخرج وتوظف، بدل ما يقدّر كل اللي عملته عشان…
من صبية
تزوجت شخص، والحمد لله كان إلي العوض عن كل شي شفته بحياتي، بس للأسف أنا كنت السبب بوجع كبير ما رح أقدر أنساه طول عمري.
أهلي كانوا يعاملوني بقسوة وإهانة، لدرجة إني أحياناً كنت أترك وجباتي وما آكل، من كتر ما كانوا يذلّوني حتى على الأكل. ولما تزوجت، كنت مفكّرة إني أخيراً رح لاقي الراحة، والغريب إنو أم زوجي، اللي هي أمه الوحيدة وهو ابنها الوحيد، عاملتني أحسن من أمي.
كانت تطعمني بإيدها، وتهتم فيني، وتشتريلي كل ش…
❤11😢8👀4🔥1
Signed 120 123
Showing the 12 most recent of 60 posts we hold for @kss1009. 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 8 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.
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 26 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.
خلطات طبيه طبيعيه ، وصفات 💍 @wsafat · 144,266 Telegram ranks this channel #66 of 84 here — alongside 83 others — read 12 August 2026
عالم حواء💍🦋 @ealshaghaf · 115,545 Telegram ranks this channel #67 of 79 here — alongside 78 others — read 14 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 6 November 2026 — this
entry's latest reading, not the date you are reading this.
“قصص وروايات” (@kss1009), 20,616 subscribers as measured 6 November 2026. Telegram Register, tgregister.com/channel/kss1009.
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.