International news — 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 17 September 2026 and assigned it the closest of 31 fixed categories, at 67% 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.
No sample pairs were kept for this entry. The counts below were measured, but without the pairs there is nothing here for you to open and check, so treat the relationship as recorded rather than demonstrated.
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. 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 83 comparable posts for this entry, running 22 June 2026 to 5 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 @Nistarot — 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.
48 measurements spanning 95 days, net -49,622. 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”.
Measurement log — every subscribers count we have recorded, most recent 20 of 48
Measured (UTC)
Subscribers
Change
9 Nov 2026, 16:48
280
-51,452
8 Nov 2026, 06:49
51,732
-43
6 Nov 2026, 23:35
51,775
-32
6 Nov 2026, 02:17
51,807
-35
5 Nov 2026, 03:19
51,842
-24
4 Nov 2026, 02:01
51,866
-66
3 Nov 2026, 00:17
51,932
-78
2 Nov 2026, 01:28
52,010
-68
31 Oct 2026, 22:11
52,078
-70
30 Oct 2026, 18:56
52,148
-38
29 Oct 2026, 17:06
52,186
-45
28 Oct 2026, 17:49
52,231
-63
27 Oct 2026, 16:29
52,294
-43
26 Oct 2026, 18:08
52,337
-48
25 Oct 2026, 14:54
52,385
-91
24 Oct 2026, 12:04
52,476
-26
23 Oct 2026, 12:19
52,502
-70
22 Oct 2026, 14:18
52,572
+52,296
15 Oct 2026, 21:27
276
-51,448
14 Oct 2026, 20:56
51,724
first reading
Engagement
83 posts held, back to 22 June 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 136 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
5141.0%
avg views ÷ 280 subscribers
Avg views / post
14,400
21 posts measured
Reaction rate
0%
reactions ÷ views · ER floor
Posts in window
21
of 83 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 12 of 21 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 5 November 2026
Posts held
83 (22 June 2026 – 5 November 2026)
Views total
302,290
Reactions total
0
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
8 Nov 2026, 04:58 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
1h 06m
Average length
1m 29s
Measured directly from 45 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.
Telegram Stars
Stars received
823
across the posts below
Posts paid on
61
of 83 we hold a reading for · 73%
Most on one post
99
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @Nistarot. 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 83 most recent posts we hold for this entry, published 22 June 2026 to 5 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: 10 of 21 posts held from the last 30 days (47.6%) 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).
מייסד ומנכ"ל המכללה לחינוך חדש, גדי בן לביא: כל מורה, מנהלת או יועצת בביה"ס שלוחצת על אבחון או על ריטלין צריכה להיזהר, היא עוסקת ברפואה ללא רישיון, זו עבירה פלילית
@NISTAROT | רב הנסתר בטלגרם
גדי בן לביא יזם מהלך משפטי רשמי בשיתוף פעולה עם עו"ד גינזבורג שמטרתו🔴לאסוף עדויות מהורים🔴שחוו לחץ מצד צוותי בתי הספר לדחוף את ילדיהם לאבחון או לנטילת ריטלין, כתבו מה דעתכם בתגובות.
• אולי יעניין אותך: פסיכיאטר בעל שם עולמי: מ…
קולונל בדימוס בצבא ארה”ב, לורנס ב. וילקרסון: אירופה נקנתה על ידי סוכנות ה-CIA, לאחר מכן גם המוסד הצטרף
@NISTAROT | רב הנסתר בטלגרם
לורנס ב. וילקרסון, קולונל בדימוס בצבא ארה”ב טוען - “קנינו שני מזכירים כלליים של נאט"ו, קנינו מדינות מבחינת המנגנון הפוליטי שלהן ואת כל מה שמשפיע עליהן”, וילקרסון הוא ראש המטה לשעבר של מזכיר המדינה לשעבר קולין פאוול, כתבו מה דעתכם בתגובות.
• אולי יעניין אותך: פרופ' יוסף זרניק, מנתח סוכנ…
פרסמו את העסק שלכם כאן בערוץ ותזכו לחשיפה אדירה!
✅ מעל 50,000 מנויים.
✅ ממוצע צפיות גבוה במיוחד.
✅ פרסום אפקטיבי שיוביל לתוצאות.
✅ מחיר משתלם.
זה הזמן לקדם העסק שלכם.
✅להצעת מחיר משתלמת פנו » @analayzer
@NISTAROT | רב הנסתר
מומחה למדעי המוח, ד”ר סאליבן: הרפואה של ימינו השתנתה במכוון על ידי איל הון בשם רוקפלר, כל מה שהניע אותו זה כסף, כך הוא עשה זאת
@NISTAROT | רב הנסתר בטלגרם
ד”ר סאליבן טוען כי איל ההון ג'ון ד. רוקפלר ביד אחת השתמש ברפואה טבעית עבור עצמו אך בידו השנייה גרם להפך הגמור, שינה את מערכת הבריאות באופן שמטיל דופי בטיפול טבעי, כתבו מה דעתכם בתגובות.
תרגום: CRN
• אולי יעניין אותך: מי ייסד את תעשיית התרופות שכולנו מכירים? מי ה…
מי באמת שולט בפייסבוק?
@NISTAROT | רב הנסתר בטלגרם
ג'יימס לי, עיתונאי עצמאי טוען שחשף מידע שעשוי לעניין אתכם בנוגע לפייסבוק ורשתות חברתיות נוספות, צפו וכתבו לנו מה דעתכם בתגובות.
• אולי יעניין אותך: קצין ה-CIA לשעבר: ה-CIA לא באמת מועיל לביטחון הלאומי של ארה"ב
• לצפיה ישירה: https://nistarot.substack.com/p/311026
Join » ערוץ • דיונים • ווטסאפ • פרסום
פרופ' יוסף זרניק, מנתח סוכנויות משפט, יום לאחר הבחירות: זה לא משנה במי בחרו, השאלה היא "מי סופר את הקולות?"
@NISTAROT | רב הנסתר בטלגרם
הקטע שלפניכם פורסם בטיקטוק של פרופ' יוסף זרניק לפני שעות בודדות וכבר הפך לויראלי ברשתות, פרופ' זרניק משמש במשך 17 השנים האחרונות כמנהל מחקר של Human Rights Alert, תחום העניין המרכזי שלו הוא בניתוח סוכנויות משפט ומערכות ICT, האזינו לדבריו וכתבו לנו מה דעתכם בתגובות.
• אולי יעניין א…
חוקרים גילו שצ’אט ג’י.פי.טי כותב בסתר פרופיל פסיכולוגי של כל משתמש ופרסמו מאמר, התוצאות מפחידות
@NISTAROT | רב הנסתר בטלגרם
על פי המאמר 96% מהדברים שהצ’אט אוגר נשמרו מבלי שהמשתמש שאל או ביקש, החוקרים גילו שהצ’אט “קורא” בין השורות בכדי להסיק מה הן תכונות האופי, חרדות, דמוגרפיה ודפוסי ההתנהגות של המשתמש, זהו תהליך נסתר ואוטונומי. המודל בונה “מצב משתמש” שעוקב אחר קו הרגש של כל אחד לאורך זמן, בנוסף הוא מסיק מתי האדם לח…
מתחת לרדאר: מדוע חנויות בכל רחבי אמריקה מחליפות בשקט את הפירות בפירות ללא זרעים?
@NISTAROT | רב הנסתר בטלגרם
קשיף חאן הוא מנכ"ל חברת The DNA Company, חברת בריאות שאחראית לפיתוח ישומי ניהול בריאות מבוססי גנומיקה, החברה מציעה טיפול רפואי מדוייק המותאם לביולוגיה הייחודית של כל אדם, בקטע שלפניכם חושף חאן מה מסתתר מאחורי החלפת הפירות.
• אולי יעניין אותך: נוהגים לנסוע ברכב חשמלי? המידע שבקטע הבא לא ישאיר אתכם אדישים!
•…
✔️ חדש!
ערוץ הלכה יומית, קצר וקולע, בשפה קלה ומובנת לכל אדם.
הצטרפו » https://t.me/+M8vRREdtEMY4NjZk
✅✅✅✅
ברוכים הבאים בשם ה'!
Showing the 12 most recent of 83 posts we hold for @Nistarot. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
Posts edited after publishing
@Nistarot edited 12 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
28 August 2026
Most recent edit
3 November 2026
Posts recorded as deleted after publishing
We recorded 11 posts on @Nistarot 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
9 August 2026
Most recent seen missing
17 September 2026
Median time before seen missing
14 days
Seen missing in the last 30 days
0
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 12 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.
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 9 November 2026 — this
entry's latest reading, not the date you are reading this.
“רב הנסתר” (@Nistarot), 280 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/Nistarot.
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.