National 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 36% 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 12 other registered channels. This entry also sits inside a group of 7 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts with each other. 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. 101 of the 101 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 986 comparable posts for this entry, running 6 August 2026 to 11 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 7 channels, the one whose matching posts we hold earliest is @ehdwl. That is a statement about our reading window, not a claim of authorship.
Recorded under the keys clone_copy · clone_mutual · clone_source, last confirmed 12 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 7 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. 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.
63 measurements spanning 97 days, net +1,481. 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 39,275–41,200 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 63
Measured (UTC)
Subscribers
Change
11 Nov 2026, 21:48
40,978
+33
10 Nov 2026, 01:46
40,945
+5
8 Nov 2026, 13:56
40,940
-3
7 Nov 2026, 08:54
40,943
-3
6 Nov 2026, 06:56
40,946
+8
5 Nov 2026, 05:17
40,938
+7
4 Nov 2026, 07:08
40,931
+10
3 Nov 2026, 06:45
40,921
+16
2 Nov 2026, 05:59
40,905
+18
1 Nov 2026, 08:58
40,887
-1
31 Oct 2026, 06:21
40,888
-16
30 Oct 2026, 07:19
40,904
+1
29 Oct 2026, 08:37
40,903
+45
28 Oct 2026, 09:39
40,858
+5
27 Oct 2026, 07:00
40,853
+23
26 Oct 2026, 10:06
40,830
+37
25 Oct 2026, 09:29
40,793
+10
24 Oct 2026, 07:05
40,783
+2
23 Oct 2026, 07:25
40,781
+19
22 Oct 2026, 05:10
40,762
first reading
Engagement
2,565 posts held, back to 6 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 188 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
8.37%
avg views ÷ 40,978 subscribers
Avg views / post
3,430
887 posts measured
Reaction rate
0.586%
reactions ÷ views · ER floor
Posts in window
887
of 2,565 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 877 of 887 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 12 November 2026
Posts held
2,565 (6 August 2026 – 12 November 2026)
Views total
3,042,777
Reactions total
17,805
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Nov 2026, 22:01 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
≈28,100
Videos
≈64
Links
≈29,800
Lifetime counters from Telegram’s own channel header, read 12 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
2m 34s
Average length
31s
Measured directly from 5 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
20,298 reactions across 986 posts, in 40 distinct kinds. The most used accounts for 52.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
10,659
52.5%
😭
1,582
7.79%
😱
1,309
6.45%
🌚
1,097
5.40%
👍
1,082
5.33%
😁
726
3.58%
🔥
591
2.91%
🤣
555
2.73%
🤯
441
2.17%
🙏
382
1.88%
😢
296
1.46%
🤔
293
1.44%
👏
210
1.03%
💋
134
0.66%
😎
130
0.64%
👌
91
0.448%
❤🔥
89
0.438%
😈
85
0.419%
🙈
77
0.379%
🥰
61
0.301%
20 further kinds
408
2.01%
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 990 of the 1,000 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 20,298 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 1,000 most recent posts we hold (the sample is capped at 1,000), published 8 October 2026 to 12 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: 173 of 887 posts held from the last 30 days (19.5%) 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).
속보: 앤트로픽의 기업공개(IPO)에 공모 물량의 약 6배에 달하는 주문이 몰린 것으로 나타났다.
앤트로픽과 공동 창업자들은 IPO로 최대 644억 달러, 동시 사모 배정으로 260억 달러를 조달할 계획이다.
두 거래를 합치면 스페이스X의 IPO를 넘어서며 사상 최대 규모의 상장이 된다.
목표 기업가치는 완전희석 기준 약 1.4조 달러로, 일부 투자자들이 예상했던 수준보다 낮게 책정됐다.
강한 수요로 최종 가격을 끌어올리기 위해 마케팅 초기에 조건을 보수적으로 제시하는 경우도 있다.
앤트로픽의 매출 런레이트는 10월 31일 기준 810억 달러로, 9월 말보다 100억 달러 늘었다.
7월 말에는 650억 달러 수준이었다.
엔비디아는 260억 달러 규모 사모 배정에서 약 100억 달러어치 주식을 매수하기로 합의했다.
엔비디아에서는 …
📝. 어플라이드 머티어리얼즈 2026 회계연도 4분기 순매출 104.1억 달러, 시장 예상치 상회
Disclaimer: 면책고지, 모든 메세지는 벡터 기반 분산 표상형으로 작성 등의 사유로 착오가 발생할 수 있음. 모든 투자 책임은 투자자 본인에게 일체 귀속됨. (증권사 채널 등 100개 추가하기)
APPLIED MATERIALS Q4 2026 NET SALES $10.41 BLN, ABOVE ESTIMATE OF $10.27 BLN, WITH ADJUSTED EPS $4.16, ABOVE ESTIMATE OF $4.06, AND SEMICONDUCTOR SYSTEMS NET SALES $8.00 BLN
코멘트:
반도체 장비 수요가 AI 인프라 투자 사이클과 맞물려 예상치를 상회하는 실적을 견인한 것으로, 장비 발주는 통상 파운드리·메모…
📝. 미 법무부, 바이낸스서 탈취된 12억 달러 상당 암호화폐 압수…북한 암호화폐 절도 차단 조치
Disclaimer: 면책고지, 모든 메세지는 벡터 기반 분산 표상형으로 작성 등의 사유로 착오가 발생할 수 있음. 모든 투자 책임은 투자자 본인에게 일체 귀속됨. (증권사 채널 등 100개 추가하기)
THE DOJ IS TO SEIZE $1.2 BLN WORTH OF CRYPTOCURRENCY STOLEN FROM BINANCE, REPORTING ACTIONS TO DISRUPT NORTH KOREA’S CRYPTOCURRENCY THEFT
코멘트:
북한의 암호화폐 탈취는 핵·미사일 개발 자금 조달의 핵심 경로로, 이번 압수는 가상자산이 국가 안보 위협의 전장으로 편입됐음을 보여준다. 미국의 사법적 자산 동결은 제재의 실효성을 높이는 동…
양자컴퓨팅: 실적 부진과 국채금리 급등에 동반 약세
아이온큐(-9.40%), 리게티컴퓨팅(-7.02%), 디웨이브퀀텀(-7.61%)등 양자컴퓨팅 관련주가 전반적으로 하락. 특히 미국 10년물 국채금리가 5%를 상회하는 등 금리 급등이 높은 밸류에이션이 부담으로 작용한 점이 영향. 여기에 최근 실적 발표에서 리게티는 분기 매출 감소, 디웨이브퀀텀은 신규수주 감소 등 실적 불안도 재차 부각된 점도 영향. 아이온큐는 실적이 견조했었지만 결국 엔비디아의 영향에서 벗어나지 못함. 엔비디아가 아이온큐의 양자컴퓨터를 자체 연구센터에 도입하기로 했었기 때문.
제약, 바이오: 모더나는 투자 확대 부담에 하락
일라이릴리(-0.70%)는 경구용 비만치료제 오포글리프론이 중국 최초의 먹는 GLP-1 비만치료제로 승인되면서 장 전 크게 상승했지만 장 중 매물 출회…
*특징 종목: 엔비디아, 마이크론 하락 Vs. AMD, 브로드컴, 알파벳, 테슬라 상승
AI 반도체: 엔비디아 경영 불확실성 확대, AMD·브로드컴 반사이익 기대에 강세
엔비디아(-4.34%)는 젠슨 황 CEO의 사임과 법무부 조사 관련 보도로 장중 14% 넘게 급락한 후 저가 매수세가 유입되며 낙폭을 축소. 반면 AMD(+5.86%), 브로드컴(+5.54%)은 상승하는 등 차별화가 진행. 특히 윌리엄블레어는 엔비디아의 경영 공백과 인텔(-2.08%)의 서버 CPU 공급 문제를 AMD의 시장점유율 확대 기회로 평가하며 투자의견을 시장수익률에서 시장수익률 상회로 상향. AMD의 2027년 매출이 80% 이상 증가할 수 있다고 전망했으며, BOA도 AMD 목표주가를 650달러에서 700달러로 상향. 브로드컴 역시 맞춤형 AI 반도체와 네트워크 …
11/12 미 증시, 엔비디아 충격에 하락 후 상승 전환했지만 금리, 유가 부담에 재차 하락
미 증시는 엔비디아(-4.34%)가 젠슨 황 CEO의 사임 여파로 장 초반 14% 넘게 급락하자 나스닥이 한때 1.56% 하락하는 등 부진. 그러나 엔비디아에 저가 매수세가 유입되며 낙폭을 빠르게 축소했고, 경쟁 반도체 기업들의 강세와 대형기술주들의 강세에 힘입어 지수는 상승 전환에 성공. 특히 엔비디아 개별 기업의 경영 불확실성이 AI 산업 전체의 수요 둔화를 의미하지는 않는다는 인식이 반등을 견인. 그러나 오후 들어 미국 국채금리와 국제유가의 상승세가 지속되고, 엔비디아 관련 법무부 조사 및 향후 경영 전략에 대한 불확실성이 재부각되자 주요 지수는 다시 하락 전환. 여기에 상승 종목보다 하락 종목이 많고 네오클라우드, 메모리, AI 인프라 관련주…
파도파도 괴담 시리즈
젠슨황, 세 번째 불륜 의혹⋯“또다른 여직원과도 내연관계”
https://www.kmib.co.kr/article/20261113500060
❤3🤯2
Showing the 12 most recent of 2,565 posts we hold for @tazastock. 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.
Posts edited after publishing
@tazastock edited 1 post 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
24 September 2026
Most recent edit
24 September 2026
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 4 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 2 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.
⚡️번개맞은뉴스 - 상위2% 주식텔레그램 채널 @stock_messenger · 25,226 Telegram ranks this channel #1 of 90 here — alongside 89 others — read 14 September 2026
주식 급등일보🚀급등테마·대장주 탐색기 | Korean Stocks @FastStockNews · 128,811 Telegram ranks this channel #2 of 92 here — alongside 91 others — read 2 September 2026
오를주식 - 뉴스/경제/주식 @GoUpstock · 21,091 Telegram ranks this channel #3 of 83 here — alongside 82 others — read 25 September 2026
기술적 분석으로 보는 주식시장 (by 소라게아빠) @hermitcrab41_1 · 21,506 Telegram ranks this channel #4 of 81 here — alongside 80 others — read 28 September 2026
AWAKE - 52주 신고가 모니터링 @awake_realtimeCheck · 22,881 Telegram ranks this channel #4 of 92 here — alongside 91 others — read 20 September 2026
시황맨의 주식이야기 @shmstory · 39,582 Telegram ranks this channel #4 of 89 here — alongside 88 others — read 2 September 2026
스탁이지 - AI 투자 어시스턴트 @maddingStock · 20,884 Telegram ranks this channel #6 of 87 here — alongside 86 others — read 27 September 2026
김찰저의 관심과 생각 저장소 @kimcharger · 25,264 Telegram ranks this channel #9 of 88 here — alongside 87 others — read 15 September 2026
급등일보 미국주식🇺🇸 속보·매크로·리서치 @FastStockNewsUSA · 41,039 Telegram ranks this channel #9 of 89 here — alongside 88 others — read 2 September 2026
Yeouido Lab_여의도 톺아보기 @Yeouido_Lab · 43,450 Telegram ranks this channel #9 of 90 here — alongside 89 others — read 2 September 2026
도PB의 생존투자 @survival_DoPB · 22,047 Telegram ranks this channel #11 of 90 here — alongside 89 others — read 21 September 2026
여의도스토리 Ver2.0-习近平是个狗东西 @YeouidoStory2 · 57,976 Telegram ranks this channel #11 of 92 here — alongside 91 others — read 2 September 2026
피카츄 아저씨⚡️ @pikachu_aje · 24,917 Telegram ranks this channel #12 of 91 here — alongside 90 others — read 14 September 2026
엄브렐라리서치 Jay의 주식투자교실 @Jstockclass · 26,508 Telegram ranks this channel #13 of 90 here — alongside 89 others — read 13 September 2026
실시간 주식 뉴스 @realtime_stock_news · 36,958 Telegram ranks this channel #13 of 93 here — alongside 92 others — read 2 September 2026
그로쓰리서치(Growth Research) [독립리서치] @growthresearch · 40,090 Telegram ranks this channel #14 of 90 here — alongside 89 others — read 2 September 2026
AWAKE - 실시간 주식 공시 정리채널 @darthacking · 65,571 Telegram ranks this channel #14 of 91 here — alongside 90 others — read 2 September 2026
습관이 부자를 만든다. 🧘 @habit4117 · 28,295 Telegram ranks this channel #15 of 87 here — alongside 86 others — read 10 September 2026
신영증권 박소연 @sypark_strategy · 29,245 Telegram ranks this channel #15 of 91 here — alongside 90 others — read 8 September 2026
가치투자클럽 @corevalue · 67,105 Telegram ranks this channel #15 of 93 here — alongside 92 others — read 2 September 2026
💯똥밭에 굴러도 주식판 @daegurr · 23,789 Telegram ranks this channel #16 of 83 here — alongside 82 others — read 18 September 2026
하나 중국/신흥국 전략 김경환 @HANAchina · 35,273 Telegram ranks this channel #16 of 92 here — alongside 91 others — read 2 September 2026
재야의 고수들 @gaoshoukorea · 41,185 Telegram ranks this channel #16 of 90 here — alongside 89 others — read 2 September 2026
선수촌 @athletes_village · 22,145 Telegram ranks this channel #17 of 88 here — alongside 87 others — read 23 September 2026
This channel appears in 47 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. 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 11 November 2026 — this
entry's latest reading, not the date you are reading this.
“타점 읽어주는 여자(타자)” (@tazastock), 40,978 subscribers as measured 11 November 2026. Telegram Register, tgregister.com/channel/tazastock.
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.
