Crypto & trading — 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 92% 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 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 (1 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 30 comparable posts for this entry, running 4 August 2026 to 7 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 @triple_stock. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_mutual, 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.
35 measurements spanning 51 days, net +981. 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 21,377–22,652 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 35
Measured (UTC)
Subscribers
Change
25 Sept 2026, 18:02
22,505
+22
19 Sept 2026, 05:37
22,483
+67
16 Sept 2026, 16:58
22,416
+8
14 Sept 2026, 17:20
22,408
+13
13 Sept 2026, 03:20
22,395
-4
11 Sept 2026, 03:15
22,399
+61
8 Sept 2026, 03:17
22,338
+36
4 Sept 2026, 23:57
22,302
+15
3 Sept 2026, 08:13
22,287
+11
1 Sept 2026, 23:35
22,276
+8
1 Sept 2026, 02:13
22,268
+12
31 Aug 2026, 03:55
22,256
+37
30 Aug 2026, 06:16
22,219
+31
29 Aug 2026, 03:02
22,188
+5
28 Aug 2026, 03:25
22,183
+50
27 Aug 2026, 06:46
22,133
+4
26 Aug 2026, 06:46
22,129
+18
25 Aug 2026, 05:25
22,111
+17
24 Aug 2026, 07:09
22,094
+32
22 Aug 2026, 18:04
22,062
first reading
Engagement
238 posts held, back to 4 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 58 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
38.4%
avg views ÷ 22,505 subscribers
Avg views / post
8,640
48 posts measured
Reaction rate
0.548%
reactions ÷ views · ER floor
Posts in window
48
of 238 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 3 September 2026
Posts held
238 (4 August 2026 – 3 September 2026)
Views total
414,950
Reactions total
2,276
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 12:19 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.
Reaction mix
14,684 reactions across 226 posts, in 18 distinct kinds. The most used accounts for 45.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
6,655
45.3%
👍
2,670
18.2%
🔥
1,267
8.63%
🙏
779
5.31%
🤮
602
4.10%
😱
481
3.28%
👏
479
3.26%
🤬
465
3.17%
🤔
411
2.80%
😁
364
2.48%
🤩
142
0.967%
🤯
136
0.926%
😢
59
0.402%
🎉
44
0.3%
🕊
43
0.293%
👀
38
0.259%
😨
30
0.204%
👻
19
0.129%
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 238 of the 238 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 15,394 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 238 most recent posts we hold, published 4 August 2026 to 3 September 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.
9월 9일 애플 신제품 행사는 단순한 연례 아이폰 발표회가 아니라, 2017년 아이폰 X 이후 가장 중요한 아이폰 이벤트가 될 가능성이 높다
애플 최초의 폴더블 아이폰 공개 예상
iPhone 18 Pro·Pro Max의 대규모 가격 인상 가능성
팀 쿡이 아닌 신임 CEO 존 터너스(John Ternus)가 처음 주도하는 대형 제품 행사
https://www.barrons.com/articles/apple-iphone-launch-event-stock-06e4a794?utm_source=chatgpt.com
브로드컴 컨콜 중
"이제 Anthropic으로 넘어가겠습니다. 2026년에 배치할 1기가와트의 Ironwood를 시작으로, 우리는 Anthropic이 2027년에 TPU v8i의 추가 5기가와트를 배치할 것으로 예상합니다."
"그리고 2028년에는 추가 10기가와트를 제공할 수 있는 명확한 전망이 있습니다."
"Google이 우리를 위해 성장할 것으로 예상되지만, Anthropic은 2027년에 우리의 가장 큰 XPU 고객이 될 것으로 예상되며, 2028년에 이를 유지할 것입니다."
오픈AI 수석과학자 야쿠브 파초키, Astra 추론 불투명성 우려에 대한 해명
혼란스러운 보도로 인해 ‘모니터링 불가능한 모델을 향한 경쟁’이 촉발되는 것을 막고 싶습니다
Astra를 포함한 현재 저희 프론티어 모델들의 연산 그래프 깊이(computation graph depth)는 GPT-4 대비 2배 이내 수준입니다.
오픈AI는 최초의 추론 모델을 개발했을 때부터 Chain-of-Thought(CoT) 모니터링을 보존하고 활용하기 위해 노력해왔습니다.
저희는 이 기법을 매우 중요하게 생각합니다. 이를 통해 모델 정렬이 학습 데이터 분포를 벗어난 상황에서도 어떻게 일반화되는지를 들여다볼 수 있기 때문입니다.
다만 저는 이 기법이 취약하며, 안타깝게도 현재 부정적인 방향으로 가고 있다고 생각합니다. 그 이유는 아키텍처 변화에 국한된 …
Dell 컨콜 중
추론 수요는 이미 학습 수요를 넘어섰으며, 우리 산업 전반에 걸쳐 순수한 추가 수요를 만들어내고 있습니다.
저희는 추론이 만들어내는 토큰 수가 2030년까지 87배 증가해 3,600 quadrillion(360경) 토큰에 이를 것으로 보고 있습니다.
학습 수요 역시 2030년까지 5배 증가해 850 제타플롭스(ZFLOPS)에 이를 것으로 예상합니다.
또한 기업용 에이전틱 AI(Enterprise Agentic AI)가 2028년까지 단일 최대 워크로드가 될 것으로 전망합니다.
Showing the 12 most recent of 238 posts we hold for @cahier_de_market. 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.
Republished by 90 registered channels. The 48 listed are the ones that have forwarded the most posts; the rest are counted here but not each listed.
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 9 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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@cahier_de_market named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@firstadopter named in 1 post, 27 August 2026 – 27 August 2026
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 20 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.
AWAKE - 52주 신고가 모니터링 @awake_realtimeCheck · 22,632 Telegram ranks this channel #37 of 92 here — alongside 91 others — read 20 September 2026
루팡 @bornlupin · 48,199 Telegram ranks this channel #41 of 91 here — alongside 90 others — read 2 September 2026
키움증권 미국주식 톡톡 @kwusa · 36,480 Telegram ranks this channel #42 of 92 here — alongside 91 others — read 2 September 2026
선수촌 @athletes_village · 21,621 Telegram ranks this channel #45 of 88 here — alongside 87 others — read 23 September 2026
엄브렐라리서치 Jay의 주식투자교실 @Jstockclass · 25,947 Telegram ranks this channel #46 of 90 here — alongside 89 others — read 13 September 2026
도PB의 생존투자 @survival_DoPB · 22,243 Telegram ranks this channel #54 of 90 here — alongside 89 others — read 21 September 2026
TNBfolio @TNBfolio · 24,657 Telegram ranks this channel #56 of 93 here — alongside 92 others — read 15 September 2026
시장 이야기 by 제이슨 @bumgore · 35,352 Telegram ranks this channel #57 of 89 here — alongside 88 others — read 2 September 2026
조기주식회 공부방 @EarlyStock1 · 24,432 Telegram ranks this channel #65 of 91 here — alongside 90 others — read 16 September 2026
YIELD & SPREAD @yieldnspread · 22,018 Telegram ranks this channel #71 of 89 here — alongside 88 others — read 21 September 2026
[메리츠 Tech 김선우, 양승수, 김동관] @merITz_tech · 24,059 Telegram ranks this channel #71 of 92 here — alongside 91 others — read 17 September 2026
에테르의 일본&미국 리서치 @aetherjapanresearch · 34,469 Telegram ranks this channel #72 of 89 here — alongside 88 others — read 3 September 2026
하나 중국/신흥국 전략 김경환 @HANAchina · 35,301 Telegram ranks this channel #75 of 92 here — alongside 91 others — read 2 September 2026
서화백의 그림놀이 🚀 @easobi · 23,597 Telegram ranks this channel #79 of 87 here — alongside 86 others — read 18 September 2026
💯똥밭에 굴러도 주식판 @daegurr · 23,375 Telegram ranks this channel #80 of 83 here — alongside 82 others — read 18 September 2026
선진짱 주식공부방 @sunstudy1234 · 54,428 Telegram ranks this channel #82 of 89 here — alongside 88 others — read 2 September 2026
시황맨의 주식이야기 @shmstory · 38,160 Telegram ranks this channel #83 of 89 here — alongside 88 others — read 2 September 2026
타점 읽어주는 여자(타자) @tazastock · 40,320 Telegram ranks this channel #83 of 89 here — alongside 88 others — read 2 September 2026
급등일보 미국주식🇺🇸 속보·매크로·리서치 @FastStockNewsUSA · 40,651 Telegram ranks this channel #85 of 89 here — alongside 88 others — read 2 September 2026
김찰저의 관심과 생각 저장소 @kimcharger · 24,877 Telegram ranks this channel #86 of 88 here — alongside 87 others — read 15 September 2026
그로쓰리서치(Growth Research) [독립리서치] @growthresearch · 40,127 Telegram ranks this channel #88 of 90 here — alongside 89 others — read 2 September 2026
This channel appears in 21 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 25 September 2026 — this
entry's latest reading, not the date you are reading this.
“카이에 de market” (@cahier_de_market), 22,505 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/cahier_de_market.
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.
