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 97% 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. They sit inside a group of 3 channels that share the same post bodies 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 (5 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 39 comparable posts for this entry, running 5 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 3, the earliest publisher we hold is @KiwoomResearch. That is a statement about our reading window, not a claim of authorship.
Recorded under the key 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 2 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.
34 measurements spanning 51 days, net -372. 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 36,424–36,908 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)
Subscribers
Change
26 Sept 2026, 02:20
36,480
-48
19 Sept 2026, 06:21
36,528
-23
16 Sept 2026, 18:01
36,551
-11
14 Sept 2026, 20:20
36,562
-11
13 Sept 2026, 06:55
36,573
-13
11 Sept 2026, 10:18
36,586
-31
8 Sept 2026, 19:38
36,617
-20
5 Sept 2026, 11:41
36,637
-8
3 Sept 2026, 14:19
36,645
+5
2 Sept 2026, 08:22
36,640
-6
1 Sept 2026, 05:07
36,646
-10
31 Aug 2026, 07:07
36,656
-16
30 Aug 2026, 08:16
36,672
-12
29 Aug 2026, 06:23
36,684
-4
28 Aug 2026, 09:06
36,688
-20
27 Aug 2026, 07:42
36,708
-11
26 Aug 2026, 07:42
36,719
-4
25 Aug 2026, 10:06
36,723
-28
24 Aug 2026, 07:16
36,751
-26
20 Aug 2026, 22:47
36,777
first reading
Engagement
537 posts held, back to 5 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 89 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
4.53%
avg views ÷ 36,480 subscribers
Avg views / post
1,650
185 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
185
of 537 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 25 September 2026
Posts held
537 (5 August 2026 – 25 September 2026)
Views total
305,976
Reactions total
—
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
27 Sept 2026, 03:51 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
≈606
Links
≈6,980
Lifetime counters from Telegram’s own channel header, read 27 September 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.
제목 : 메타 뮤즈, 역대 최대의 소비자용 인공지능 앱 될 잠재력 가져 - JPM *연합인포맥스*
제이피모간체이스의 Doug Anmuth 애널리스트는 메타 플랫폼스(NAS: META)의 뮤즈가 역대 최대의 소비자용 인공지능 앱이 될 수 있다고 예상했다. “메타의 개인용 인공지능 에이전트 뮤즈(Muse)는 지난 9월 8일 출시 이후, 약 열흘 만에 미국 iOS 앱스토어 다운로드 건수 1위를 차지했다. 출시 이후 12일간 iOS에서의 다운로드 건수는 143만 건인데, 이는 ▲ 오픈AI의 챗GPT가 동기간 137만 건 다운로드를 기록한 것을 상회한다”고 밝혔다. 또한 “뮤즈의 일간 활성사용자(DAU)는 약 55.7만 명으…
제목 : 메타, 뮤즈에 ‘인간 컨시어지’ 투입 테스트 *연합인포맥스*
씨킹알파는 “메타 플랫폼스(NAS: META)가 앱스토어 정상에 오른 새로운 AI 앱 ‘뮤즈’에서 ‘인간 컨시어지’를 활용하는 방안을 내부 테스트하고 있다”고 주요 외신을 인용해 보도했다. “주요 외신은 사내 게시물을 인용해 뮤즈가 사용자 요청에 따라 수행하는 일부 전화 업무를 외부 계약직 인력이 대신 처리하는 방안을 테스트하고 있다고 전했다. 메타는 지난주 직원들에게 해당 테스트를 알린 것으로 전해졌다”고 설명했다. 다만 “일부 직원들은 사람이 전화 업무를 처리할 경우 사회보장번호나 신용카드 정보 등 민감한…
제목 : 마이크로소프트, Xbox 조직 추가 개편 *연합인포맥스*
씨킹알파는 “마이크로소프트(NAS: MSFT)의 Xbox 사업부가 268명을 감원하고 여러 게임 스튜디오를 재편한다. 버라이어티가 인용한 직원 메모에 따르면 액티비전은 차기 ‘헤일로’ 타이틀 개발을 총괄하게 된다”고 보도했다. “Xbox의 맷 부티 최고콘텐츠책임자는 22일(화) 메모를 통해 이번 감원이 헤일로 스튜디오와 기타 자체 게임 스튜디오, Xbox 게임 스튜디오의 경영진 및 중앙 조직에 영향을 미친다고 밝혔다”고 설명했다. “조직개편에 따라 액티비전은 월드스 엣지와 레어도 총괄하게 된다. 차기…
제목 : 온홀딩, ’29년 매출 $70억 목표…$10억 자사주 매입 *연합인포맥스*
바론스는 “온홀딩(NYS: ONON)은 22일(화) 투자자의 날에서 새로운 중기 재무목표를 제시하고 FY26('26년 1~12월) 가이던스를 재확인한이후 주가가 급등했다”고 보도했다. “온홀딩은 연간 매출 성장률이 10% 후반대에 이를 것으로 예상했으며, '29년까지 최소 56억 스위스프랑(약 70억 달러)의 매출을 달성한다는 목표를 제시했다. 경영진은 FY26 전망도 재확인했으며, '23년 제시했던 기존 다년간 목표를 초과 달성할 수 있는 궤도에 있다고 밝혔다”고 설명했다. “경영진은…
제목 : 페이팔, 메타 뮤즈 쇼핑·결제 지원…AI 커머스 협력 확대 *연합인포맥스*
바론스는 "페이팔(NAS:PYPL)이 메타 플랫폼스(NAS:META)의 뮤즈를 통한 쇼핑과 결제를 지원한다"고 보도했다. “페이팔 고객은 메타 플랫폼스의 인공지능(AI) 에이전트 뮤즈를 이용해 상품을 검색하고 결제할 수 있게 된다. 이번 협력으로 뮤즈는 페이팔의 글로벌 가맹점 네트워크를 활용해 상품 검색부터 결제까지 수행할 수 있게 됐다”고 설명했다. 이어 “페이팔은 AI 기반 커머스 영역으로 결제 인프라를 확대하고 있다. 뮤즈는 예약, 주문, 이메일 작성 등 사용자를 대신해 여러 작업을수행하는 AI 에이전트로 지난…
Showing the 12 most recent of 537 posts we hold for @kwusa. 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 2 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.
신영증권 박소연 @sypark_strategy · 29,465 Telegram ranks this channel #31 of 91 here — alongside 90 others — read 8 September 2026
유진투자증권 코스닥벤처팀 @SmallCap · 23,176 Telegram ranks this channel #77 of 96 here — alongside 95 others — read 19 September 2026
하나 중국/신흥국 전략 김경환 @HANAchina · 35,301 Telegram ranks this channel #90 of 92 here — alongside 91 others — read 2 September 2026
This channel appears in 3 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 26 September 2026 — this
entry's latest reading, not the date you are reading this.
“키움증권 미국주식 톡톡” (@kwusa), 36,480 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/kwusa.
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.
