Other / unclassifiable — 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 44% 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 5 other registered channels. This entry also sits inside a group of 6 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 (4 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. 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 76 comparable posts for this entry, running 29 July 2026 to 12 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 6 channels, the one whose matching posts we hold earliest is @kkkontemp — 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 13 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 6 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.
67 measurements spanning 99 days, net +3,487. 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 17,173–21,706 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 67
Measured (UTC)
Subscribers
Change
13 Nov 2026, 12:20
21,183
+14
10 Nov 2026, 22:09
21,169
+4
9 Nov 2026, 07:47
21,165
+24
8 Nov 2026, 00:17
21,141
+5
6 Nov 2026, 21:28
21,136
+25
5 Nov 2026, 18:29
21,111
+53
4 Nov 2026, 21:30
21,058
+18
3 Nov 2026, 21:56
21,040
+4
3 Nov 2026, 00:15
21,036
+39
2 Nov 2026, 01:48
20,997
+38
1 Nov 2026, 03:29
20,959
+3
31 Oct 2026, 04:58
20,956
-1
30 Oct 2026, 03:36
20,957
+46
29 Oct 2026, 04:01
20,911
+44
28 Oct 2026, 03:39
20,867
+3
27 Oct 2026, 00:09
20,864
+2
25 Oct 2026, 23:27
20,862
+8
24 Oct 2026, 21:54
20,854
-2
23 Oct 2026, 21:57
20,856
+30
22 Oct 2026, 19:21
20,826
first reading
Engagement
121 posts held, back to 29 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 77 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
67.9%
avg views ÷ 21,183 subscribers
Avg views / post
14,400
52 posts measured
Reaction rate
0.667%
reactions ÷ views · ER floor
Posts in window
52
of 121 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 12 November 2026
Posts held
121 (29 July 2026 – 12 November 2026)
Views total
747,620
Reactions total
4,987
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
13 Nov 2026, 15:46 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
≈1,880
Links
≈481
Lifetime counters from Telegram’s own channel header, read 13 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.
Reaction mix
14,725 reactions across 116 posts, in 13 distinct kinds. The most used accounts for 44.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
6,472
44.0%
👍
3,899
26.5%
👌
630
4.28%
😭
630
4.28%
😱
622
4.22%
🤔
548
3.72%
🔥
547
3.71%
🙏
430
2.92%
👏
324
2.20%
🤬
238
1.62%
💯
151
1.03%
🤯
147
0.998%
✍
87
0.591%
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 121 of the 121 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 15,699 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 121 most recent posts we hold, published 29 July 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: 7 of 52 posts held from the last 30 days (13.5%) 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).
MS 환율 데스크: 아시아 통화 전반의 약세 속 원화 나홀로 강세
오늘 오후 한국 시장에서 흥미로운 디커플링(괴리) 현상이 나타나고 있습니다. 전반적인 달러 강세 기조가 유지되고 코스피(KOSPI)가 약 1% 하락세를 보이는 가운데서도, 원/달러 환율은 전일 종가 1,337.40원에서 하락한 1,329.65원에 거래되고 있습니다.
수급 요인이 환율 하락을 견인하고 있는 것으로 보입니다.
증시 부진에도 불구하고 수출업체들의 네고 물량(달러 매도)이 여전히 활발하게 출회되고 있으며, 역외(offshore) 시장 참가자들 역시 달러 매도에 가세했습니다. 또한 외국인 주식 자금 유입에 따른 원화 수요도 아시아 통화 전반을 짓누르는 약세 압력으로부터 원화를 방어하는 데 힘을 보태고 있습니다.
---
한쪽은 외화벌이를 너무 많이해서 문제
아시아 네오클라우드 사업자에 따르면, 최근의 엔비디아 움직임은 중국 데이터센터의 경쟁력 약화와 AI 칩 우회 반입 경로 차단 규제 심화로 이어질 전망
동시에 미국의 데이터센터 인프라 전략이 강화되면서, 중국을 제외한 아시아 지역 데이터센터 공급자들이 반사이익을 얻을수 있는 경로
다만, 다음과 같은 구조적 한계와 리스크도 공존
미국의 테넌트(입주사) 실사 및 규제 개입이 동반되고 있어 단순 수혜를 넘어선 컴플라이언스 부담
빅테크 기업들이 GPU를 직접 구매해 데이터센터에 입주하는 방식이 확대될 경우, 빅테크의 가격 협상력이 강해지면서 네오클라우드 사업자의 수익성(마진)이 압박받을 수 있는 구조
결국 미·중 AI 패권 다툼 속에서 시장의 셈법이 조금 바뀐?
제목과 논리는 비슷한데 내용이 더 알찬
https://t.me/kkkontemp/2134
https://t.me/kkkontemp/2725
https://t.me/kkkontemp/2730
MS: 한강의 기적을 넘어: 한국의 르네상스
CIO(최고투자책임자)에게 보내는 메시지
코리아 르네상스: 향후 5년간 주식 시장 최고의 스토리
한국의 투자 환경이 변화하고 있습니다. AI 주도의 성장, 새롭게 살아나는 산업 수요, 그리고 구조 개혁이 맞물려 한국 기업의 자기자본이익률(ROE)을 지속적으로 끌어올리고 한국 증시의 장기적인 재평가(re-rating)를 이끌 수 있습니다.
당사는 과거 약 8% 수준이던 한국 기업 부문의 ROE가 2030년까지 14% 이상으로 상승할 것으로 전망하며, 자본시장의 심화와 기업 지배구조 개선은 자기자본비용(…
그래도 AI군 전체적으로 얼마전까지 4분기 ARR 부진 소식에 민감하게 반응했을텐데 많이 팔긴 팔았나 봄
앤트로픽 IPO 콜중:
2026년 4분기 부진 및 원인: 4분기 매출 가이드라인은 193억 달러(연환산 시 약 770억 달러)로, 10월 연간반복매출(ARR)인 810억 달러에 못 미칩니다. 경영진은 그 원인으로 1) 홀리데이 시즌 계절성, 2) 증류(Distillation) 매출 제거 (7~9월 월간 매출의 각각 10%, 7%, 2%에서 현재 2% 미만으로 감소), 3) 가격 인하 (Opus 5.5 등 동급 기준 약 40% 저렴해짐에 따라 계정 마이그레이션 과정에서 발생하는 일시적 매출 둔화) 등 세 가지를 꼽았습니다. 애널리스트들은 이 외에 다른 일회성 요인은 언급되지 않았다고 밝혔습니다.
컴퓨팅 효율성: 경영진의 핵심 메시지입니…
우리는 지난 6년간(코로나19 위기 이후) 통화량 증가가 인플레이션을 유발한다는 사실을 경험해 왔다.
각국 정부가 전시상황과 같은 재정을 집행하는 '재정 우위(Fiscal Dominance)' 기조 속에서, 지금까지 경기침체 리스크보다 인플레이션 리스크가 더 크게 대두되고 있는 상황이다.
특히 미국의 경우, 재정 정책의 효과가 올해 정점에 달한 후 내년부터는 점차 둔화될 전망이다.
차트 1: 미국 재정효과 전망
이에 따라 '재정 효과 축소 ➔ 유동성(통화량) 압박 완화 ➔ 인플레이션 리스크 감소 ➔ 본격적인 금리 인하 사이클 진입'으로 이어지며 주식 시장에는 우호적인 환경이 조성될 것으로 보여진다.
물론, AI 인프라 투자가 둔화될 경우 전체 성장이 제동 걸리면서 경기침체 리스크가 다시 고개 들 수 있다는 변수는 경계해야 한다고 보여…
UBS CPI 리뷰 요약
CPI 리뷰 요약
그럼에도 불구하고, 추정된 12개월 근원 CPI 변동률 2.76%는 1년 전 이 시점에 당사가 전망했던 3.0%의 근원 CPI 물가 상승률보다는 여전히 0.25%포인트 낮은 수준입니다.
이번 달의 큰 놀라움은 여가 서비스와 비항공 교통 서비스(둘 다 10월에 +0.9% 상승)의 물가 상승세가 강해졌고, 가정용 가구 및 용품(+0.7%)도 함께 올랐다는 점입니다. 이 세 가지 그룹이 합쳐져 근원 CPI 상승에 기여한 폭은 당사 전망치를 0.09%포인트 상회했습니다. 가정용 가구의 강세는 수입 물가의 가파른 상승세와 관련이 있을 수 있으며, 소비재 수입 물가(관세 제외)의 12개월 변동률은 2025년 5월 -1.3%에서 현재 +2.7%로 급반등했습니다.
컴퓨터와 소프트웨어 모두 10월에는 상승세가 …
내일 밤 미국 CPI (CPI 맛집 UBS 참고)
헤드라인 CPI: UBS 미국 경제팀 0.26%, 컨센서스 0.29%, UBS 나우캐스트(Nowcast) 0.32% 당초 이번 주 초의 전체 CPI 프리뷰에서 0.28%였던 10월 헤드라인 CPI 전망치는 현재 0.26%로 소폭 하향 조정되었습니다. 블룸버그 컨센서스 평균은 0.29%를 유지하고 있습니다.
근원 CPI: UBS 미국 경제팀 0.23%, 블룸버그 컨센서스 0.24%, UBS 나우캐스트 0.22% 의류 가격 하락과 중고차 가격 상승 등 세부 항목 일부에 변동이 있었으나, 다음 주 발표될 근원 CPI 전망치는 0.23%로 수정 없이 유지되었습니다. 다음 달 11월 발표를 위한 당사의 근원 CPI 전망치는 0.17%에서 0.13%로 낮아졌습니다.
어도비(Adobe)와 JD파워, …
❤27👍14🤔6🔥2👌1
Showing the 12 most recent of 121 posts we hold for @kkkontemp. 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
@kkkontemp 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
27 August 2026
Most recent edit
27 August 2026
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republished by 94 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 19 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 30 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.
YIELD & SPREAD @yieldnspread · 22,083 Telegram ranks this channel #17 of 89 here — alongside 88 others — read 21 September 2026
서화백의 그림놀이 🚀 @easobi · 23,419 Telegram ranks this channel #28 of 87 here — alongside 86 others — read 18 September 2026
[하나증권 ETF] 박승진 @globaletfi · 20,610 Telegram ranks this channel #30 of 86 here — alongside 85 others — read 28 September 2026
기술적 분석으로 보는 주식시장 (by 소라게아빠) @hermitcrab41_1 · 21,506 Telegram ranks this channel #32 of 81 here — alongside 80 others — read 28 September 2026
Rafiki research @rafikiresearch · 20,656 Telegram ranks this channel #32 of 88 here — alongside 87 others — read 26 September 2026
회색인간의 매크로 + 투자 @mistergray_11 · 19,794 Telegram ranks this channel #34 of 78 here — alongside 77 others — read 1 October 2026
선수촌 @athletes_village · 22,145 Telegram ranks this channel #37 of 88 here — alongside 87 others — read 23 September 2026
루팡 @bornlupin · 49,268 Telegram ranks this channel #46 of 91 here — alongside 90 others — read 2 September 2026
Market News Feed @marketfeed · 39,766 Telegram ranks this channel #50 of 84 here — alongside 83 others — read 31 August 2026
키움증권 미국주식 톡톡 @kwusa · 36,188 Telegram ranks this channel #51 of 92 here — alongside 91 others — read 2 September 2026
TNBfolio @TNBfolio · 25,163 Telegram ranks this channel #53 of 93 here — alongside 92 others — read 15 September 2026
도PB의 생존투자 @survival_DoPB · 22,047 Telegram ranks this channel #55 of 90 here — alongside 89 others — read 21 September 2026
엄브렐라리서치 Jay의 주식투자교실 @Jstockclass · 26,508 Telegram ranks this channel #62 of 90 here — alongside 89 others — read 13 September 2026
카이에 de market @cahier_de_market · 23,110 Telegram ranks this channel #68 of 85 here — alongside 84 others — read 20 September 2026
하나 중국/신흥국 전략 김경환 @HANAchina · 35,273 Telegram ranks this channel #81 of 92 here — alongside 91 others — read 2 September 2026
유진투자증권 코스닥벤처팀 @SmallCap · 22,982 Telegram ranks this channel #90 of 96 here — alongside 95 others — read 19 September 2026
This channel appears in 17 seed channels' Telegram-generated recommendation lists in total, of which the 16 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 13 November 2026 — this
entry's latest reading, not the date you are reading this.
“KK Kontemporaries” (@kkkontemp), 21,183 subscribers as measured 13 November 2026. Telegram Register, tgregister.com/channel/kkkontemp.
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.
