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 why it has no browse page of its own yet.
Growth
36 measurements spanning 51 days, net +2,157. 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 17,372–20,177 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 36
Measured (UTC)
Subscribers
Change
26 Sept 2026, 11:38
19,853
+27
19 Sept 2026, 11:21
19,826
+48
16 Sept 2026, 20:58
19,778
+75
14 Sept 2026, 22:02
19,703
+40
13 Sept 2026, 11:21
19,663
+253
11 Sept 2026, 18:41
19,410
+94
9 Sept 2026, 07:56
19,316
+78
5 Sept 2026, 23:21
19,238
+81
3 Sept 2026, 19:57
19,157
-1
2 Sept 2026, 14:08
19,158
+39
1 Sept 2026, 15:15
19,119
+31
31 Aug 2026, 17:34
19,088
+31
30 Aug 2026, 18:13
19,057
+9
29 Aug 2026, 17:57
19,048
+45
28 Aug 2026, 17:41
19,003
+65
27 Aug 2026, 16:05
18,938
+39
26 Aug 2026, 14:09
18,899
+55
25 Aug 2026, 14:59
18,844
+23
24 Aug 2026, 16:27
18,821
+15
22 Aug 2026, 21:34
18,806
first reading
Engagement
69 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 55 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
67.9%
avg views ÷ 19,853 subscribers
Avg views / post
13,500
9 posts measured
Reaction rate
0.777%
reactions ÷ views · ER floor
Posts in window
9
of 69 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 2 September 2026
Posts held
69 (29 July 2026 – 2 September 2026)
Views total
121,330
Reactions total
943
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 01:27 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
9,738 reactions across 64 posts, in 13 distinct kinds. The most used accounts for 45.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
4,392
45.1%
👍
2,606
26.8%
👌
550
5.65%
🔥
423
4.34%
😱
416
4.27%
🤔
293
3.01%
👏
267
2.74%
😭
243
2.50%
🙏
165
1.69%
🤬
150
1.54%
🤯
84
0.863%
💯
82
0.842%
✍
67
0.688%
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 69 of the 69 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 10,712 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 69 most recent posts we hold, published 29 July 2026 to 2 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.
변화는 하루만에 이루어지지 않지만..
단기 반짝 이익 성장 사이클을 넘어 구조적인 대한민국 경제/금융시장 리레이팅을 위해 상법 개정 (이사회 충실 의무) 외 풀어야 할 숙제
1) 상속세 개편 (코리아 할인 핵심)
2) 순환출자 해소
3) 기업들의 미래 성장 전략적 투자
4) 소득세 단일 세율화
5) 글로벌 LP들의 국내 자산운용사 직접 출자를 위한 규제완화
당연히 하나하나가 난제. 하지만 정책 결정자들이 진정으로 부강한 대한민국의 미래를 바란다면 결단해야 할 필수 과제들
과거에는 구조 개혁의 파급력이 상대적으로 미미했을수도. 하지만 지금은 다행히 AI 투자 사이클이 도래하면서 국내 기업들이 어마어마한 이익을 거두고 있는 골든타임. 이익 스케일이 뒷받침되는 지금 변화를 꾀해야 그 파급효과도 압도적일 것
우리가 이런 꿈을 꿀 수 있는 바…
미국 주식 시장 기준 기관 주식 포지셔닝 관점 (골드만):
현황: 9월 계절적 약세를 기대하며 포지션을 대거 비운 상태 (2023년 이후 ‘해방의 날’ 제외 최저 수준).
9월 하반기부터 연말 랠리를 대비한 재매수(Re-leveraging) 수요가 대기 중이나, 만약 그 전에 시장이 매크로 불확실성을 뚫고 상승하거나 견고하게 버틴다면 기관들의 비중 확대 시점이 강제로 당겨질 수 있는 상방 비대칭성 구조
엔더슨 호로위츠:
지금 우리가 겪고 있는 투자 환경은 역사상 그 어떤 시대와도 다릅니다. 보통 투자자들은 한 세대에 하나 나올까 말까 한 기술 채용의 'S-커브(초기 성장을 지나 폭발적으로 성장하는 구간)'를 그 시작점부터 잡을 수 있기를 간절히 바라고 희망합니다. 하지만 오늘날 우리는 무려 최소 6가지의 메가트렌드가 동시에 분출하는 광경을 목격하고 있습니다.
• 기업형 AI: 기업들은 조직의 모든 영역에 AI 툴을 도입하고 있습니다. 연산 능력(Compute)을 비즈니스 성과로 전환하려는 수요에는 상한선이 없다고 확신합니다.
• 소비자용 AI: ChatGPT의 형태가 검색 엔진을 대체하기 시작한 것을 제외하면, 소비자용 AI는 아직 시작 단계에 불과합니다. 하지만 때가 되면 시장을 근본적으로 뒤흔드는 혁신이 일어날 것입니다.
• 아메리…
씨티: 반도체(SEMIS) 피드백
지난주 컨퍼런스에서 다양한 투자자들과 나눈 대화에 따르면, 반도체 섹터에 대한 투심은 여전히 매우 비관적(bearish)입니다. 흥미롭게도 미지근한 주가 흐름(지난주 NVDA 등)으로 인해 테크 전문 투자자(tech spec)보다 일반 투자자(generalist)가 더 신중한 모습을 보이고 있습니다. 악재에는 훨씬 민감하게 반응하는 반면, 호재는 갈수록 매도 기회로 활용되는 분위기입니다. 컨퍼런스 기간 중 AI 반도체 관련 부작용이나 음성적인 데이터 포인트는 찾을 수 없었으나, 금요일 필라델피아 반도체 지수(SOX)는 3.5% 하락했습니다. 엔비디아(NVDA)는 파격적인 2027년 매출 가이던스 제시에도 불구하고 상승세를 이어가지 못한 반면, 소프트웨어 지수(IGV)는 특히 세일즈포스(CRM) 실적 발표 이…
단기적으로는 시장이 금리 인상 확률을 높게 반영하며 단기 금리가 상승하고, 이에 따른 채권 베어 플래트닝(Bear Flattening) 정국에서 트레이딩 기회가 제공
하지만 올해 이미 수차례 경험했듯, 시장의 금리 인상 기대감은 매번 소멸 되었음. 따라서 기간 프리미엄 (Term Premium)이 하락세를 유지하는 가운데, 금리 인상 기대감 소멸로 다시 단기물 약세로 인한 베어 플래트닝이 불 플래트닝(Bull Flattening)이나 불 스티프닝(Bull Steepening)으로 전환된다면, 이는 또 한 번의 위험 자산의 랠리를 위한 세팅이 될 것
(기간 프리미엄의 감소로 인해 국채 금리가 하락하는 상황은, 역사적으로 주식 등 위험 자산 상승에 최고의 호재)
Buy the dip 접근이 더 옳을듯
정리 잘해주신듯
https://x.com/laylaperfume/status/2093344786451497385?s=46&t=xqAS5Xg0kII_7JxezPKEHw
개인적 관점: 전반적으로 매파와 비둘기파 경계선에 있는 약간의 매파(Hawkish) 톤이었으나, 시장을 놀라게 할 만한 충격은 없었음. (좋게 말하면 신중함, 나쁘게 말하면 알맹이 없는 '워드 샐러드'.)
다만 연설 초반 '등산(hike)' 비유에 등장한 단어에 트레이딩 알고리즘(기계)들이 좀 더 예민하게 반응한 것으로 보임
매크로 이벤트들의 당일 리액션은 프리 포지션된 포지션들의 대응 결과 가능성 커서 그 이후 흐름이 더 중요
https://t.me/kkkontemp/2619
*CHINA TO IMPLEMENT PROACTIVE MACROECONOMIC POLICIES IN 2H: MOF
당국 입장에서도 어떤 식으로든 대응에 나설 수밖에 없어 보이긴
차트: 중국 신용자극지수 (GDP 대비 신규 신용(대출 등)의 증감 비율 변화를 나타내는 지표)
👍34❤13👌4🔥2
Showing the 12 most recent of 69 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 86 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 16 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.
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 · 22,018 Telegram ranks this channel #17 of 89 here — alongside 88 others — read 21 September 2026
서화백의 그림놀이 🚀 @easobi · 23,597 Telegram ranks this channel #28 of 87 here — alongside 86 others — read 18 September 2026
[하나 Global ETF] 박승진 @globaletfi · 20,222 Telegram ranks this channel #30 of 86 here — alongside 85 others — read 28 September 2026
기술적 분석으로 보는 주식시장 (by 소라게아빠) @hermitcrab41_1 · 20,334 Telegram ranks this channel #32 of 81 here — alongside 80 others — read 28 September 2026
Rafiki research @rafikiresearch · 20,596 Telegram ranks this channel #32 of 88 here — alongside 87 others — read 26 September 2026
선수촌 @athletes_village · 21,621 Telegram ranks this channel #37 of 88 here — alongside 87 others — read 23 September 2026
루팡 @bornlupin · 48,199 Telegram ranks this channel #46 of 91 here — alongside 90 others — read 2 September 2026
Market News Feed @marketfeed · 38,760 Telegram ranks this channel #50 of 84 here — alongside 83 others — read 31 August 2026
키움증권 미국주식 톡톡 @kwusa · 36,480 Telegram ranks this channel #51 of 92 here — alongside 91 others — read 2 September 2026
TNBfolio @TNBfolio · 24,657 Telegram ranks this channel #53 of 93 here — alongside 92 others — read 15 September 2026
도PB의 생존투자 @survival_DoPB · 22,243 Telegram ranks this channel #55 of 90 here — alongside 89 others — read 21 September 2026
엄브렐라리서치 Jay의 주식투자교실 @Jstockclass · 26,067 Telegram ranks this channel #62 of 90 here — alongside 89 others — read 13 September 2026
에테르의 일본&미국 리서치 @aetherjapanresearch · 34,509 Telegram ranks this channel #66 of 89 here — alongside 88 others — read 3 September 2026
카이에 de market @cahier_de_market · 22,505 Telegram ranks this channel #68 of 85 here — alongside 84 others — read 20 September 2026
하나 중국/신흥국 전략 김경환 @HANAchina · 35,301 Telegram ranks this channel #81 of 92 here — alongside 91 others — read 2 September 2026
유진투자증권 코스닥벤처팀 @SmallCap · 23,176 Telegram ranks this channel #90 of 96 here — alongside 95 others — read 19 September 2026
This channel appears in 16 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.
“KK Kontemporaries” (@kkkontemp), 19,853 subscribers as measured 26 September 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.