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 11 August 2026 and assigned it the closest of 31 fixed categories, at 62% 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 (2 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. 1 of the 1 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 25 comparable posts for this entry, running 2 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 @macro_OK — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_mutual, 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 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.
4 measurements spanning 6 days, net +60. 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 2,133–2,211 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 12:27
2,202
+6
9 Aug 2026, 10:32
2,196
+54
6 Aug 2026, 06:07
2,142
no change
6 Aug 2026, 04:49
2,142
first reading
Engagement
29 posts held, back to 2 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 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
86.5%
avg views ÷ 2,202 subscribers
Avg views / post
1,900
29 posts measured
Reaction rate
0.487%
reactions ÷ views · ER floor
Posts in window
29
of 29 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 7 August 2026
Posts held
29 (2 August 2026 – 7 August 2026)
Views total
55,214
Reactions total
269
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 16:55 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
199 reactions across 20 posts, in 6 distinct kinds. The most used accounts for 62.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
124
62.3%
👍
43
21.6%
🙏
13
6.53%
👌
9
4.52%
✍
6
3.02%
🔥
4
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 29 of the 29 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 269reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 29 most recent posts we hold, published 2 August 2026 to 7 August 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.
_해당 네프콘...
지난 고용보고서뿐만 아니라 이번주에 발표된 ADP nfp, ISM 서비스업 고용지수를 보면, 지난 2년간 미국 연준을 애먹였던 ‘노동시장’이 아직 완전히 회복되지 않았다고 지적했었습니다.
참고하시면 도움될 듯합니다.
7월 고용보고서를 통해, 미국 노동시장이 완전히 회복되지 않았음을 다시 확인하게 되네요...
앞으로 노동시장이 어떨지 계속 지켜보겠습니다만, 예상보다 부진한 고용지표에 따라...
발표 직후,
1) 9월 FOMC 금리 인상 확률: 발표 전 약 55~62% → 발표 후 약 43%로 하락 (CME fed watch)
2)10년물 국채금리 4.67%→4.6%대로 소폭 하락
https://t.me/macro_OK 숏박스
미국 고용 쇼크
네프콘에서 우려된다고 언급했던 일이 진짜 일어났네요...
• 비농업고용: -23,000명 (예상 +83,000명 대비 큰 쇼크)
• 5-6월 합산 하향 수정: -103,000명
• 실업률: 4.1% (6월 4.2%에서 하락) — 단, 구직 포기자 증가로 인한 하락(고용 개선 아님)
• 장기실업(6개월+) 비중: 실업자의 약 30%, 팬데믹 회복기 이후 최고
https://t.me/macro_OK 숏박스
◆연준 회의 이후... 왜 리치먼드 연은 총재가 "현재 노동시장이 의미있게 강화되었다고 보기 어렵다"는 식으로 말했을까요?
오늘있을 고용보고서와 관련해서, 이번주 노동시장에서 나왔던 신호🚨들을 정리한 글입니다.
최근 매크로가 중요해지는 만큼 네프콘 컨텐츠도 무료로 활발히 운영하려고 합니다. 많은 관심 부탁드립니다.
매크로 케이와 함께 매크로 열심히 공부해서, 매크로 이슈를 이해하고 대응할 수 있도록 함께 성장합시다! 🔥🫡
하트 눌려주시면 큰 힘이 됩니다! 감사합니다!
https://naver.me/xxoIneon
[오늘 밤 미국 고용보고서, 뭘 봐야 할까]
최근 미국 노동시장은 “당장 무너졌다”기보다, 채용의 힘이 조금씩 약해지는 신호가 쌓이는 상황.
지난 고용보고서 뿐만 아니라
이번 주 발표된 노동지표도 비슷한 방향.
1. ADP 민간고용 +4.4만 명
민간고용은 증가했지만 증가폭은 약한 편
계절조정 전 기준으로는 보통 늘던 7월 고용이 올해는 6월보다 감소
2. ISM 서비스업 고용 47.4
서비스업 활동과 신규주문은 견조한 반면 고용은 다시 위축 구간
즉 경기가 바로 꺾였다기보다 기업들이 신규채용에는 여전히 신중한 모습
◆봐야할 것
오늘 밤은 NFP 하나만 보면 안 됩니다.
① NFP + 이전치 수정
② 실업률 + 노동참가율 + 취업자
③ 임금
④ 평균 근로시간
◆해석
1) 고용·임금 모두 강함 → 추가 금리 인상 위험 ↑
2) 고…
최근 마이클 버리가 언급한 VIX가 뭔지 아십니까?
혹시나 VIX가 뭔지 잘 모르신다면 쇼츠를 통해 확인해보세요! 🔥
다음엔, 버리가 VIX에 대해 뭐라고 했는지도 다뤄보겠습니다.
https://youtube.com/shorts/g50qEQO3wk4?si=uafvDDocRd3yL2w4
[한국 7월 물가 둔화 및 통화정책 동향]
1. 7월 소비자물가(CPI) 동향 및 평가
물가 상승률 둔화:
_7월 소비자물가지수(CPI)가 전년 동기 대비 2.8% 상승을 기록
_6월(3.2%) 대비 하락 및 시장 예상치(3.0%)를 밑돎 (4월 이후 최저치).
주요 원인: 연료 및 운송비 상승세 둔화가 전체 지표의 안정세를 견인함.
근본적 압박 지속:
변동성 품목을 제외한 근원 인플레이션은 2.6%를 기록해, 표면적인 지표 둔화에도 불구하고 경제 기저의 물가 상승 압력은 여전히 잠재해 있다는 분석.
————
2. 한국은행(BOK) 통화정책 및 경기 판단
긴축 기조 유지:
한은은 지난달 기준금리를 2.75%로 인상한 데 이어, 견조한 2분기 경제성장률(전분기 대비 0.6% 성장)과 반도체 수출 호조를 바탕으로 추가적인 통화 긴축(인…
[오늘 자, 연준 의원의 발언 정리]
연준의 무살렘, 인플레이션에 대한 의미 있는 억제 촉구
_(올해 정책 결정 투표권 없는 무살레의원) 지난주 FOMC에서 금리 0.25%p인상했어야 한다는 입장
1. 생산성
_생산성 증가세가 강해질 가능성을 기다리는 동안 높은 인플레이션을 용인할 여유가 없다
(긴축통화정책이 경제 위축시켜 생산성 성장을 저해할 수 있으나, 연준의 인플레 관리 능력에 대한 신뢰를 잃는 건 더 큰 문제가 된다고 발언)
_중앙은행이 장기적인 성장에 가장 크게 기여하는 부분
: 기업들이 경제 성장을 촉진하는 투자와 혁신을 계획할 수 있도록 안정적인 물가 환경을 제공하는 것
————
2. 인플레 유발 원인
_AI기반 수요뿐만 아니라 '공급충격'이 인플레 부추김
_연준은 일련의 공급 충격을 예측할 수 있는지 검토 중이며, 올가…
AI LLM 모델 지수 와 메모리 3사 주가 흐름.
HBM 채택율을 낮추는 움직임이 NVDA나 ASIC 업체들 중심으로 나타나고 있습니다 .
다른 것은 몰라도 HBM 가격의 peak는 확인했다는 것은 확실해 보입니다.
경제학에서 말하는 "수요"라는 것을 요즘은 너무 마구잡이로 쓰는 경향이 있지요.
"수요, 구매력이 뒷받침 되는" 이게 올바른 정의 입니다.
증권사나, 주식쟁이가 HBM 수요가 무지막지하다고 하는 것은 엄밀한 의미에서 "수요"로 보긴 어렵습니다.
NVDA, ASIC 이 HBM 채택률을 줄이고, 효율성을 높이려는 움직임을 보이고 있습니다.
지금 삼전, 닉스 주가의 하락이 단순한 조정이 되려면, 몇 가지가 더 필요합니다.
유튜브로 영상을 한번 찍어 보겠습니다. 이번에는 진짜 짧게..
오늘은 크게 반등해주면 좋겠습니다.…
_수출업체들이 달러 수익을 자국 통화로 전환,
한국 원화가치는 거의 10개월 만에 최고 수준으로 상승.
_특히 SK하이닉스가 265억 달러 규모의 미국 증시 상장을 통해 조달한 자금을 한국 내 사업에 투자할 것이라고 밝힌 점이 두드러짐.
_지난주 한국 당국이 달러 매도에 나서 엔화 가치를 끌어올리는 외환시장 개입을 실시하며, 한국 외환 당국도 미국 및 일본 외환 당국과 긴밀히 협력 중임.
_단기적으로 달러당 1410-1440사이에 움직일 가능성 높다는 애널 전망.
from. Bloomberg news
https://t.me/macro_OK 숏박스
아까 접속이 안되시는 분들도 있다고 하셨는데, 이젠 확인 가능하십니다.
감사합니다☺️ 매크로 공부 파이팅!🔥
❤10
Showing the 12 most recent of 29 posts we hold for @macro_OK. 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.
Citation-graph rank
Citation-graph rank — 466,384 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“숏박스 (Macro OK)” (@macro_OK), 2,202 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/macro_OK.
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.