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 57% 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 32 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 2, the earliest publisher we hold is @macro_OK. 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.
8 measurements spanning 6 days, net -10. 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 17,476–17,499 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 13:45
17,479
-1
11 Aug 2026, 15:53
17,480
-4
10 Aug 2026, 18:28
17,484
-4
9 Aug 2026, 17:42
17,488
-2
8 Aug 2026, 17:41
17,490
-4
7 Aug 2026, 18:17
17,494
-2
6 Aug 2026, 14:51
17,496
+7
6 Aug 2026, 04:48
17,489
first reading
Engagement
127 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 16 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
8.18%
avg views ÷ 17,479 subscribers
Avg views / post
1,430
127 posts measured
Reaction rate
0.388%
reactions ÷ views · ER floor
Posts in window
127
of 127 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. It is computed over the 81 of 127 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 12 August 2026
Posts held
127 (5 August 2026 – 12 August 2026)
Views total
181,679
Reactions total
462
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 09:44 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
Video runtime
20s
Average length
20s
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
424 reactions across 63 posts, in 20 distinct kinds. The most used accounts for 73.8% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
313
73.8%
👍
27
6.37%
🤔
16
3.77%
❤🔥
12
2.83%
😇
9
2.12%
😁
6
1.42%
💯
5
1.18%
😐
5
1.18%
🎃
4
0.943%
🤣
4
0.943%
🤨
4
0.943%
🥴
4
0.943%
✍
3
0.708%
😨
3
0.708%
🥱
3
0.708%
👀
2
0.472%
🔥
1
0.236%
😴
1
0.236%
🙏
1
0.236%
🥰
1
0.236%
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 81 of the 127 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 462reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 127 most recent posts we hold, published 5 August 2026 to 12 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.
◆엔비디아, 월가와 5,000억 달러 이상의 AI 금융 플랫폼 추진 — 8월 11일
_GPU·서버 장비 등을 담보로 AI 인프라 구매자에게 자금을 공급하는 구조
_AI 투자가 기업의 자체 현금뿐 아니라 금융기관의 신용공급으로 확장되고 있다는 점에서 글의 문제의식과 직접 연결
https://www.reuters.com/commentary/breakingviews/jensen-huang-takes-wheel-500-bln-ai-bandwagon-2026-08-11/
◆오라클, AI 투자 확대로 신용등급 부담 확대 — 8월 4일
_오라클의 S&P 신용등급은 투기등급보다 한 단계 높은 BBB-까지 하락
_데이터센터 리스는 15~19년인 반면 고객계약은 최대 5년 수준. 수요가 기대만큼 이어지지 않을 경우 장기 리스 약정과 차입 부담이 커질 수 있는…
최근 Credit관련 이슈가 커지는 듯합니다.
그렇기에 지금의 AI붐이 닷컴버블때 보단 2008년 주택시장 때 양상과는 닮진 않았을까?의문이 드는데요.
2008년 금융위기와 지금 AI붐을 연결한다면 어떤 점이 닮았는지 참고하시어, 인사이트 얻는데 조금이라도 도움되시길!!
https://naver.me/FaeikPqC (무료배포)
과연... AI에 대한 투자는 성공적으로 마무리될 수 있을까요?
내가 투자하는 회사가 이 경쟁적인 AI생태계에서 생존자가 되길 바라며...
글 공유합니다!!
하트 눌러주시면 큰 힘이 됩니다 🫡🫡 감사합니다.
https://t.me/macro_OK 숏박스
8월 1-10일 화장품 수출 데이터입니다!
미국과 영국에서 YoY가 크게 뛴 모습을 볼 수 있습니다.
화장품 상반기 수출데이터 관련해서 제작한 쇼츠가 있는데 시청하면서 8월과 비교해보면 좋을 것 같습니다❗️
https://youtube.com/shorts/2xiwIniOJm8?si=6ZL_Eh5Tl5Z_xcZ7
https://t.me/davidstocknew 주식소리통
[실제 고용은 둔화했는데, 중소기업 채용계획은 늘었다]
어젯밤 NFIB 중소기업낙관지수
◆핵심:
_7월 고용보고서와 ADP는 실제 고용 증가세 둔화를 보여준 반면, NFIB에서는 중소기업의 향후 채용·투자 계획이 크게 개선.
»실제 고용은 약해졌지만 기업들이 아직 채용과 투자를 줄이는 단계로 돌아서지는 않았다는 의미
————
1. 중소기업의 채용·투자 계획은 반등
_NFIB 낙관지수는 시장 예상을 웃돌며 장기평균 회복
_향후 고용과 자본지출 계획이 크게 개선되며 지수 상승 주도
_채우지 못한 일자리도 증가하며 기업들의 인력 수요 확인
2. 실제 고용 증가 속도는 둔화
_고용보고서: 7월 전체 고용은 감소했지만 민간 고용은 소폭 증가
_ADP 주간 고용: 6주 연속 둔화
*NFIB는 특히 향후 채용 계획을. 고용보고서와 ADP는 실제 고…
탄력 오른 K화장품 수출, 1위 프랑스까지 위협
https://www.mk.co.kr/news/business/12124842
한국의 기초·색조 화장품 수출이 올해 1~5월 21% 급증하며 세계 1위 프랑스와의 격차를 31억달러에서 4억달러대로 좁혔고, 내년 역전 가능성까지 거론된다. 다만 향수·헤어케어를 포함한 뷰티 전체 수출로는 프랑스가 여전히 한국의 약 2배 수준을 유지하고 있다. 전문가는 K뷰티가 기초 대비 색조 경쟁력이 약한 점을 보완하면 1~2년 내 프랑스 추월도 가능하다고 전망했다.
https://t.me/davidstocknew 주식소리통
코스맥스, 2분기 역대 최대 실적...목표주가 30만원으로 상향-NH
https://n.news.naver.com/mnews/article/018/0006349923
코스맥스는 2분기 매출 7,949억원, 영업이익 737억원을 기록하며 전년 대비 각각 27%, 21% 증가해 역대 최대 실적을 경신했다. 한국 법인은 영업이익이 13%(대손 환입분 제거 시 23%) 늘며 마진 회복세를 보였고, 중국 법인은 매출이 33% 증가했으며, 미국 법인은 매출이 79% 성장하며 사상 첫 흑자를 달성했다. 성장 요인으로는 7~8월 높은 수출 성장률 지속, 한국 법인 마진 하방 압력 완화, 중국·미국 법인의 고성장이 꼽힌다.
https://t.me/davidstocknew 주식소리통
코어위브·슈퍼마이크로 실적·전망 예상 상회…AI 붐 재확인
https://www.yna.co.kr/view/AKR20260812010900009?input=copy
https://t.me/davidstocknew 주식소리통
Showing the 12 most recent of 127 posts we hold for @davidstocknew. 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
@davidstocknew 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
9 August 2026
Most recent edit
9 August 2026
Citation-graph rank
Citation-graph rank — 308,621 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.
“주식소리통 NEO by Bilanx Research” (@davidstocknew), 17,479 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/davidstocknew.
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.
