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 76% 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 (6 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 10 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 15 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 @nnninvestment. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_copy, 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.
32 measurements spanning 49 days, net -146. 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 11,954–12,146 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)
Subscribers
Change
25 Sept 2026, 23:17
11,976
-9
19 Sept 2026, 05:18
11,985
-12
16 Sept 2026, 23:01
11,997
+16
14 Sept 2026, 23:39
11,981
+3
13 Sept 2026, 07:58
11,978
-2
11 Sept 2026, 08:58
11,980
-12
8 Sept 2026, 16:21
11,992
-13
5 Sept 2026, 09:00
12,005
-6
3 Sept 2026, 10:08
12,011
-10
2 Sept 2026, 03:37
12,021
-5
1 Sept 2026, 00:48
12,026
-2
30 Aug 2026, 23:42
12,028
-5
30 Aug 2026, 02:37
12,033
-4
29 Aug 2026, 02:28
12,037
-1
28 Aug 2026, 04:14
12,038
-5
27 Aug 2026, 06:58
12,043
-9
26 Aug 2026, 07:32
12,052
-1
25 Aug 2026, 04:48
12,053
-2
24 Aug 2026, 05:34
12,055
-10
21 Aug 2026, 00:35
12,065
first reading
Engagement
103 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 46 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
10.8%
avg views ÷ 11,976 subscribers
Avg views / post
1,300
2 posts measured
Reaction rate
—
this channel exposes no reaction counts
Posts in window
2
of 103 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
103 (2 August 2026 – 2 September 2026)
Views total
2,590
Reactions total
—
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 23:14 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.
■ 우산 X NNN Pick 하루를 돌아보는 뉴스 정리 (09.02)
(https://m.blog.naver.com/s1kkw/224398894189)
美·이란 보복전 격화…"또 공격시 전멸" 확전 우려 증폭(연합뉴스)
https://n.news.naver.com/mnews/article/001/0016285651?sid=104
푸틴 "이란 국민과 연대…필요한 지원 제공할 것"(뉴시스)
https://n.news.naver.com/mnews/article/003/0014162786?sid=104
: 미국채 금리를 중심으로 글로벌 금리가 계속해서 급등하는 상황임에도 여전히 미국과 이란의 서로를 향한 공격은 계속되고 있음. 이 와중에 러시아 푸틴 대통령은 이란 대통령을 만나 지원과 연대를 강조하며 지정학적 리스크는 계속해서 더 커지는 중
유…
■ 우산 X NNN Pick 하루를 돌아보는 뉴스 정리 (09.01)
(https://m.blog.naver.com/s1kkw/224397509902)
美국채 10년물 금리 19개월 만에 최고…매도세 확산(연합뉴스)
https://n.news.naver.com/mnews/article/001/0016281347?sid=101
日 10년물 국채금리 3% 돌파…1996년 이후 30년 만에 최고(뉴스1)
https://n.news.naver.com/mnews/article/421/0009144316?sid=101
: 가뜩이나 불안불안하던 글로벌 주요국들의 장기 금리가 미국과 이란의 추가 공방 소식이 전해지며 추가로 레벨업되는 모습. 특히 일본 국채 10년물은 무려 30년 만에 3%를 돌파했으며 BOJ의 9월 금리 인상 확률은 92%까지 상승함. …
■ 우산 X NNN Pick 하루를 돌아보는 뉴스 정리 (08.31)
(https://m.blog.naver.com/s1kkw/224396271894)
8월 한 달도 모두 고생 많으셨습니다.
워시, ‘물가냐 트럼프냐’…잭슨홀 발언으로 금리인상 가능성 급등(디지털타임스)
https://n.news.naver.com/mnews/article/029/0003044937?sid=104
: 시장이 주목하던 잭슨홀 미팅에서의 워시 의장 발언은 매파적이었다는 평가가 지배적. 물가 목표 2% 향해 움직여야 한다는 발언 등 금리 인상을 시사하는 발언을 쏟아내며 9월 금리 인상 확률은 60%에 육박하는 수준까지 급등
치솟는 국채 금리…"G7, 1년 추가 이자만 68조원"(머니투데이)
https://n.news.naver.com/mnews/article…
■ 다음 주 스케쥴 점검 (현지시각 기준)
Check Point 1) G20 재무장관/중앙은행총재 회의(월)
Check Point 2) 미국 고용지표 발표(비농업취업자수(수), 논 팜 페이롤(금), 베이지북 공개(수)
Check Point 3) 브로드컴 실적발표(수)
■ 이재명 대통령 국정수행 긍정평가 비중
* 단순평균은 리얼미터(주 초)와 한국갤럽(주 말), NBS(격주) 데이터의 단순 평균을 의미
** 데이터 공백이 있을 경우 가장 최근 데이터 활용 임의 보간
- 단순평균 : 44.1%, -1.60%p
- 리얼미터(주 초) : 40.2%
- 한국갤럽(주 말) : 42.0%
- NBS(격주) : 50.0%
Showing the 12 most recent of 103 posts we hold for @WoosanXNNN. 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.
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 25 September 2026 — this
entry's latest reading, not the date you are reading this.
“우산 X NNN의 아이디어” (@WoosanXNNN), 11,976 subscribers as measured 25 September 2026. Telegram Register, tgregister.com/channel/WoosanXNNN.
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.