Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 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 2 other registered channels. 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 (1 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. 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 209 comparable posts for this entry, running 4 August 2026 to 10 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).
Recorded under the keys clone_mutual · clone_source, last confirmed 11 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.
Growth
61 measurements spanning 97 days, net +684. 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 17,447–18,391 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 61
Measured (UTC)
Subscribers
Change
11 Nov 2026, 11:21
18,244
-4
8 Nov 2026, 08:51
18,248
-3
6 Nov 2026, 07:06
18,251
-7
5 Nov 2026, 06:50
18,258
-17
4 Nov 2026, 08:06
18,275
-1
3 Nov 2026, 07:15
18,276
+3
2 Nov 2026, 10:30
18,273
-9
1 Nov 2026, 10:06
18,282
+4
31 Oct 2026, 13:37
18,278
+9
30 Oct 2026, 13:41
18,269
+36
29 Oct 2026, 11:49
18,233
+31
28 Oct 2026, 13:46
18,202
+23
27 Oct 2026, 13:46
18,179
+46
26 Oct 2026, 17:07
18,133
+10
25 Oct 2026, 18:17
18,123
-3
22 Oct 2026, 20:07
18,126
+4
21 Oct 2026, 19:21
18,122
+23
20 Oct 2026, 16:56
18,099
-3
19 Oct 2026, 16:06
18,102
-1
18 Oct 2026, 15:35
18,103
first reading
Engagement
248 posts held, back to 4 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 72 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
27.0%
avg views ÷ 18,244 subscribers
Avg views / post
4,930
82 posts measured
Reaction rate
0.119%
reactions ÷ views · ER floor
Posts in window
82
of 248 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 63 of 82 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 11 November 2026
Posts held
248 (4 August 2026 – 11 November 2026)
Views total
404,420
Reactions total
405
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
11 Nov 2026, 04:19 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
2,498 reactions across 195 posts, in 7 distinct kinds. The most used accounts for 53.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
1,331
53.3%
👍
317
12.7%
😢
276
11.0%
😱
245
9.81%
🤔
167
6.69%
👏
87
3.48%
🔥
75
3.00%
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 215 of the 248 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 2,709 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 248 most recent posts we hold, published 4 August 2026 to 11 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: 16 of 82 posts held from the last 30 days (19.5%) 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).
[SK증권 해외주식 박제민]
코어위브(CRWV/US) 3Q26 Review: 3종 우려 불식
코어위브는 강한 레버리지 사업자인만큼 최전방 서버 수요에 민감하다. 여기에 무담보 채권 금리 급증, 블루웨이브 사태 등으로 이번 실적 발표는 수요 뿐 아니라 자금 여력과 공급 지연 우려가 추가된 주가에서 진행됐다. 결과는 3 중 우려를 많은 부분 해소했다.
▶️ 가격 결정력에서 보이는 강한 수요
장기계약 GPU 임대료를 W 당 $20~25 (+10% QoQ 추정)으로 공개
2028년 용량 가격 교섭 위해 판매 지연 중
▶️ 자금 부족 리스크: 이자는 싸지고 확보해둔 돈 많다
1) 이자/EBITDA 비중 개선: 1Q26 46% → 37%
2) 가중평균 조달 금리 하락: 1Q26 9% → 7.9%
3) $9B 수준 선급금 규모 공개
▶️ DC 지…
[SK증권 반도체 한동희, 손 건]
11월 1~10일 반도체 수출액 잠정치
DRAM 66.7억달러 (+27% MoM, +44% QoQ)
DRAM 모듈 35.2억달러 (+98% MoM, +983% QoQ)
NAND 9.0억달러 (+11% MoM, +15% QoQ)
MCP 31.6억달러 (+43% MoM, +50% QoQ)
SSD 9.5억달러 (+94% MoM, +95% QoQ)
[11월 1~10일/10월]전월 대비 비중
DRAM 37%
DRAM 모듈 22%
NAND 30%
MCP 26%
SSD 17%
* 위 내용은 공개 발표 내용으로 별도의 승인절차 없이 제공합니다.
* SK증권 리서치 IT팀 채널 :
https://t.me/skitteam
Q: 2027 Funding은 Parent Company Debt 발행 없이 가능한가?
A: 상당 부분 가능하다는 설명. 기존 DDTL Undrawn Capacity 약 $5B, Prepayment 약 $9B, Operating Cash Flow 증가, 신규 Asset-level Financing을 활용해 추가 Parent-level Capital은 $5B 미만 전망
Q: 신규 DDTL Financing Cost와 LTV는?
A: Investment-grade Contract 기반 차기 DDTL은 약 7~7.5% Yield, 관련 CapEx의 90%+ Financing 가능 전망
Q: Interest Expense가 너무 빠르게 늘어나는 것 아닌가?
A: 3Q Interest Expense $846M으로 증가했으나 Adjusted Oper…
[SK증권 반도체 한동희, 손 건]
2026.11.11 주요 뉴스
코어위브 3Q 매출 36.6억 달러로 컨센 상회…연간 가이던스 130~133억 달러로 상향
수주잔고는 132.5억 달러가 아닌 1325억 달러로 집계됐고 주당손실 1.27달러로 예상(-1.46달러)을 상회, 다만 이자비용이 조정 영업이익을 크게 웃돈다는 지적과 데이터센터 반대 정치 리스크는 잔존
https://buly.kr/7QPG6rr
앤트로픽, 최대 1.4조 달러 밸류로 IPO 로드쇼…11월 19일 나스닥 ‘ANT’ 상장
주당 70~78달러에 8.25억주를 공모해 최대 644억 달러, 사모 260억 달러를 더해 총 904억 달러 조달 예정이며 10월 말 런레이트 매출은 810억 달러(9월 말 대비 +100억 달러), 컴퓨팅 약정은 9000억 달러
https://buly…
IT하드웨어 뉴스 (26.11.11)
[ SK증권 IT하드웨어/배터리 박형우, 정영환 ]
▶️ 삼성전자, 메모리 가격 급등에 대응해 갤럭시S27 고가 모델 비중 71~74% 확대 전망
(26.11.11. 조선비즈)
삼성전자, 갤럭시S27 시리즈에 울트라보다 작고 S펜을 제외한 프로 모델 추가 검토. 연간 생산량 약 3,500만대 중 울트라 1,800만대, 프로 700만~800만대를 배정해 고가 모델 비중을 기존 50%에서 71~74%로 확대할 전망
https://buly.kr/ChrzWKh
▶️ 미쓰이금속, AI·반도체 수요 확대에 말레이시아 동박 공장 신설
(26.11.10. Newsweek Japan)
미쓰이금속, AI 및 반도체 시장 확대에 따른 동박 제품의 안정적인 공급을 위해 말레이시아 신규 생산공장 건설 발표. 토지·건물·생산…
[SK증권 반도체 한동희, 손 건]
▶️ TSMC 26년 10월 매출액:
- NT$ 586.9B (+14.7% MoM, +59.7% YoY)
URL: https://buly.kr/ChrzHEf
* SK증권 리서치 IT팀 채널 :
https://t.me/skitteam
👏9
Signed 손 건
Showing the 12 most recent of 248 posts we hold for @skitteam. 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.
Republished by 58 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 61 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. The 23 listed below are the most frequent namers; the rest are counted above but not each listed.
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.
[메리츠 Tech 김선우, 양승수, 김동관] @merITz_tech · 24,795 Telegram ranks this channel #10 of 92 here — alongside 91 others — read 17 September 2026
키움증권 미국주식 톡톡 @kwusa · 36,213 Telegram ranks this channel #86 of 92 here — alongside 91 others — read 2 September 2026
This channel appears in 2 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 11 November 2026 — this
entry's latest reading, not the date you are reading this.
“[ IT는 SK ]” (@skitteam), 18,244 subscribers as measured 11 November 2026. Telegram Register, tgregister.com/channel/skitteam.
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.