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 11 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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 7 other registered channels. This entry also forms a group of 2 channels linked by matching posts. 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)
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. None of the 0 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 351 comparable posts for this entry, running 5 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).
Of the group of 2 channels, the one whose matching posts we hold earliest is @Samsung_Global_AI_SW — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key 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.
Channels linked by matching posts
This channel is in a group of 2 registered channels linked by matching posts. Links are found by comparing text fingerprints across every channel on the register.
Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim 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.
66 measurements spanning 96 days, net +2,263. 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 10,244–13,185 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 66
Measured (UTC)
Subscribers
Change
10 Nov 2026, 09:21
12,846
+51
9 Nov 2026, 00:36
12,795
+60
7 Nov 2026, 16:00
12,735
+15
6 Nov 2026, 12:17
12,720
+27
5 Nov 2026, 11:35
12,693
+24
4 Nov 2026, 11:19
12,669
+14
3 Nov 2026, 07:54
12,655
+19
2 Nov 2026, 04:20
12,636
+9
1 Nov 2026, 04:09
12,627
+1
31 Oct 2026, 06:27
12,626
+11
30 Oct 2026, 09:14
12,615
+12
29 Oct 2026, 08:06
12,603
+19
28 Oct 2026, 05:28
12,584
+4
27 Oct 2026, 02:54
12,580
+14
26 Oct 2026, 03:16
12,566
+15
25 Oct 2026, 02:37
12,551
+1
23 Oct 2026, 23:19
12,550
+4
22 Oct 2026, 22:44
12,546
+11
21 Oct 2026, 23:28
12,535
+33
20 Oct 2026, 15:51
12,502
first reading
Engagement
372 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 58 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
43.3%
avg views ÷ 12,846 subscribers
Avg views / post
5,560
97 posts measured
Reaction rate
0.122%
reactions ÷ views · ER floor
Posts in window
97
of 372 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 11 November 2026
Posts held
372 (5 August 2026 – 11 November 2026)
Views total
539,751
Reactions total
661
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
11 Nov 2026, 08:21 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
Photos
≈2,180
Videos
≈8
Links
≈2,910
Lifetime counters from Telegram’s own channel header, read 11 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Reaction mix
2,287 reactions across 351 posts, in 24 distinct kinds. The most used accounts for 45.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
1,030
45.0%
❤
776
33.9%
😁
141
6.17%
🔥
59
2.58%
😱
52
2.27%
🤔
40
1.75%
🤣
37
1.62%
👏
36
1.57%
😭
32
1.40%
🥰
24
1.05%
👀
21
0.918%
😢
12
0.525%
👎
6
0.262%
🤯
4
0.175%
🎉
2
0.087%
😈
2
0.087%
🙏
2
0.087%
🤡
2
0.087%
🤪
2
0.087%
🤷♂
2
0.087%
4 further kinds
5
0.219%
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 370 of the 372 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 2,394 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 372 most recent posts we hold, published 5 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.
Telegram Stars
Stars received
3
across the posts below
Posts paid on
3
of 372 we hold a reading for · 0.8%
Most on one post
1
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @Samsung_Global_AI_SW. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 372 most recent posts we hold for this entry, published 5 August 2026 to 11 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
Recent posts
Telegram’s own ‘edited’ marker: 13 of 97 posts held from the last 30 days (13.4%) 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).
오픈AI, Codex 업데이트 및 사용량 한도 초기화 암시
: 오픈AI 티보 소티오는 목요일은 Codex와 관련된 날이 될 것 같다”며, “사용량 한도를 초기화하기에도 좋은 시점일 수 있다”고 언급. 앞선 게시물에서도 한도 초기화 조치를 재개할 가능성을 시사
: 구체적인 발표 내용은 미공개. 신규 모델 출시 또는 Codex 기능 업데이트 여부와 사용량 한도 초기화 대상 및 방식은 확인되지 않은 상황
: 앤스로픽이 최근 Claude 사용자에게 사용량 한도 초기화 혜택 제공 중. 이에 일부 이용자들은 오픈AI가 경쟁사의 사용량 확대 정책을 뒤따라가는 모습이라며 냉소적인 반응
https://x.com/thsottiaux/status/2120410480204525936
오픈AI, ChatGPT 내 기업 전용 플랫폼 ‘Businesses in ChatGPT’ 출시
: 오픈AI는 주간 이용자 12억 명을 기반으로 기업과 고객을 직접 연결하는 ‘Businesses in ChatGPT’ 베타 출시. 기업 정보 관리 및 고객 인사이트를 제공하는 Business Hub와 고객 상담을 지원하는 Business Agents로 구성
: Business Hub: 기업이 자사 정보를 인증하고 제품 가격·재고 등 최신 정보를 직접 등록할 수 있는 플랫폼. ChatGPT 내 기업 언급 횟수, 웹사이트 클릭 수, 주요 검색·발견 주제 등 고객 참여 데이터 제공. 사용자 대화 내용은 기업에 공유하지 않음
: Business Agents: 기업이 자체 AI 에이전트를 구축해 ChatGPT 내에서 고객의 제품 비교, 추천 및 후속 …
코어위브(CRWV US) 3Q26 리뷰
안녕하세요 삼성증권 글로벌 AI/SW 담당 이영진입니다.
코어위브는 매출 성장과 수익성 개선을 동시에 달성하는 긍정적 실적을 발표했습니다. 가이던스도 상향 조정되었는데요
’27년에는 조정 순손실이 의미 있게 축소되고, ‘28년 상반기 및 연간 기준 조정 순이익 흑자 전환을 예상하고 있습니다.
GPU 공급 부족에 따른 가격 결정력 강화 속 GPU 재계약을 기반으로 수익성을 빠르게 개선하고 있으며,
고객 선급금 및 자산 담보부 금융을 활용해 신용시장 불안과 데이터센터 규제 강화에도 기존 성장 계획을 유지할 수 있다는 자신감을 강조했습니다.
최근 네오클라우드 전반으로 확산된 자금 조달 및 신용 위험 우려를 일부 완화할 수 있는 내용으로 판단합니다. 컨퍼런스콜 주요 내용을 꼽아보았습니다.
■ AI …
앤스로픽, 유료 사용자 전원에게 사용량 한도 초기화권 1회 무료 제공
: 앤스로픽은 Claude Pro, Max, Ultra, Team 사용자 전원에게 모든 모델 및 사용량 한도를 완전히 초기화할 수 있는 무료 초기화권 1회 제공
: 최근 일부 오류로 사용량 한도가 의도보다 빠르게 소진되는 문제가 발생했으며, 기존 Fable 사용량 초기화는 전체 모델의 주간 사용량 한도에 도달한 사용자에게 실질적인 도움이 되지 않았다고 설명
: 사용 기한은 12월 10일 오후 2시(미국 태평양시간). 초기화권은 Claude의 Settings > Usage 또는 초기화 전용 페이지에서 사용 가능
https://x.com/claudedevs/status/2120275296637321476
앤스로픽, 연말 연 환산 매출 1,000억 달러 달성 가능할까?
안녕하세요 삼성증권 글로벌 AI/SW 담당 이영진입니다.
앤스로픽이 IPO 로드쇼를 시작하며 S-1/A(버전 2)를 공시했습니다.
공모 희망 밴드, 주식 수 등이 업데이트되었고, 시장의 관심을 모았던 연 환산 매출도 공개되었습니다.
희망 공모가격은 $70-78로 공모 주식 수는 8.25억 주입니다. 이에 따른 총 공모 규모는 578~644억 달러입니다.
여기에 구글, 엔비디아, AMD, 브로드컴, 마이크론, 마이크로소프트, TSMC가 참여하는 사모 발행을 통해 260억 달러를 추가 조달할 예정입니다. 합산 최대 904억 달러네요
공모 희망 가격 기준 기본 Equity Value는 0.92~1.02조 달러입니다. 다만 완전 희석 기준으로는 1.26~1.4조 달러가 됩니다…
앤스로픽 IPO 주관사, 기업가치 1.5조 달러 이상으로 상향 준비
: 앤스로픽 IPO 로드쇼 첫날 강한 투자 수요가 확인되면서 골드만삭스, 모건스탠리 등 주관사들이 기업가치를 1.5조 달러 이상으로 상향하는 방안을 검토 중. 기존 희망 밸류에이션 1.26~1.40조 달러(완전 희석 기준)를 상회하는 수준
: 기존 공모 희망가격은 주당 $70~78로, 8.25억 주 발행을 통해 최대 $64.4B 조달 예정. 초과배정옵션 약 $10B와 빅테크 전략적 투자자들의 사모 발행 $26B를 포함하면 총 조달 규모는 약 $100B. 공모가격 인상 시 $100B를 넘어설 가능성
: 기존 투자자들에 따르면 앤스로픽은 기업가치 극대화보다 대규모 자금 확보에 집중. 향후 약 $900B의 컴퓨팅 투자 의무를 이행하기 위한 자금 조달이 우선순위
https:/…
소프트뱅크 그룹 F2Q26 실적 컨퍼런스콜 주요 내용
■ AI 투자 전략
: AI 모델(오픈AI), 반도체(Arm), 인프라(SB Energy·DigitalBridge), Physical AI 등 4대 영역에 집중. AI 산업의 장기 성장 전망에는 변화가 없지만, 최근 시장 조정으로 단기 투자 속도는 조절할 방침
: 미국 중간선거 이후 데이터센터 규제, 하이퍼스케일러 신용위험, 고용 및 환경 문제 등이 부각되고 있지만, AI 기술 발전은 되돌릴 수 없는 구조적 변화라고 평가
■ 오픈AI 투자 및 밸류에이션
: 오픈AI 후속 투자 완료로 지분 약 13% 확보. 신규 자금조달에 따른 밸류에이션 변화는 아직 NAV에 반영되지 않았으며, FY2026 3Q 반영 예상
: 오픈AI의 경쟁력은 제품·플랫폼, AI 모델, 컴퓨팅 인프라를 결합한 …
앤스로픽, Claude Sheets·Whiteboards 등 신규 업무 도구 출시
: 앤스로픽은 Claude Sheets, Whiteboards, Data 등 신규 업무 도구와 Interactive Connectors 업그레이드 발표.
: 기존 코딩·문서·프레젠테이션 중심에서 스프레드시트, 협업 화이트보드, 데이터 분석까지 기능 확장
- Claude Sheets는 데이터 정리·수식 생성·다중 시트 편집을 지원
- Whiteboards는 팀 단위 아이디어 시각화 및 회의 내용 요약 제공
- Claude Data는 Snowflake·Salesforce 등과 연동해 대시보드와 데이터 시각화 콘텐츠 제작
: Interactive Connectors를 통해 Adobe·Atlassian 등 외부 애플리케이션을 Claude 내부에서 직접 사용 …
[삼성 이영진] 글로벌 AI/SW 뉴스 🤖 (26/11/11)
■ 앤스로픽, IPO 공모 주식의 최대 20%를 미국 및 해외 개인투자자에게 배정할 계획. 약 1.65억 주가 대상
■ 앤스로픽, 로드쇼에서 장기 조정 영업이익률 목표로 35% 이상 제시
■ 앤스로픽, 로드쇼 개시 직후 전체 공모 물량을 충족하는 투자자 주문 확보 보도
■ 오픈AI, AI 기반 여행 계획 및 예약 플랫폼 Odessia 인수
■ Nscale, AI 투자심리 악화와 데이터센터 구축 지연으로 350억 달러 규모 IPO의 27년 연기 검토
■ 엔비디아 이사회, 젠슨 황 CEO 관련 의혹 조사를 위해 미국 대형 로펌 Wachtell, Lipton, Rosen & Katz를 선임
■ 미국 상무부, 싱가포르 클라우드 기업 Megaspeed의 수출총제 명단 등재 연기…
구글, Gemini 4 Carbon 정식 출시
: 구글은 Gemini 4 Carbon을 개발자, 기업, 일반 사용자 대상으로 출시. 기존 Gemini 4 Argon이 범용 코딩 및 지식 업무에 최적화됐다면, Carbon은 고난도 추론, 장기 에이전트 작업, 사이버보안 및 과학 연구에 특화
: 에이전트 성능 강화. CursorBench 4.0, Perplexity WANDR, Zapier AutomationBench에서 1위 기록. 차순위 외부 모델 대비 작업당 비용 50% 이상 절감. 멀티모달 지식 업무 벤치마크 MKA에서는 2위로, 1위 모델 대비 작업당 비용 6배 이상 낮음
: 멀티에이전트 기능 확대. Google Antigravity에 /swarm 기능 도입. 단일 프롬프트로 여러 AI 에이전트를 병렬 실행하고 실시간으로 작업 추적…
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Showing the 12 most recent of 372 posts we hold for @Samsung_Global_AI_SW. 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
@Samsung_Global_AI_SW edited 4 posts 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
13 August 2026
Most recent edit
9 November 2026
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republished by 84 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 67 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.
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 10 November 2026 — this
entry's latest reading, not the date you are reading this.
“[삼성 이영진] 글로벌 AI/SW” (@Samsung_Global_AI_SW), 12,846 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/Samsung_Global_AI_SW.
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.