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[삼성 이영진] 글로벌 AI/SW

@Samsung_Global_AI_SW

On this record: Topic · Growth · Engagement · Reactions · Stars · Posts · Posts edited after publishing · Citations · Cite this entry

12,109subscribers

+1,526 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002023301274
TypeChannel
Username@Samsung_Global_AI_SW
CreatedBetween 1 November 2023 and 31 May 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live26 September 2026
Measurements held37
Confirmed unchanged1 time, most recently 26 September 2026
On Telegramt.me/Samsung_Global_AI_SW

Topic

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 why it has no browse page of its own yet.

Growth

10,58312,10911,3466 August 2026 — 10,583 subscribers6 August 2026 — 10,583 subscribers6 August 2026 — 10,595 subscribers7 August 2026 — 10,603 subscribers8 August 2026 — 10,633 subscribers9 August 2026 — 10,646 subscribers10 August 2026 — 10,668 subscribers11 August 2026 — 10,676 subscribers12 August 2026 — 10,716 subscribers13 August 2026 — 10,770 subscribers15 August 2026 — 10,782 subscribers16 August 2026 — 10,862 subscribers17 August 2026 — 10,934 subscribers18 August 2026 — 10,963 subscribers19 August 2026 — 10,967 subscribers20 August 2026 — 10,993 subscribers21 August 2026 — 11,005 subscribers23 August 2026 — 11,098 subscribers24 August 2026 — 11,189 subscribers25 August 2026 — 11,201 subscribers26 August 2026 — 11,209 subscribers27 August 2026 — 11,216 subscribers28 August 2026 — 11,234 subscribers29 August 2026 — 11,241 subscribers30 August 2026 — 11,247 subscribers31 August 2026 — 11,276 subscribers1 September 2026 — 11,283 subscribers2 September 2026 — 11,320 subscribers3 September 2026 — 11,326 subscribers5 September 2026 — 11,360 subscribers9 September 2026 — 11,469 subscribers11 September 2026 — 11,521 subscribers13 September 2026 — 11,695 subscribers15 September 2026 — 11,779 subscribers17 September 2026 — 11,890 subscribers19 September 2026 — 11,948 subscribers26 September 2026 — 12,109 subscribers6 August 202626 September 2026
37 measurements spanning 51 days, net +1,526. 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 10,354–12,338 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 37
Measured (UTC)SubscribersChange
26 Sept 2026, 10:4212,109+161
19 Sept 2026, 11:2011,948+58
17 Sept 2026, 03:3611,890+111
15 Sept 2026, 04:3711,779+84
13 Sept 2026, 10:3711,695+174
11 Sept 2026, 14:5711,521+52
9 Sept 2026, 03:1511,469+109
5 Sept 2026, 19:1711,360+34
3 Sept 2026, 20:1711,326+6
2 Sept 2026, 12:0311,320+37
1 Sept 2026, 11:1211,283+7
31 Aug 2026, 14:2411,276+29
30 Aug 2026, 13:3611,247+6
29 Aug 2026, 12:5811,241+7
28 Aug 2026, 16:0611,234+18
27 Aug 2026, 17:0811,216+7
26 Aug 2026, 15:4311,209+8
25 Aug 2026, 17:1211,201+12
24 Aug 2026, 20:2511,189+91
23 Aug 2026, 07:2611,098first reading

Engagement

275 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 41 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
26.8%
avg views ÷ 12,109 subscribers
Avg views / post
3,250
39 posts measured
Reaction rate
0.197%
reactions ÷ views · ER floor
Posts in window
39
of 275 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 38 of 39 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 3 September 2026
Posts held275 (5 August 2026 – 3 September 2026)
Views total126,710
Reactions total248
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken3 Sept 2026, 00:45 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

1,626 reactions across 254 posts, in 20 distinct kinds. The most used accounts for 46.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍76046.7%
❤56734.9%
😁965.90%
🤔392.40%
🤣301.85%
👏281.72%
😱271.66%
🔥231.41%
🥰191.17%
👀130.8%
😢80.492%
🎉20.123%
🙏20.123%
🤡20.123%
🤪20.123%
🤯20.123%
🤷‍♂20.123%
🥴20.123%
👎10.062%
👾10.062%

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 273 of the 275 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 1,733 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 275 most recent posts we hold, published 5 August 2026 to 3 September 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
2
across the posts below
Posts paid on
2
of 275 we hold a reading for · 0.7%
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 275 most recent posts we hold for this entry, published 5 August 2026 to 3 September 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

3 Sept 2026, 00:13 UTC511 viewsread 3 September 2026
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LLM 토큰 지수 신저점, 폐쇄형 모델 하락이 주도 LLM 토큰 지수가 아직 바닥을 찍지 않은 것으로 나타났습니다. 오히려 신저점을 기록했습니다. 최근 2주 동안 오픈 LLM과 폐쇄형 LLM 토큰 지수가 모두 하락했지만, 하락을 주도한 것은 폐쇄형 모델입니다. 연초 이후 기준으로는 오픈 LLM 지수가 폐쇄형 LLM 지수를 추월했습니다. https://t.me/Samsung_Global_AI_SW

2 Sept 2026, 23:33 UTC712 views4 reactionsread 3 September 2026

[삼성 이영진] 글로벌 AI/SW 뉴스 🤖 (26/9/3) ■ 메타, Muse Spark 1.3 발표 ■ 구글, Gemini 3.8 Flash 및 사이버보안 특화 모델 발표 ■ 트럼프 행정부, NYT 저작권 소송에서 오픈AI 측 논리 공식 지지. LLM 학습은 극도로 변형적인 이용으로 공정 사용에 해당한다는 입장 ■ 러트닉 상무장관, 앤스로픽과 트럼프 행정부의 관계가 회복됐다고 언급 ■ 앤스로픽, Claude에 Mac 백그라운드 제어 기능 추가 ■ 오라클-HPE, AI 데이터센터 네트워크 계약 확대. Juniper 장비 글로벌 공급 ■ 크라우드스트라이크, 오픈AI와 파트너십 확대, Codex 보안 및 GPT-5.6 Cyber 통합 ■ 크라우드스트라이크, Falcon 플랫폼을 앤스로픽의 마켓플레이스에 통합. Claude를 Falc…

❤2👍2

2 Sept 2026, 22:43 UTC≈4,410 views5 reactionsread 3 September 2026
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메타 알렉산더 왕, 구글 도발 이런 말 하긴 정말 싫지만… Gemini, 누구더라? → 모델 출시 싸움에 업데이트하는 사람만 죽어나.. https://x.com/alexandr_wang/status/2095249704888197175

🤣5

2 Sept 2026, 22:09 UTC≈1,870 views7 reactionsread 3 September 2026

메타, Muse Spark 1.3 발표 : 메타가 5개월 만에 네 번째 Muse Spark 모델 Muse Spark 1.3 출시 : 장기 에이전트 작업, 코딩, 멀티태스킹, 지시 이행 능력을 강화. 복잡하고 상충하는 정보에서 스스로 컨텍스트를 구성하고 계획의 빈틈을 수정하며, 하나의 긴 대화에서 여러 작업을 동시에 추적. 모호한 요청에는 질문하고 중요한 행동 전 사용자 확인을 거치도록 개선 : 코딩에서는 장기 작업 중심으로 추가 훈련해 불필요한 턴과 장황한 출력을 축소. 메타 내부 평가에서 전작 대비 도구 호출 약 20%, 토큰 사용량 약 25% 감소하며 효율성 개선 : Muse Code와 Meta Model API에서 제공. max reasoning은 추가 안전성 테스트 후 출시 예정 : Artificial Analysis Inte…

❤5👍2

2 Sept 2026, 21:51 UTC914 views5 reactionsread 3 September 2026

구글, Gemini 3.8 Flash 및 사이버보안 특화 모델 발표 : 구글 딥마인드는 Gemini 3.8 Flash와 3.8 Flash Cyber 공개. 3.7 Flash 출시 3주 만이자 6주 동안 세 번째 Flash 업데이트로, 두 모델은 동일한 베이스 모델 : Gemini 3.8 Flash는 코딩, 에이전틱 작업, 다단계 추론 성능 대폭 개선. 복잡한 작업에서 추론 단계와 도구 호출을 늘리는 방식으로 장기 실행 에이전트 성능 강화 : 가격은 3.7 Flash와 동일한 인풋/ 아웃풋 = $0.75/$3.75. 다만 높은 effort에서는 성능 극대화를 위해 토큰 사용량이 증가할 수 있으며, 비용 효율성이 중요하면 낮은 effort 또는 3.7 Flash 사용 권고 : AAII 59점으로 3.7 Flash 대비 3점 상승. 작업당 …

❤3👍2

2 Sept 2026, 21:07 UTC943 views3 reactionsread 3 September 2026

[삼성 이영진] 스노우플레이크(SNOW) F2Q27 실적 요약 ■ F2Q27 실적 : 매출 15.5억 달러(+35%) vs 컨센 14.8억 달러 : 제품 매출 14.9억 달러(+37%) vs 컨센 14.2억 달러, 가이던스 14.15-14.2억 달러(+30%) : RPO 90억 달러(+30%) vs 컨센 94.9억 달러 : Non GAAP 영업이익률 15.3% vs 컨센 12.7%, 가이던스 12.5% ■ F3Q27 가이던스 : 제품 매출 15.88-15.93억 달러(+37.5%) vs 컨센 15억 달러 : Non GAAP 영업이익률 15.5% vs 컨센 14.1% ■ FY27 가이던스 : 제품 매출 60.7억 달러(+36%) vs 컨센 58.5억 달러, 기존 58.4억 달러(+31%) : 제품 매출총이익률 74% vs 컨센 75%, 기…

👍2❤1

2 Sept 2026, 12:59 UTC≈1,730 views8 reactionsread 3 September 2026

샘 올트먼 x 알렉스 히스 팟캐스트 주요 내용 ■ 프론티어 RL 훈련 지연, 모델 발전 속도가 안전 연구를 앞지름 : 최근 모델 능력 향상 속도가 예상보다 훨씬 빨라지면서 안전, 정렬, 보안 연구가 이를 따라잡을 시간이 필요해짐 : 수주 전부터 일부 훈련을 늦추고 컴퓨팅을 안전, 정렬 및 모니터링으로 재배분했으며, 최근 주요 프론티어 RL 훈련을 공식적으로 연기. 모든 훈련을 중단한 것은 아니며 현재 가장 큰 위험이 있다고 판단하는 프론티어 RL에 한정 : 훈련 중 단일한 결정적 사고가 발생한 것은 아니지만 여러 샘플에서 다양한 수준의 정렬 문제를 발견. 최근 프리트레이닝 성능까지 급격히 개선되면서 선제적으로 대응 : 과거에는 모델 배포 이후의 위험이 중요했다면 앞으로는 모델의 훈련과 생산 과정 자체의 위험도 커지는 단계로 진입했다고…

❤5👍3

2 Sept 2026, 12:24 UTC≈7,090 views4 reactionsread 3 September 2026

크라우드스트라이크, 오픈AI와 파트너십 확대, Codex 보안 및 GPT-5.6 Cyber 통합 : 크라우드스트라이크가 오픈AI와 파트너십을 확대해 Falcon Guardian으로 Codex 에이전트를 런타임 단계에서 보호하고, GPT-5.6 Cyber를 Falcon 플랫폼에 통합 : Falcon Guardian은 기업 내 Codex 에이전트를 실시간으로 식별하고 배포 주체, 접근 대상, 보안 상태를 파악 : Falcon 텔레메트리와 연계해 에이전트 행동을 실시간 모니터링하고, 침해·비인가 행동 탐지 및 대응, 허용 행동에 대한 정책 집행까지 지원 : GPT-5.6 Cyber는 우선 크라우드스트라이크의 FAIRR Service에 적용. 크라우드스트라이크의 위협 인텔리전스, 위협 모델링, 익스플로잇 검증 및 전문가 감독과 결합해 공격 경…

❤2👍2

2 Sept 2026, 10:49 UTC≈1,160 views6 reactionsread 3 September 2026

SB Energy, S-1 주요 내용 : 소프트뱅크 산하 AI 데이터센터·전력 인프라 업체 SB Energy가 미국 IPO를 위한 S-1 제출. Nasdaq에 티커 SBE로 상장 예정. 공모 주식 수와 가격 범위는 아직 미정 ■ 4,390억 달러 계약 백로그 : 총 계약 백로그 약 4,390억 달러. 데이터센터 약 4,300억 달러, 전력 프로젝트 약 100억 달러로 구성. 잔여 계약기간 가중평균은 데이터센터 19.6년, 전력 16.6년 : 다만 장기 계약의 총 임대료 합산 수치로 매출 인식 시점은 상당히 후행. 향후 24개월 내 매출 전환 예상액은 약 10억 달러에 불과. 약 3,570억 달러는 8년 이후 인식 예정 ■ 데이터센터 8.8GW-IT 계약 : 현재 체결된 데이터센터 임대계약은 총 8.8GW-IT. 이 중 약 0.8GW…

❤3👍3

2 Sept 2026, 06:50 UTC≈6,090 views6 reactionsread 3 September 2026
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오픈AI 수석과학자 야쿠브 파초키, Astra 추론 불투명성 우려에 대한 해명 혼란스러운 보도로 인해 ‘모니터링 불가능한 모델을 향한 경쟁’이 촉발되는 것을 막고 싶습니다 Astra를 포함한 현재 저희 프론티어 모델들의 연산 그래프 깊이(computation graph depth)는 GPT-4 대비 2배 이내 수준입니다. 오픈AI는 최초의 추론 모델을 개발했을 때부터 Chain-of-Thought(CoT) 모니터링을 보존하고 활용하기 위해 노력해왔습니다. 저희는 이 기법을 매우 중요하게 생각합니다. 이를 통해 모델 정렬이 학습 데이터 분포를 벗어난 상황에서도 어떻게 일반화되는지를 들여다볼 수 있기 때문입니다. 다만 저는 이 기법이 취약하며, 안타깝게도 현재 부정적인 방향으로 가고 있다고 생각합니다. 그 이유는 아키텍처 변화에 국한된 …

👍5❤1

2 Sept 2026, 05:31 UTC≈1,450 views16 reactionsread 3 September 2026
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앤스로픽, Fable 5.1과 Mythos 5.1 - Astra보다 한 발 먼저 안녕하세요 삼성증권 글로벌 AI/SW 담당 이영진입니다. 앤스로픽은 최상위 티어 모델인 Fable 5.1과 Mythos 5.1의 업데이트를 진행했습니다. AAII 점수는 66점으로 기존 Fable 5(62점) 대비 4점 개선하며 1위 자리를 공고히했는데요 코딩, 지식 업무, 컴퓨터 유즈, 장기 에이전트 작업에서의 성능 강화가 강조되었고, 과학 연구 분야와 커널 최적화 등의 성과도 제시되었습니다. API 가격은 기존과 동일하나, 캐시 읽기만 75% 인하해 에이전트 워크로드는 최대 45% 비용 절감을 이야기하고 있습니다. 하지만 AA Index 평가에 활용되는 아웃풋 토큰은 전작 대비 1.7배 수준으로 작업 당 비용이 오히려 올라갔습니다. 사실 유저 불만…

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2 Sept 2026, 04:10 UTC≈1,370 views6 reactionsread 3 September 2026
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오픈AI Astra, ‘Recurrent Depth’ 적용으로 성능 향상 : 오픈AI 차세대 모델 Astra는 Recurrent Depth, Looped Transformer 기술이 적용되었다는 보도. 동일한 Transformer 레이어를 여러 차례 반복 통과시켜 추가 연산을 수행하는 방식 : 기존 모델이 고정된 수의 레이어를 한 번씩 통과하는 것과 달리, Recurrent Depth는 동일 레이어를 반복 사용해 모델 크기를 크게 늘리지 않고도 추론 연산량 확대. 수학, 코딩 성능을 높이는 동시에 더 작은 모델로 대형 모델에 준하는 성능을 구현해 메모리, 대역폭 비용 절감 가능 : 다만 반복 과정에서 일부 추론이 자연어 Chain of Thought가 아닌 latent space 내부에서 진행되면서 사람이 읽을 수 있는 사고 과정이 줄어…

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Showing the 12 most recent of 275 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 3 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
26 August 2026

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

Republished by 72 registered channels. The 48 listed are the ones that have forwarded the most posts; the rest are counted here but not each listed.

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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 58 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 24 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 26 September 2026 — this entry's latest reading, not the date you are reading this.

“[삼성 이영진] 글로벌 AI/SW” (@Samsung_Global_AI_SW), 12,109 subscribers as measured 26 September 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.