7 measurements spanning 6 days, net -19. 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 10,001–10,026 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 03:42
10,004
-6
11 Aug 2026, 01:55
10,010
-6
9 Aug 2026, 23:37
10,016
+3
8 Aug 2026, 21:05
10,013
-3
7 Aug 2026, 19:21
10,016
-5
6 Aug 2026, 19:00
10,021
-2
6 Aug 2026, 03:32
10,023
first reading
Engagement
22 posts held, back to 4 July 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 15 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
26.9%
avg views ÷ 10,004 subscribers
Avg views / post
2,690
9 posts measured
Reaction rate
0.438%
reactions ÷ views · ER floor
Posts in window
9
of 22 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 August 2026
Posts held
22 (4 July 2026 – 11 August 2026)
Views total
24,177
Reactions total
106
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 14:33 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
≈1,500
Videos
≈33
Links
≈1,660
Lifetime counters from Telegram’s own channel header, read 12 August 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
192 reactions across 17 posts, in 4 distinct kinds. The most used accounts for 53.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
102
53.1%
👍
53
27.6%
🔥
33
17.2%
😁
4
2.08%
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 19 of the 22 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 192reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 4 July 2026 to 11 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.
해시드 플랫폼팀에서 AI Ecosystem Growth 인턴을 모집합니다!
저희 Nitro 이니셔티브를 같이 운영할 분을 찾고 있습니다.
Nitro는 올해 1월 Hashed Vibe Labs라는 이름으로 시작한 프로그램이고, AI를 적극적으로 쓰는 극초기 팀들과 함께합니다.
하는 일
- 워크샵, 멘토링 세션, 데모데이 등 프로그램 기획과 실행 서포트
- 빌더 커뮤니티 온오프라인 접점 설계, 소셜 채널 운영
- 홍보 콘텐츠 기획과 제작
- 공동 주최사, 스폰서, 협력사 실무 조율
- 해커톤과 밋업 현장 운영
- 참여자 데이터 정리 → 결과 리포트 작성
이런 분
- AI 생태계와 빌더 커뮤니티에 관심 있는 분
- 여러 건이 동시에 돌아갈 때 순서를 스스로 잡는 분
- 영어 자료 리딩과 기본 커뮤니케이션 가능한 분
- 커뮤니티 운영이나 그로…
구글의 수석과학자 제프 딘 퇴사. 구글 사번 30번, 1999년 입사, 그 이후 구글의 AI를 포함한 수 많은 역사를 써내려온 레전드 개발자.
이후 새로운 창업은 ML/Science/Engineering을 AI를 통한 자동화를 목표로 한 회사 DiscoveryLoop.
진짜 말도 안되는 건 초기 팀원이 구글에서 남긴 역사적인 기록들...진짜 GOAT of GOAT
> Between us, we have pioneered massive scale computing and led the creation of critical infrastructure, products, and foundational AI advances that the world relies on, including multiple generations of Google S…
근래 아침과 저녁 틈틈히 Kaggle에서 열심히 AI 대회를 참여해보고 있습니다. 특히나 밤에 Loop을 돌리고 대략 아침 7.5시~9시 사이에 결과를 해석하는 루틴을 보내고 있습니다.
Kaggle을 다시 시작한 이유는 단순합니다. 오랜만에 다시 AI를 에이전트 레벨이 아닌 코드 레벨에서 탐구하는 경험이 필요하다고 느꼈습니다. (1) 회사에서 주워듣는 여러 AI 방법론 (주로 RL 테크닉) 테스트와 (2) 제가 기존에 알고 있던 AI 방법론과 그 변천사 등 이제는 짧은 시간 내에 학습과 동시에 실험 돌리며 전체적인 저만의 AI를 바라보는 해상도를 높이고 있습니다. Kaggle과 같이 제한적인 환경이 빠른 이론 학습과 적절한 참교육에는 매우 도움이 된다고 느껴지네요.
Kaggle에는 크게 3가지 유형의 대회가 있습니다. (1) Metric …
이번에 국장을 배아파하면서 관전한 입장에서
매수 타이밍은 꽤 있었으나 단기적으로는 희망회로에서 스스로 잘 팔았을까 하면 저는 못팔았을 것 같아요. 제 기존 성향 상 추매하다가 평단가 아래로 온 상황이 아니었을까 싶네요.
매매는 다를 수 있으나 투자는 장기전이자 어느 정도의 분산이 중요하다는 것을 또 새기게 되네요
저는 비트로 탈탈잃어 현금이 없긴 하지만, 하이닉스 이번 아래꼬리쯤 오면 그래도 매수 고민해봅니다.
그 전까지는 투자 신경안쓰고 향후 3년 이후의 미래 근로소득을 위해 갈고 닦는 게 저에게 더 득되는 선택이라는 믿음으로 사는 중입니다.
opus5가 fable5보다 가볍고 빠르고, 특히 성능은 체감도 비슷한 것 같고 이전 sonnet 4.5에서 느꼈던 알잘딱갈센스러운 부분도 있어서 좋네요. 다만 ai들의 문체가 점점 slop스럽게 가고 있어서 이건 이제 개인화된 스킬이 필수로 느껴지네요.
현재 codex/claude 합쳐서 3-4개 정도 구독을 유지하고 있으며 다음과 같은 상태입니다.
- coding & loop: 5.6 sol xhigh
- 단일 질의: 5.5 Instant / 5.6 pro
- web dev/docs/design: opus5
- simple web automation: chrome extension claude
- 비용효율적 단순 작업 및 우회(?)작업: composer 2.5
grok과 중국 선진 모델도 사용해봐야 하는데 아직 개인적인 작업에 쓸 일…
최근 Kaggle에서 최적화 대회 참여했는데 종료 3일전까지만 해도 100위권이다 막판에 미끄러져서 200등권으로 bronze medal.
회고해보면 시간도 테크닉도 부족했는데 루프 엔지니어링과 visual interface 기반 eda 도구의 완성도가 낮았고 기본적으로 수학적 사고도 많이 퇴화한듯.
여튼 이제 다시 시작이니까 아쉬움은 빨리 떨치고 다시 경쟁력있는 엔지니어가 되기 위해 달릴 예정.
https://x.com/subinium/status/2077538119193378869?s=46&t=_9QK1B_9Xfb5kENxfV3x-g
주칸의 ICML 후기
한국 AI 스타트업이나 랩실의 연구력이 생각보다 너무 떨어짐. 인재유출 방지 / 해외영입을 위한 대책이 필요해보인다. LG AI리서치를 제외, 한국 AI 모델 개발자들에 대해 매우매우 베어리시하다
https://x.com/jukan05/status/2076205112750379183?s=20
리서치 작업에서는 sol이 fable보다 좋음. 근데 여전히 웹개발 알잘딱갈센은 4.8 max가 손맛이 좋음.
리서치 센트릭 작업에서 토큰가성비는 sol xhigh가 개인적으로는 제일 좋음.
그래서 codex 하나 더 구독함. claude는 두 개에서 하나로 줄일 것 같음.
Showing the 12 most recent of 22 posts we hold for @web3subin. 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.
Citation-graph rank
Citation-graph rank — 133,736 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 1 registered channel — 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.
“ordinary subinium” (@web3subin), 10,004 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/web3subin.
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.
