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Channel

크립토 부엉이🦉

@CryptoOwlNotice

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

7,914subscribers

-16 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001646725578
TypeChannel
Username@CryptoOwlNotice
Description둥지 짓는 부엉이 코인에 대한 모든 정보를 다루는 채널입니다 공지방: https://t.me/CryptoOwlNotice 채팅방: https://t.me/CryptoOwlNest 트위터: https://x.com/crypto_owl77 카카오톡:https://open.kakao.com/o/g5QSMgNf 링크트리: https://linktr.ee/cryptoowl Dm: @cryptoowl77
Created12 July 2022measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live12 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 12 August 2026
On Telegramt.me/CryptoOwlNotice

Growth

7,9147,9307,9226 August 2026 — 7,930 subscribers6 August 2026 — 7,927 subscribers9 August 2026 — 7,919 subscribers12 August 2026 — 7,914 subscribers6 August 202612 August 2026
4 measurements spanning 6 days, net -16. 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 7,912–7,932 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 02:327,914-5
9 Aug 2026, 07:117,919-8
6 Aug 2026, 14:017,927-3
6 Aug 2026, 03:057,930first reading

Engagement

144 posts held, back to 5 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 11 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
0.581%
avg views ÷ 7,914 subscribers
Avg views / post
45.9
144 posts measured
Reaction rate
16.9%
reactions ÷ views · ER floor
Posts in window
144
of 144 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 137 of 144 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 12 August 2026
Posts held144 (5 August 202612 August 2026)
Views total6,616
Reactions total1,085
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 17:00 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
8,570
Videos
405
Links
7,110

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

896 reactions across 118 posts, in 8 distinct kinds. The most used accounts for 61.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
55261.6%
👍20823.2%
🔥9710.8%
😈283.13%
🥰50.558%
👏30.335%
🆒20.223%
😁10.112%

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 137 of the 144 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 1,085reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 144 most recent posts we hold, published 5 August 2026 to 12 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.

Recent posts

12 Aug 2026, 16:14 UTC18 viewsread 12 August 2026
Forwarded from @emperorcoinPhoto

비트코인 11개월의 법칙 11개월 음봉으로 고통 받으면 다음부턴 상승장이라는 이야기 #BTC

12 Aug 2026, 15:12 UTC60 views12 reactionsread 12 August 2026
Photo

DAPPOS 스토리텔러 끝!!! 여러분들이 도와주신 덕분에 15등으로 마감했습니다!!! 스토리텔러 보상은 TGE 7일 이내 지급해준다고 하니 관련 내용 전달 받으면 올리겠습니다~~ 다들 수고하셨습니다~~ 굿밤 보내세욤!

🔥12

12 Aug 2026, 13:50 UTC49 views9 reactionsread 12 August 2026
Forwarded from @cryptocurrencymagePhoto

Codex 활성 사용자 가속 추이

🔥81

12 Aug 2026, 13:06 UTC77 views14 reactionsread 12 August 2026
Photo

DAPPOS, 잘못 기억하는 AI가 더 위험할 수 있음 6/10 AI가 검색 과정에서 잘못된 자료 하나를 보는 것과 그 자료를 반복적으로 사용하는 기억으로 남기는 것은 위험도가 다름. 기억에 들어간 정보는 이후 여러 판단에 영향을 줄 수 있기 때문임. DAPPOS는 Bubble Engine의 주요 메커니즘을 설명하면서 Compound Memory와 함께 misinformation handling을 명시적으로 언급합니다. 공식 기술 자료에서는 cross-verification과 Web3 기반 메커니즘을 활용해 정보 품질 문제를 다루는 방향도 제시합니다. 즉 Memory 시스템에서 중요한 질문은 “무엇을 기억할까” 뿐 아니라 “이걸 기억해도 되는가” 임. 기억 능력이 강해질수록 잘못된 정보도 더 오래 살아남을 수 있기 때문에 Me

9👍3👏2

12 Aug 2026, 12:11 UTC67 views12 reactionsread 12 August 2026
Photo

DAPPOS, Durable이 영원히 고정된다는 뜻은 아님 5/10 Web3에서는 오늘 맞는 정보가 몇 달 뒤에는 달라질 수 있음. 프로토콜이 업그레이드되고, 새로운 네트워크가 등장하고, 시장 구조도 계속 변하기 때문임. DAPPOS는 Bubble Engine을 실시간 정보를 받아들이며 계속 적응하는 adaptive learning core로 설명하면서, 동시에 Compound Memory에서 durable knowledge와 episodic learning을 결합합니다. 여기서 durable을 “절대로 변하지 않는 데이터”라고 이해하면 안 됨. 공식 구조를 바탕으로 보면 더 자연스러운 해석은 일회성 경험보다 오래 유지하며 재사용할 가치가 있는 검증된 지식 층에 가까움. Web3 AI에서는 기억하는 능력만큼 언제 새로운 정보로 기존

12

12 Aug 2026, 11:07 UTC56 views12 reactionsread 12 August 2026
Photo

DAPPOS, 찾는 것과 기억하는 것을 분리하는 이유 4/10 RAG와 AI Memory는 비슷해 보여서 헷갈릴 때가 많음. 둘 다 AI에게 추가 정보를 주기 때문임. 하지만 역할은 다름. DAPPOS는 Bubble Engine에서 Contextual RAG와 Compound Memory를 서로 보완하는 별도의 메커니즘으로 설명합니다. Contextual RAG는 provenance-first 방식으로 필요한 정보를 모으는 쪽이고, Compound Memory는 durable knowledge와 episodic learnings를 결합하는 쪽임. 쉽게 표현하면 RAG → 지금 필요한 정보를 찾아옴 Memory → 반복적으로 활용할 지식과 학습을 유지함 에 가까움. 좋은 AI가 되려면 매번 최신 정보를 찾아오는 능력과 과거에 이

12

12 Aug 2026, 10:02 UTC58 views12 reactionsread 12 August 2026
Photo

DAPPOS, Episodic Learning이 따로 필요한 이유 3/10 AI가 어떤 작업을 수행하면서 새로운 패턴을 발견했다고 해보자. 그 경험이 유용할 수는 있지만 곧바로 시스템 전체의 영구적인 진실이라고 보기는 어려움. DAPPOS의 Compound Memory는 durable knowledge와 함께 episodic learnings를 별도의 층으로 결합합니다. 다른 공식 기술 설명에서는 이를 bubble-driven episodic learnings라고 표현합니다. 쉽게 보면 episodic 쪽은 “세상은 반드시 이렇게 작동한다” 보다는 “최근 이런 상황에서 이런 경험과 학습이 있었다” 에 더 가까운 정보 형태로 이해할 수 있음. AI가 새로운 경험에서 빠르게 배우면서도 모든 경험을 곧바로 장기 지식과 동일시하지 않

12

12 Aug 2026, 09:08 UTC70 views13 reactionsread 12 August 2026
Photo

DAPPOS, 오래 기억할수록 검증이 중요한 이유 2/10 AI가 잘못된 정보를 한 번 답하는 것도 문제지만, 그 내용을 장기 지식으로 계속 재사용한다면 문제는 더 커짐. 다음 질문에서도 같은 오류가 반복될 수 있기 때문임. DAPPOS는 Compound Memory의 durable knowledge를 core, verified Web3 facts and models​라고 설명합니다. 즉 오래 사용하는 지식에는 단순히 최근에 발견했다는 이유보다 검증된 핵심 정보라는 성격을 강조하고 있음. 여기서 중요한 단어가 verified임. 장기 기억은 여러 미래 작업에서 다시 쓰일 가능성이 있기 때문에 입력 단계의 품질이 더 중요해질 수 있음. 기술적으로 보면 기억의 수명이 길어질수록 잘못된 정보의 영향 범위도 커질 수 있기 때문임. 그래

13

12 Aug 2026, 08:06 UTC71 views14 reactionsread 12 August 2026
Photo

DAPPOS, Compound Memory가 기억을 나누는 이유 1/10 AI에게 기억 기능이 있다고 하면 보통 하나의 거대한 저장소부터 떠올리게 됨. 그런데 Web3에서는 정보의 성격이 완전히 다름. 프로토콜의 기본 구조처럼 비교적 오래 유지되는 지식이 있는 반면, 최근 시장에서 발견한 패턴처럼 시간이 지나면서 가치가 달라지는 정보도 있음. DAPPOS의 Bubble Engine은 그래서 Compound Memory에서 durable knowledge와 episodic learnings를 함께 다루는 구조를 사용합니다. Durable 쪽에는 core, verified Web3 facts and models가 포함된다고 공식 문서에서 설명합니다. 즉 모든 기억을 똑같이 취급하는 게 아니라 오래 유지할 지식 + 최근 경험에서 얻은 학

👍122

12 Aug 2026, 07:05 UTC74 views16 reactionsread 12 August 2026
Photo

DAPPOS, Intent-centric dApp Interaction 전체 구조 정리 10/10 이번 기술을 하나로 연결하면 이런 흐름으로 볼 수 있음. 사용자 목표 ↓ 필요한 dApp Interaction ↓ Contract Calls + Asset Bridging ↓ Step Dependency 처리 ↓ Unified Account의 Cross-chain Assets ↓ Service Provider Authorization ↓ Fee Abstraction ↓ 원하는 결과 DAPPOS는 Intent-centric dApp Interaction을 contract-based wallet 호출과 asset bridging을 결합하고, 여러 중간 단계와 dependency가 있는 dApp 작업을 자동화하는 framework로 설명합니다.

16

12 Aug 2026, 06:44 UTC42 views8 reactionsread 12 August 2026
Forwarded from @BChoSNPhoto

부수입 있는 직장인 건보료 오른다 원문 유튜브, X, 슈퍼챗 등 부수입 있는 직장인 주목 급여 외 공제 2000만원에서 1000만원으로 줄어듬

8

Showing the 12 most recent of 144 posts we hold for @CryptoOwlNotice. 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 — 322,374 of 1,160,990entries 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

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.

Names

Channels on the register whose handles appear in this channel's posts.

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.

“크립토 부엉이🦉” (@CryptoOwlNotice), 7,914 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/CryptoOwlNotice.

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.