Channel
@OrderlyNetwork_kr
On this record: Also posting the same content · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
1,718subscribers
+3 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1002154563741 |
|---|---|
| Type | Channel |
| Username | @OrderlyNetwork_kr |
| Created | Between 1 June 2024 and 30 September 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 14 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 14 August 2026 |
| On Telegram | t.me/OrderlyNetwork_kr |
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Aug 2026, 04:57 | 1,718 | +11 |
| 10 Aug 2026, 14:33 | 1,707 | -8 |
| 7 Aug 2026, 04:27 | 1,715 | no change |
| 7 Aug 2026, 01:02 | 1,715 | first reading |
23 posts held, back to 27 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 2 pagesof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 7 August 2026 |
|---|---|
| Posts held | 23 (27 July 2026 – 7 August 2026) |
| Views total | 3,500 |
| Reactions total | 89 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 17:41 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.
Measured directly from 4 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
89 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 93.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 83 | 93.3% | |
| 👍 | 4 | 4.49% | |
| 🥰 | 2 | 2.25% |
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 23 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 89reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 27 July 2026 to 7 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.
📣 Builders Marketplace, 새로운 Starchild 모듈 추가! 이제 몇 분 만에 여러분의 Perp DEX에 개인 AI 에이전트를 붙일 수 있습니다. 트레이더는 거래 화면을 벗어나지 않고도 아래 기능들을 바로 이용할 수 있습니다: 🟣 포지션 분석 🟣 크로스체인 시장 리서치 🟣 Coinglass, CoinGecko, DefiLlama, DeBank, TwelveData, TAAPI 등 80개 이상의 스킬 DEX 안에서 거래와 AI 기반 시장 분석을 한 번에 이용해 보세요! 🖥 Builders Marketpalce
❤3👍1
대부분의 경우 AI는 좋은 판단을 내리고도 마지막 단계에서 멈춥니다. 분석은 끝났지만, 실제 주문을 실행할 수 있는 연결이 없기 때문입니다. Orderly MCP는 그 빈 공간을 메웁니다. Claude·Codex·Starchild 같은 AI 에이전트가 오덜리의 거래 인프라와 연결되어 전략을 세우고, 필요한 작업을 실행할 수 있도록 합니다. 사람이 직접 여러 과정을 거쳐 실행하던 거래를, 이제 AI 에이전트가 하나의 흐름 안에서 이어갈 수 있습니다. 전략을 세우고, 포지션을 모니터링하고, 사용자가 설정한 조건에 따라 주문을 실행하는 자동화까지 하나의 흐름으로 연결됩니다. 여기에 Orderly One을 더하면 가능성은 더 넓어집니다. 나만의 금융 시장을 만들고, 주식·지수·원자재 등 RWA 시장까지 하나의 공유 유동성 인프라 위에서 운영할…
❤5
📣 Perp Anything Perp Anything으로 직접 Perp 마켓을 만들고, 단 몇 단계 만에 400개 이상의 Orderly 기반 DEX에 상장하세요. 💵 마켓 크리에이터가 가져가는 수익: • 거래 수수료 50% • 청산 수익 100% • 마켓이 성장할수록 지속적으로 쌓이는 리워드 크립토든 주식이든 원자재든, 가격만 있다면 무엇이든 Perp 마켓이 됩니다. 마켓 크리에이터가 되어 원하는 마켓을 출시해보세요 🦦
❤3
Orderly Korea 공지방 pinned a video
🔔 $PAXG, $ZHIPU 신규 상장 알림 이제 $PAXG와 $ZHIPU를 오덜리 기반 덱스에서 만나보실 수 있습니다. 금부터 AI 주식까지, 130개 이상의 마켓으로 나만의 Perp DEX를 런칭하거나 원하는 마켓을 직접 출시해 수익을 창출하세요! 🖥 퍼프덱스에 필요한 모든 툴, Orderly One
❤5
봇한테 맡겨두고 여행갈까요? 💜 Orderly x Tread.fi NAS100 마켓메이킹 대회 자동화된 마켓메이킹 전략을 실행하고, NAS100 시장에 유동성을 공급하며 총 $5,000의 상금에 도전해 보세요 🟣총 상금: $5,000 🟣진행 기간: ~8월 16일 🟣보상 기준: 봇 가동 시간(Uptime) + 실제 체결 거래량 참여방법은 해당 트윗을 확인해 주세요 풀영상은 요기에! 응원 댓글 달아주시면 감사합니다❤️
❤6
🔔 2주 연속 거래량 1위는 Vanta! Orderly 기반 무기한 선물 덱스들도 빠르게 성장하며, 지난 한 주 동안 약 10만 달러의 수수료를 창출했습니다. Orderly는 다양한 무기한 선물 시장을 구축할 수 있는 인프라를 제공하며 생태계를 확장해 나가고 있습니다. 👉 오덜리의 모든 퍼프 마켓 인프라
❤6
📌 Orderly Q2 커뮤니티 콜 리캡 - 원문링크 주요 지표: 🟣 거래량 55억 달러(QoQ +17%) 🟣 빌더 수익 250만 달러(QoQ +262%) 🟣 93개의 신규 DEX가 정식 출시 🟣 DEX 출시 비용 10달러로 인하 🟣 Perp Anything 출시 새롭게 출시된 주요 기능: 💠 Perp Anything 자체 DEX와 출금 가능한 25,000달러의 담보를 기반으로, 누구나 원하는 무기한 선물 마켓을 상장할 수 있습니다. 상장자는 Orderly 기반 400개 이상의 DEX에서 발생하는 해당 마켓의 청산 수익 100%와 Orderly 측 거래 수수료 50%를 받을 수 있습니다. 현재 50개 이상의 마켓이 출시되었습니다. 💠 Permissionless Vaults 전략을 구성하고 API를 연결하면, Orderly 기반 DE…
❤4
Orderly Korea 공지방 pinned a video
📣 Orderly x Tread.fi 마켓메이킹 대회 트레이딩에서 잠시 휴가가 필요하지 않으신가요? 그렇다면 Orderly와 Tread.fi의 마켓메이킹 대회에 참여해보세요. 🟣총 상금: $5,000 🟣진행 기간: 2026년 8월 3일 - 17일 🟣보상 기준: 봇 가동 시간(Uptime) + 실제 체결 거래량 몇 번의 클릭만으로 자동화된 마켓메이킹 봇을 세팅하고 보상을 받아가 보세요. 자세한 참여방법은 해당트윗을 참고해주세요!
❤5
👑 Orderly × Tread.fi 마켓 메이킹 대회가 시작되었습니다! 자동화된 마켓메이킹 전략을 실행하고, NAS100 시장에 유동성을 공급하며 총 $5,000의 상금에 도전해 보세요! 🟣총 상금: $5,000 🟣진행 기간: 2주 🟣보상 기준: 봇 가동 시간(Uptime) + 실제 체결 거래량 참여 방법: 1. app.tread.fi에서 계정을 생성합니다. 2. 지갑 아이콘을 클릭한 뒤, Orderly 기반 Perp DEX 계정을 연결합니다. 3. Bots → Market Maker Bot으로 이동합니다. 4. Campaigns를 선택합니다. 5. 원하는 시간 슬롯과 전략을 선택합니다. 6. 봇 실행 일정을 예약합니다. 자세한 참여 방법은 원문 게시물을 확인하고, 지금 바로 나만의 마켓 메이킹 전략을 시작해 보세요!
❤4
Orderly Korea 공지방 pinned a video
Showing the 12 most recent of 23 posts we hold for @OrderlyNetwork_kr. 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 — 124,077 of 1,189,255entries 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.
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.
Named by 5 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.
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.
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 14 August 2026 — this entry's latest reading, not the date you are reading this.
“Orderly Korea 공지방” (@OrderlyNetwork_kr), 1,718 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/OrderlyNetwork_kr.
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.