3 measurements spanning 6 days, net -6. 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 508–516 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
13 Aug 2026, 08:14
509
-6
7 Aug 2026, 01:27
515
no change
7 Aug 2026, 01:02
515
first reading
Engagement
20 posts held, back to 24 June 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 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
20.5%
avg views ÷ 509 subscribers
Avg views / post
105
9 posts measured
Reaction rate
2.44%
reactions ÷ views · ER floor
Posts in window
9
of 20 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 1 August 2026
Posts held
20 (24 June 2026 – 1 August 2026)
Views total
941
Reactions total
23
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 01:02 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
78 reactions across 17 posts, in 5 distinct kinds. The most used accounts for 52.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
41
52.6%
❤
31
39.7%
🔥
4
5.13%
👎
1
1.28%
😢
1
1.28%
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 17 of the 20 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 78reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 24 June 2026 to 1 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.
Neutrl 추천 프로그램이 지금 활성화되었습니다 👀
추천 링크나 초대 코드를 공유하면, 당신이 Season 2에 참여시킨 참가자들이 획득한 Neutrl 포인트의 10%를 보상으로 받습니다.
추천받은 사람은 가입 후 첫 30일 동안 Neutrl 포인트가 5% 추가 보너스를 받습니다.
시작하려면 Rewards → Season 2로 이동하세요.
Neutrl 추천 프로그램(Season 2) 핵심요약
- 개요: 참가자별 고유 추천 링크/초대코드 공유로 추천 보상 획득 가능.
- 추천자 보상: 추천인이 획득한 Neutrl Points의 10% 자동 지급.
- 피추천자 보너스: 추천 링크로 가입한 참여자는 가입 후 30일간 Neutrl Points 5% 추가 지급.
- 시작 방법:
1. 지갑 연결 → Neutrl 앱 접속
2. Reward…
Neutrl 한국 커뮤니티만의 시즌5 수다주막이 오픈 ‼️
드디어 Neutrl 커뮤니티에도 수다주막이 찾아왔습니다!
수다주막이란👇
- 여러분들은 커뮤니티 활동 (채팅) 을 통해서 "막걸리"를 획득하실 수 있습니다!
- 해당 막걸리는 커피 쿠폰을 얻을 수 있는 래플권이 되어집니다.
- 막걸리를 획득하고 "수다주막 뽑기" 를 채팅방에 치시면 래플을 할 수 있는 페이지가 나오게됩니다!
- 그 후 레플에 참여를 해서 상품을 받아가시면 되십니다!
시즌5 수다주막 오픈✨
여러분들 Neutrl 커뮤니티에서 시즌 2 수다주막이 오픈이 되었습니다!
🗓 시즌5 기간: ~ 08.15 (11:59 PM)
⭐ 시즌5 보상: 커피 30잔
해당 이벤트에 대해서 궁금하신점이 있다면 언제든지 관리자를 태깅해주세요!
여러분들 Neutrl의 커뮤니티를 활성화 시키고 …
Neutrl 한국 커뮤니티 수다주막 시즌4 당첨자 취합🎆
수다주막 당첨자분들은 7/31전까지 @Iceeaaa로 커피를 받으실 휴대전화를 제출해주시면 감사하겠습니다.
🗓 제출 기간: ~07.31
수다주막 Winner List👇
@kys74
@Nann3786
@Seibaba
@yoontongi
@soonuu78
기프티콘 제공은 현재까지 진행된 모든 이벤트를 31일에 취합 후 전송 예정입니다‼️
Neutrl 한국 커뮤니티만을 위한 소규모 깜짝 이벤트 #2 결과 발표 ‼️
다들 Neutrl에 대해 공부가 많이 되었을까요?~
최대한 누군가에게 잘 설명이되어지는 글을 작성해주신 분들로 선정을했습니다!
당첨자👇
@nubchee1
@ksull2758
@bluebird2758
@soonuu78
@n1u2b3c4h5
@minjju88
@rfvi835
@hamjjida
@bum7120
@powerfus
@thinkubell
@Ssssse222
@sogooiii
@DDODDO10
@hungrysiro
@kkminsu2002
@Meentmat
@silverplus81
@simsimh
@dingo2030
당첨자분들은 7/31전까지 @Iceeaaa로 커피를 받으실 휴대전화를 제출해주시면 감사하겠습니다.
기프티콘 제공에는 약 몇일의 시간이 걸릴 수 있…
Delta - Hedged JLP: Neutrl의 최신 수익원 👀
수익은 실재합니다. 리스크는 그럴 필요가 없습니다.
JupiterExchange Perps는 수수료 발생 기준으로 솔라나에서 가장 큰 페퍼츄얼 DEX로, 지난 30일 동안 거래량이 56.9억 달러를 넘었습니다. Jupiter Perps의 모든 거래는 오픈·클로징 수수료, 차입 수수료, 가격 임팩트 등으로 수수료를 생성하며 그중 75%가 JLP 보유자에게 직접 흘러갑니다. 토큰 발행이나 인플레이션성 인센티브는 없고, JLP는 프로토콜 내부의 네이티브 SOL 스테이킹 및 대출 활동으로부터도 수익을 얻어 여러 실질적 수익원이 존재합니다. 현재 APY는 약 12% 수준으로 전적으로 프로토콜 수수료에서 지급됩니다.
기회는 명확합니다. 도전은 JLP에 내재된 방향성 노출을 제거하면서 …
Neutrl 한국 커뮤니티만의 시즌4 수다주막이 오픈 ‼️
드디어 Neutrl 커뮤니티에도 수다주막이 찾아왔습니다!
수다주막이란👇
- 여러분들은 커뮤니티 활동 (채팅) 을 통해서 "막걸리"를 획득하실 수 있습니다!
- 해당 막걸리는 커피 쿠폰을 얻을 수 있는 래플권이 되어집니다.
- 막걸리를 획득하고 "수다주막 뽑기" 를 채팅방에 치시면 래플을 할 수 있는 페이지가 나오게됩니다!
- 그 후 레플에 참여를 해서 상품을 받아가시면 되십니다!
시즌4 수다주막 오픈✨
여러분들 Neutrl 커뮤니티에서 시즌 2 수다주막이 오픈이 되었습니다!
🗓 시즌4 기간: ~ 07.29 (11:59 PM)
⭐ 시즌4 보상: 커피 30잔
해당 이벤트에 대해서 궁금하신점이 있다면 언제든지 관리자를 태깅해주세요!
여러분들 Neutrl의 커뮤니티를 활성화 시키고 …
"Neutrl" 의 🇰🇷 한국 공식 커뮤니티가 오픈 이벤트 결과 발표 ‼️
정말 많은분들이 Neutrl 한국 커뮤니티 오픈이벤트에 참여를 해주셨습니다 💡
당첨자 발표👇
@jasontori
@Seibaba
@ijongchan689849
@teledooom
@parkgwangeun
@CHY8821
@ych8404
@code42
@omoiharane2
@vancity5060
@supmobis
@cookie852
@sunginlee
@Hellchang2
@mskim90
@miltung
@Hoya81
@penguin1487
@aessida
@Hyunah007
@kimjun8187
@dreamrich7
@lsy102102
@namal111
@skw3993
@coolima1128
@yh20210333
@tnwjdv13
@dhl1013
@jh921005…
pendle_fi에서 YT-sNUSD로 더 많은 수익을 얻으세요.
YT 구매자는 기본 수익과 강화된 sNUSD 인센티브를 통해 11% APY를 받을 수 있으며, Pendle 리미트 오더 인센티브로 추가 보상을 제공합니다.
모두 25배의 Neutrl 포인트를 얻으면서 가능합니다!
✅ Twitter: Link
Neutrl 한국 커뮤니티만을 위한 소규모 깜짝 이벤트 #2 ‼️
Neutrl 한국 커뮤니티만을 위한 특별한 이벤트2를 준비했습니다!
뉴트럴 프로젝트를 누군가에게 소개하는 글을 작성해주세요 🎆
🎁 보상: 가장 잘 설명해주신 20명을 선정해서 커피를 지급해드립니다!
🗓 일정: ~ 07.22
참여를 희망하시는분들은 해당 공지글에 댓글로 설명글을 작성해주세요!
다들 좋은 소개글 기대하겠습니다 💡
Neutrl 한국 커뮤니티만을 위한 소규모 깜짝 이벤트 #1 결과 발표 ‼️
정말 많은분들이 재미있는 3행시를 준비해주셨습니다!
Neutrl에 대해서 잘 설명하고, 잘 참여를 해주신 20명을 선별해서 이벤트 당첨자 발표를 하겠습니다!
당첨자👇
@teledooom
@Seibaba
@cold8day
@cocorang
@bluebird2758
@ksull2758
@sunginlee
@n_tester
@Soeolinyoung
@soonuu78
@Nann3786
@tamay544
@mitpast
@hungrysiro
@powerfus
@albammakuly
@silverplus81
@minjju88
@veruchesu
@hamjjida
당첨자분들은 7/20전까지 @Iceeaaa로 커피를 받으실 휴대전화를 제출해주시면 감사하겠습니다.
기프티콘 제…
Neutrl의 수익 엔진은 시장에 맞춰 진화하도록 설계되었습니다 👀
sNUSD는 세 가지 서로 다른 수익원에서 수익을 창출해, 자본이 단일 수익원에 의존하지 않고 각기 다른 수익 동인을 가진 시장중립 전략 사이를 회전할 수 있게 합니다.
✅ Twitter: Link
❤3
Showing the 12 most recent of 20 posts we hold for @neutrlkr. 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 — 836,979 of 1,176,251entries 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.
Mentions
Named by 2 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.
Handles this channel named that no longer answer
Dead references
2
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
2
vacant every time we have ever looked
@neutrlkr named 2 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@blockkim86 named in 1 post, 8 August 2026 – 8 August 2026
@gandan33622 named in 1 post, 8 August 2026 – 8 August 2026
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 13 August 2026 — this
entry's latest reading, not the date you are reading this.
“Neutrl 한국 공지방” (@neutrlkr), 509 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/neutrlkr.
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.