📱 评论区输入【云夕】,一起来上一堂标准的臀浪课~ 🎙 有性瘾的御姐云夕,后入时臀浪阵阵起舞,给没体验过的狼友带来感观上的一些新提升。稿子是集美一个老狼提供过来的,感谢仁兄和云夕一起给大家做了一堂教学~ ✈️ 车牌: @Yunxibabay 🤖 机器人: @Yx808_BOT #御姐 #臀浪 #服务 #视频 杭州交流群 : @JMNU0571a 【集美师范大学】视频合集
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Channel
@JMNU0571
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Cite this entry
14,501subscribers
+536 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002518186838 |
|---|---|
| Type | Channel |
| Username | @JMNU0571 |
| Created | Between 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 September 2026 |
| Measurements held | 28 |
| Confirmed unchanged | 1 time, most recently 5 September 2026 |
| On Telegram | t.me/JMNU0571 |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Sept 2026, 10:58 | 14,501 | +105 |
| 3 Sept 2026, 16:38 | 14,396 | -747 |
| 2 Sept 2026, 10:44 | 15,143 | +125 |
| 1 Sept 2026, 08:14 | 15,018 | +53 |
| 31 Aug 2026, 05:55 | 14,965 | +44 |
| 30 Aug 2026, 07:16 | 14,921 | +8 |
| 29 Aug 2026, 04:45 | 14,913 | +29 |
| 28 Aug 2026, 02:04 | 14,884 | +34 |
| 27 Aug 2026, 01:36 | 14,850 | +32 |
| 25 Aug 2026, 22:53 | 14,818 | +45 |
| 25 Aug 2026, 00:13 | 14,773 | +68 |
| 23 Aug 2026, 12:24 | 14,705 | +136 |
| 21 Aug 2026, 21:18 | 14,569 | +30 |
| 20 Aug 2026, 19:13 | 14,539 | +33 |
| 19 Aug 2026, 21:34 | 14,506 | +31 |
| 18 Aug 2026, 20:54 | 14,475 | +36 |
| 17 Aug 2026, 20:23 | 14,439 | +35 |
| 16 Aug 2026, 22:29 | 14,404 | +63 |
| 15 Aug 2026, 13:58 | 14,341 | +54 |
| 14 Aug 2026, 01:16 | 14,287 | first reading |
56 posts held, back to 4 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 52 pages of 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 3 September 2026 |
|---|---|
| Posts held | 56 (4 August 2026 – 3 September 2026) |
| Views total | 107,370 |
| Reactions total | 18,338 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 10:56 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 27 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.
19,684 reactions across 56 posts, in 16 distinct kinds. The most used accounts for 20.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 3,994 | 20.3% | |
| 🔥 | 3,917 | 19.9% | |
| 🎉 | 3,890 | 19.8% | |
| 😁 | 3,869 | 19.7% | |
| 👍 | 3,722 | 18.9% | |
| 💯 | 49 | 0.249% | |
| 🦄 | 48 | 0.244% | |
| 🎄 | 47 | 0.239% | |
| 🐳 | 43 | 0.218% | |
| ☃ | 36 | 0.183% | |
| 🏆 | 22 | 0.112% | |
| 🆒 | 16 | 0.081% | |
| 💘 | 16 | 0.081% | |
| 🤗 | 8 | 0.041% | |
| 🤩 | 5 | 0.025% | |
| 👏 | 2 | 0.01% |
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 56 of the 56 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 19,684 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 56 most recent posts we hold, published 4 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.
📱 评论区输入【云夕】,一起来上一堂标准的臀浪课~ 🎙 有性瘾的御姐云夕,后入时臀浪阵阵起舞,给没体验过的狼友带来感观上的一些新提升。稿子是集美一个老狼提供过来的,感谢仁兄和云夕一起给大家做了一堂教学~ ✈️ 车牌: @Yunxibabay 🤖 机器人: @Yx808_BOT #御姐 #臀浪 #服务 #视频 杭州交流群 : @JMNU0571a 【集美师范大学】视频合集
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艺名: #卡卡(韩系女大) 段位: #辅导员(青铜) 地点: #杭州 仅上门 编号: #JMHZ1243 👥【集美推荐官】:大D 📢【校长观察】 真实,女大,气质,仅上门 谈吐,文雅,灵魂,只夜天 韩系,高级,馒头,极品穴 🎁 今天校长教大家,什么叫做不劳而获,而且获的还是精品,哈哈,实在没时间找好品这几天,发现大屌兄那有好货,直接搬过来了😝,确实不错,素人女大,气质谈吐,而且我最喜欢能实在在自己课表标对A的选手,真诚永远是必杀技,校长天然对A有好感,哈哈哈,真实女大,有谈吐有气质,极品馒头管饱~赶紧滴,这种学生们,说不定哪天就约不到了~ 💎 车架:164/49/A 🪙 车费:3000夜/4000天(只上门,不接单p,不用叨扰询问) ✈️ 车牌: @kaka5750 🤖 机器人: @kakababy003bot #女大 #上门 #视频 #馒头 #一线天 #气质 杭州交流群 : @JMNU0571a 【集美师范大学】视频合
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💋【塞壬】,口碑两极分化的巨乳萝莉反差嫩妹 看似bbw,实则小鸟依人 看似有肉肉,实则紧致嫩滑 看似满背大腿,实则社恐i人 看似冷淡不热情,实则性开放程度非常高 只是,你,会不会打开她那个开关。开关即开,塞壬会化身充满赛博朋克风格的性欲使者,配上馒头一线天,紧致挺拔的D奶,实乃隐藏款炮架~往期视频可以欣赏塞壬女上的魅力(视频后台修复好大家再行观看,已完成百分之60),塞壬是反差天花板。 i是真i,连自己用哪些照片都选不好,还得让校长帮忙选😂 ✈️ 车牌: @sairen996 🤖 机器人: @Siren_illusion_bot #白虎 #一线天 #萝莉 #符文 #嫩妹 #反差
🎉122❤108🔥101👍87😁84☃1🎄1🏆1
🤪🔥《开学季推荐》🔥🤪 开学季精品老师推荐暨《集美师大第三期推荐》 现已出炉,今日仅做图片预告和车牌展示,明日会放上往期推荐名单及本期推荐详情~ 以下排名不分先后,《集美师范大学》老师的品质是有目共睹的,所以集美师大每期推荐的含金量大家懂的都懂~ 1️⃣天然巨乳E奶萝莉——云朵 2️⃣义乌甜妹名师——虎妹 3️⃣邻家小妹天花板——小仙女 4️⃣极致的骚又纯——鱼鱼 5️⃣温柔超标的BBW——大女人思予 6️⃣妖艳女王——温软软 7️⃣灵魂伴侣——小圆 8️⃣服务系天花板——御姐酥酥 #杭州 #推荐 #出击小红书
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⭐️ 今天是2026年9月1日,新的学期又来了,各位同学们,开学快乐😝 酷热的暑假,说来就来的台风,严重影响了广大同学迫切求学的悸动,不管是荷包的原因,还是天气的缘故,总之9月来了,开学啦,入秋了。 俗话说“金九银十”,到了收获的季节,大家荷包可以鼓起来了,可以为自己心怡老师的课堂买单啦。 新学期,也该有新的气象,稍后为大家精选出九月推荐课堂,都是经过多位靠谱狼友验证好评的精品课堂,各位狼友,走起~
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Telegram必备的搜索引擎,极搜JISOU帮你精准找到,想要的群组、频道、视频、音乐 👉 t.me/jisou2?start=a_8205892302
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📱 评论区输入【阳阳】,欣赏嫩妹萝莉和爸爸的日常~ 🆒 大D先生冒着掉头发的风险连夜剪出来的片,赶紧一起来看吧,是真不错。08年的身体是真的好,又嫩又白又粉,价格亲民再亲民,皮肤白皙🐻柔软,小屁股翘翘,啥都好~又乖巧懂事,赶紧弄回家去~ ✈️ 车牌: @xiaoyan0211 🤖 机器人: @xiaoyan021_bot #萝莉 #舌钉 #蟒蛇 #水娃 #嫩妹 #洛丽塔 杭州交流群 : @JMNU0571a 【集美师范大学】视频合集 新视频正常观看(如遇未回复,属机器人正常故障,换个时间来看就行),老视频已经修复部分,完全修复会进行正式通知
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艺名: #阳阳(白幼瘦福利姬) 段位: #辅导员(青铜) 地点: #余杭区 编号: #JMHZ1242 👥【集美推荐官】:大D,又是一个28/38,杭州性价比天花板福利姬。 📢【校长观察】 白虎,馒头,萝莉,三点粉 幼小,白皙,翘臀,ootd 穿搭,女儿,听话,真的好 🎁 大D今天刚去就被我发现了😂😂,原话是:“好逼,我必须得推一下”。08年标准白幼瘦,无毛白虎,粉嫩馒头,超级水娃,言听计从、乖巧懂事,屁屁还有点小翘,属于是人见人爱了,萝莉届的ootd,2800/3800,杭州性价比天花板了,小女友体验感无敌,这还不去,更待何时? 💎 车架:164/49/B 🪙 车费:2800(夜)/3800(天) ✈️ 车牌: @xiaoyan0211 🤖 机器人: @xiaoyan021_bot #萝莉 #福利姬 #杭州 #白虎 #白幼瘦 杭州交流群 : @JMNU0571a 【集美师范大学】视频合集
❤120😁109🔥100🎉98👍86☃1🎄1🏆1
🤪 评论区视频紧急修复中,从新到旧开始修复,预计两天全部完成,暂时观看不了的部分还请各位看官多担待,给你们带来的不便校长深表歉意,下周会给大家发一个小福利活动~
🔥92🎉90👍88❤85😁78🎄2☃1🐳1
艺名: #娜娜(外围自聊) 段位: #辅导员(青铜) 地点: #萧山区 编号: #JMHZ1241 👥【集美推荐官】:老师自荐,待群友品鉴 📢【校长观察】 大圈,外围,气质,服务系 六九,毒龙,冰火,能口爆 酥胸,嫩滑,长腿,小尤物 🎁 大圈转过来的老师,服务向莞式靠拢,69、毒龙、冰火样样精通,硬件配置高,170大长腿,C+软糯🐻,皮肤细腻,攻速鞋丝袜配备齐全,欢迎各位前去品鉴。一般大圈转过来的,素质都不会差,老师特地发放两张7折优惠券,有意向的同学请联系校长,或在交流群拍卖兑换~ 💎 车架:170/52/C+ 🪙 车费:1500/2800 ✈️ 车牌: @xiana123457 🤖 机器人: @xiana12345_bot #大圈 #外围 #御姐 #身材 杭州交流群 : @JMNU0571a 【集美师范大学】视频合集
😁114❤108👍102🎉98🔥96🐳2🆒2☃1
💐💐💐开奖了💐💐💐 175D川渝御姐的夏日福利 开奖了! 本期总参与人数: 83 1- Chris Wang 获得:尤娜单p优惠券(面额200) 2- shigure DD 获得:尤娜单p优惠券(面额200) 3- su meng 获得:尤娜单p优惠券(面额200) 4- chen chen 获得:尤娜x小小夏双飞优惠券(面额400) 谨祝中奖用户大吉大利万事顺意
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🎙 今天一起看小说+吃瓜,不懂的在校长这里都能读懂~ 先带大家认识下【孙宇晨】,B圈大佬,北大毕业,2017年创立波场tron,也就是你们平时用冷钱包在链上扭转虚拟货币所常用的能量;2024年花620万美金购买一只胶带香蕉;2025年完成亚轨道太空飞行;2026年发文撰写《我的女友景田》,瞬间全网爆炸; 景田就不多赘述了,网传背景非常之硬,但是狠瓜一直不断,张继科、孙宇晨。 建议大家先阅读原文。这个事件有意思的几个点: 一是币圈大佬问claude该不该给这5000万美金,claude给出的建议是不给,冷静如刀锋一般的ai啊; 二是孙宇晨的小作文水平,直逼高中时期的韩寒,大有剑指《新概念》之势; 三是“妈妈”一词,再次爆红,很多狼友都喜欢喊老师们妈妈,原来景田也享受,孙宇晨跟大家一样,也喜欢😝; 四是王思聪与孙宇晨掐架的这十几年,最终看来,是孙宇晨赢了,王思聪真就是个小卡拉米; 大家看完故事可以理性讨论一下,这作文该…
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Showing the 12 most recent of 56 posts we hold for @JMNU0571. 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.
@JMNU0571 edited 1 post 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.
Named by 8 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.
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.
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 5 September 2026 — this entry's latest reading, not the date you are reading this.
“【集美师范大学】杭州校区(上海后援)” (@JMNU0571), 14,501 subscribers as measured 5 September 2026. Telegram Register, tgregister.com/channel/JMNU0571.
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.