李新野和孙宇晨 以每小时一篇的速度 开启了疯狂的飞龙骑脸式输出 清华北大还是有说法的 人才济济!
❤1

Channel
@Quartet_Reviews
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
12,203subscribers
+442 since we began measuring on 10 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1003152715154 |
|---|---|
| Type | Channel |
| Username | @Quartet_Reviews |
| Created | Between 1 September 2025 and 30 November 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 10 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 29 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/Quartet_Reviews |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 72% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 06:17 | 12,203 | +30 |
| 15 Sept 2026, 21:39 | 12,173 | +10 |
| 14 Sept 2026, 05:39 | 12,163 | +20 |
| 12 Sept 2026, 11:21 | 12,143 | +54 |
| 10 Sept 2026, 10:16 | 12,089 | +88 |
| 7 Sept 2026, 02:20 | 12,001 | +30 |
| 4 Sept 2026, 10:00 | 11,971 | +39 |
| 2 Sept 2026, 20:42 | 11,932 | +14 |
| 1 Sept 2026, 18:04 | 11,918 | +12 |
| 31 Aug 2026, 19:16 | 11,906 | +19 |
| 30 Aug 2026, 21:02 | 11,887 | +13 |
| 29 Aug 2026, 18:44 | 11,874 | +5 |
| 28 Aug 2026, 16:05 | 11,869 | +11 |
| 27 Aug 2026, 13:25 | 11,858 | +10 |
| 26 Aug 2026, 11:23 | 11,848 | +5 |
| 24 Aug 2026, 11:29 | 11,843 | +13 |
| 22 Aug 2026, 22:27 | 11,830 | +10 |
| 21 Aug 2026, 10:03 | 11,820 | +13 |
| 20 Aug 2026, 07:06 | 11,807 | +19 |
| 19 Aug 2026, 04:34 | 11,788 | first reading |
36 posts held, back to 3 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 37 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. It is computed over the 13 of 14 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 2 September 2026 |
|---|---|
| Posts held | 36 (3 August 2026 – 2 September 2026) |
| Views total | 41,380 |
| Reactions total | 70 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 2 Sept 2026, 23:21 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 8 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.
155 reactions across 28 posts, in 7 distinct kinds. The most used accounts for 75.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 117 | 75.5% | |
| 🤡 | 22 | 14.2% | |
| 😭 | 7 | 4.52% | |
| 👍 | 5 | 3.23% | |
| 💔 | 2 | 1.29% | |
| 👏 | 1 | 0.645% | |
| 🙈 | 1 | 0.645% |
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 32 of the 36 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 155 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 36 most recent posts we hold, published 3 August 2026 to 2 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.
李新野和孙宇晨 以每小时一篇的速度 开启了疯狂的飞龙骑脸式输出 清华北大还是有说法的 人才济济!
❤1
包养中介乱象丛生,价格战打得飞起,却把最核心的“售后”抛到脑后? 👉人到了发现是“照骗”? 👉钱转过去她不让碰? 👉直接跑路怎么办? 👉中介到底靠不靠谱? 👉介绍费会不会打了水漂? 🥳初见包养 ——彻底告别这些糟心事! 😜全网🙁纠纷记录 😜万人真实交流群,评价看得见、摸得着 😜见面面试期不满意,100%退介绍费 😜包见面、包上床,货不对板包退包换 会员😊😊😊元 首单折扣😊😊😊, 首单介绍费 😊折。 一万出头即可安排颜值少萝伴游同居 3 天。 😢🤗😳😳: @chujian90 萝莉频道: https://t.me/+Dwvo2Hgqq5Q2MTNl 交流群: @cji888 选妃机器人: @chuj9_bot 搜索机器人: @cjian888_bot
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❤1
N142 【妹子花名】:#苏星瑶 @suxingyao6688 【标签归类】:#800p #颜值系 #身材系 #甜妹 #御姐 #班花级 #水蛇腰 #腿玩年 #口交达人 #小穴美味 #女友感 #深喉 【体验细节】:人照88成、170身高,c罩杯,未见纹身。不抽烟、科室干净。进门很热情的打招呼。嘘寒问暖一下,很有活力,主动递水。聊了一会开始帮洗。洗的时候我手就不老师。揉着老师的大C,底座硬,表面软,视觉效果很好。揉着很舒服。洗了一会,老师主动蹲着给我水中萧,很细致,很耐心,洗后帮忙擦干全身,去床上问需要什么情趣。直接原皮上场。舔胸很会,舌头来回左右晃动。能舔胸就把我舔硬,看着牛牛勃起,老师的小手就伸下去来回抚摸,很会,接着顺着开始口牛牛,看着长发美女来回舔棒,按头深喉,很舒适,而且老师很享受做爱,不是急着出货那种。口的时候聊天常常把老师逗笑,让老师舔了舔阴囊,然后开始六九。阴唇大,小蝴蝶。舌尖轻轻包裹着阴唇来回口,无异味好吃,但是…
❤5
有些人适合陪很久,有些人只适合出现在某一个阶段。 究竟是 moment 重要,还是 forever 重要? 我大概很难把性和爱彻底分开了。 可不是所有关系都一定要通往 forever。有些关系注定没有以后,但那个 moment 里的快乐、心动和真诚,我并不觉得廉价。 凌晨三点的维港 凌晨三点的景枫
N141 【妹子花名】:#盐盐 @yanyannj 【标签归类】:#1200p #颜值系 #身材系 #甜妹 #御姐 #网红脸 #大胸 #校花级 #水蛇腰 #蜜桃臀 #腿玩年 #大蟒蛇 #小穴美味 #极致紧润 #性技高超 #女桩机 #能说会道 #女友感 #深喉 【体验细节】:人照九成、166身高,D罩杯,小腿有个小海马纹身。抽电子烟无烟味、科室干净。与其说是御姐,不如说是甜姐把,甜妹御姐。听说又回来短开一下,赶紧在家洗漱干净刷个牙冲,开门又是熟悉的碎花裙,和扑面而来的大胸,看着大白脂肪胸,就开始反复揉捏,非常好rua,特别盐盐还有点小M的感觉,一直要我大力揉,嬉闹了一会,一起去洗澡,洗的时候我继续反复揩油,舌吻,互相抚摸,然后老师蹲下水中,上床以后抱着我开始聊之前休假去哪玩,然后四目含情,开始舌吻,设有互相交织,唇分时候有点拉丝,然后继续互相猛猛舌吻,吻了10分钟左右吧,老师开始口,舌尖一直绕着冠状沟,然后深喉,吞吐,上下猛猛含…
❤4
最喜欢深夜看纯爱文了😭😭😭😭
🤡1
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❤3
N140 【妹子花名】:#布丁 @xjyznj 【标签归类】:#1200p #颜值系 #身材系 #服务系 #御姐 #班花级 #美乳 #蜜桃臀#大蟒蛇 #口交达人 #性技高超 #能说会道 #女友感 #深喉 【体验细节】:人照八五成、165身高,D罩杯,腰部有纹身。抽烟无烟味,科室干净。进门穿着包臀裙迎接。大大咧咧的性格。交了水费,说南京好佛系哦,都要等评价,撅着嘴很好看。一起共浴。大灯有胸模水平,纤体胸。爱不释手了,反复的rua,老师帮忙涂沐浴乳,来回擦身子,然后蹲着开始水中萧,真空包裹,吸得很紧,来回的口,随后上床。我说有没有人说你像Madison Beer ?他说不懂但是很多人说自己像外国人,可惜你不在国外。不然也是白男收割机。先让我躺着背面吸皮过水,然后正面也做全吸皮过水,口的时候含着牛牛半天,没硬起来。老师一脸疑惑,主要是深夜高铁原因….老师来一句那你要吃药吗?我直接坐起来抱着猛蛇,猛吃大奶,硬梆梆了,老师开始口。用棒棒…
❤2
我的老师布丁(新概念版) ————————————- 有些人见面之前,你对她其实没有太多期待。 照片大概八五成,165左右,腰间有纹身。她抽烟,身上却没有烟味。房间也收拾得很干净,干净到让我一度怀疑,凌晨坐了那么久的高铁来到这里,究竟是不是一件值得的事。 门打开的时候,她穿着包臀裙站在那里。 第一眼并没有什么惊天动地。 只是后来回想起来,我发现很多事情都是这样。真正让人记住的,从来不是第一眼,而是后面一些毫无意义的小细节。 她性格很大大咧咧。 聊起南京,她撅着嘴说: 南京好佛系哦,都要等评价。 那一瞬间我突然觉得她很好看。 不是照片里的那种好看,也不是精心经营出来的好看,而是一个人在完全没有防备的时候,脸上突然出现了一个很生动的表情。 后来一起洗澡。 灯光很亮,她反而一点都不躲。身材比照片更有冲击力,腰很细,比例也很好。我当时脑子里冒出来一个很奇怪的词——像模特。 可能人在凌晨的时候,判断力本来就会下降。…
❤11🤡2
这就是2007年新概念作文冠军的实力吗 ———————————— 晚上我用手揉她的奶子,被她推开了 她抱着我说,以后别叫我景甜了,叫我妈妈吧。 好,妈妈。 不过妈妈有个禁忌词,那个运动员不能提 我没提,虽然我知道,妈妈背上的纹身是因为运动员纹的。 我用Claude Code读API把所有现金资产跑了一遍,结论是对我不会有任何影响。我又核了一遍。我在Claude Code里输入了问题,Claude说,不要把这五千万美元给她。
❤7
宁婉婉 @wanwanll1998 非标准化 ISO 课程 不是常规的舌吻、六九、三件套流程,也沒有太強的“上课感”。更像是一次幽会。 见面、聊天、靠近,情绪一点点被她带起来。很会给情绪价值,也很懂得怎么制造暧昧感,不急着推进,却能让欲望自己冒出来。 舌吻不是完成项目式地亲几下,而是能吻到难舍难分。距离越来越近,呼吸越来越乱,脑子里原本那些条条框框也慢慢没了。 不是因为颜值身材有多刺激,而是整个人的情绪和欲望都被她调动了起来,会很自然地想继续靠近她,想抱得更紧一点,想让这场幽会再久一点。 高级的老师售卖的不是一套标准化流程,而是一段短暂却足够让人入戏的亲密关系。 有些见面不是为了完成什么。只是两个人靠得太近,恰好让欲望有了名字。
❤5
Showing the 12 most recent of 36 posts we hold for @Quartet_Reviews. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
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.
@Quartet_Reviews named 1 handle 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.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“南京四方测评1.0” (@Quartet_Reviews), 12,203 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/Quartet_Reviews.
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.