【时间】:2026-08-08 【老师】:甜甜 【留名】:影子骑士 【人照】:9 【态度】:9 【环境】:9 【综合】:9 【过程】:嗯,竞拍拿下这位杭州来的老师,见面看到老师,脸要比频道宽一些,但是人还是挺好看,说话也很有元气,不像一些精神小妹,说话有气无力,给了水费后一起洗澡。 老师皮肤比较白的,身上摸上去软软的,身材比较匀称,不是竹竿,全身上下没有任何纹身。 之后我们到了床上,老师给我舔胸、口,弄的非常舒服。之后带套进入,老师里面水很多,抽查时越抽插越润,而且因为身体柔韧的原因,所以可以正面一字马,不过不会女上的一字马,我听说有些瑜伽老师,可以进行竖劈跟横劈的女上一字马,可惜我也没这种经验,只能期盼有能力的狼友去教导一下了,换了两个体位之后出货走人。 苏州茶馆主群 https://t.me/szcgll @QiangSbot 机器人查车评,例如 : 某某某 茶馆不给任何老师担保收取定金!被骗请自理!

Channel
甜甜的朋友圈
@xuelidepengyouquan1
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
533subscribers
+414 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1004496061318 |
|---|---|
| Type | Channel |
| Username | @xuelidepengyouquan1 |
| Created | 2 August 2026 — measured — dated from the channel’s first post |
| First recorded | 9 August 2026 |
| Last confirmed live | 26 September 2026 |
| Measurements held | 8 |
| Confirmed unchanged | 1 time, most recently 26 September 2026 |
| On Telegram | t.me/xuelidepengyouquan1 |
Topic
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 21 September 2026 and assigned it the closest of 31 fixed categories, at 62% 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.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 26 Sept 2026, 18:19 | 533 | -8 |
| 13 Sept 2026, 19:41 | 541 | +7 |
| 5 Sept 2026, 04:59 | 534 | +48 |
| 27 Aug 2026, 21:02 | 486 | +38 |
| 21 Aug 2026, 03:22 | 448 | +187 |
| 14 Aug 2026, 12:28 | 261 | +142 |
| 9 Aug 2026, 04:17 | 119 | no change |
| 7 Aug 2026, 12:41 | 119 | first reading |
Engagement
11 posts held, back to 2 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 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 11 posts for this entry, the most recent from 9 August 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
- Video runtime
- 6s
- Average length
- 6s
Measured directly from 1 video 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.
Reaction mix
24 reactions across 7 posts, in 4 distinct kinds. The most used accounts for 58.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 14 | 58.3% | |
| 🔥 | 7 | 29.2% | |
| 👍 | 2 | 8.33% | |
| 🥰 | 1 | 4.17% |
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 7 of the 11 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 24 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 11 most recent posts we hold, published 2 August 2026 to 9 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
四项自测已做宝贝们放心约
今天只有少量空位了宝贝们
🥰1
开课了可以预约了宝贝们
🔥2❤1
开课啦宝宝们
❤2
今天🈵啦宝贝们,明天12点准时开课
开课啦✔可以预约了宝贝们
❤4
杭州嫩妹馆报告:https://t.me/hznmg23 【 推荐程度】#好评 【优势】年轻 【不足】公寓 【出击时间】:8月3日 【老师花名】:#雪梨 @xuelidexiaowu 【出击留名】:我想静静 【开课位置】:拱墅 【修车水费】:工兵p 【高端服务】:有的打✅ ① 舌吻 ✅ 管够 ② 69舔逼 ✅ 有 ③ 情绪价值 ✅ 情绪价值给足 ④ 毒龙 【人照评价】:9分,人照相似 【身材评价】:好 【胸部评价】:b,手感不错 【鲍鱼评价】:好 【服务评价】:不过分的都会满足 【态度评价】:态度好 【环境评价】:是公寓,隔音有点不行,不过老师说后面会换小区 【过程描述】: #流程细节:看见上了新工兵,马上就报了名和老师约好了时间,本来约了中午的时间,联系老师10:30有空,交了水费聊了下天就去洗澡了,老师帮忙洗,非常不错,洗完以后躺在床上,老师的奶头非常挺,摸起来手感刚刚好一个…
❤4
#杭州肯德基 https://t.me/kdjqjt #好评 93 【出击时间】:8.3 【出击老师】:雪梨 【联系方式】:@xuelidexiaowu 【出击留名】:再见 【开课位置】:拱墅区公寓-过几天会换到小区 【修车水费】:工兵P 【人照评分满分10】:9(人照相似度/妆容) 【颜值评分满分20】:19(耐看/高颜值/一般) 【身材评分满分10】:9(皮肤白/身材不错/罩杯B+) 【吊感评分满分20】:19(户型蝴蝶/紧度不错/水量多/包裹感好) 【服务评分满分10】:9(女友感) 【态度评分满分20】:20(主动询问/自觉服务) 【环境评分满分10】:8(公寓,环境不错,门口没探头) 【综合评分满分100】:93(以上相加) 情绪价值描述: 女友感不错的,比较细心 【过程描述】:有的打 舌吻 OK 口活 OK 69舔穴 OK 毒龙 【过程描述】:过程小黄文(200字以上) 听闻老师荣登榜单…
❤3
拱墅区开课啦宝贝们 可以预约
🔥5👍2
Channel created
Showing the 11 most recent of 11 posts we hold for @xuelidepengyouquan1. 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.
Forward network
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.
Mentions
Named by 3 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.
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 26 September 2026 — this entry's latest reading, not the date you are reading this.
“甜甜的朋友圈” (@xuelidepengyouquan1), 533 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/xuelidepengyouquan1.
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.