📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 微微 💕老师车牌: @Sxk986 🔊双向联系:@weiweihe1per_bot 🪯老师频道:https://t.me/www96963 💮老师简介: 年龄25岁,身高172cm,体重52kg #胸D杯 #御姐 #皮肤白 #水蛇腰 #水多 #大车 #蜜桃臀 #长腿 #长发 #服务系 #身材好 #颜值系 #巨乳 🚪课室位置: #未央区 龙首原 🔮服务标签: #吸蛋蛋 #花式吹箫 #可外出 #角色扮演 #毒龙 #舌滑 #双飞 #制服诱惑 #水中箫 #臀推 #鸳鸯浴 #胸推 #乳交 #cos #艳舞 #上门 #有套爱爱 #指划 #足交 #冰火两重天 #按摩 #无套口 #漫游 ⚜…

Channel
西安满园春色百花榜
@MYCS002
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
22,539subscribers
-483 since we began measuring on 28 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1004318085274 |
|---|---|
| Type | Channel |
| Username | @MYCS002 |
| Created | Between 1 June 2026 and 24 August 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 28 August 2026 |
| Last confirmed live | 17 September 2026 |
| Measurements held | 14 |
| Confirmed unchanged | 1 time, most recently 17 September 2026 |
| On Telegram | t.me/MYCS002 |
Topic
Adult — 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 12 September 2026 and assigned it the closest of 31 fixed categories, at 87% 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 |
|---|---|---|
| 17 Sept 2026, 13:19 | 22,539 | -22 |
| 15 Sept 2026, 15:37 | 22,561 | -60 |
| 13 Sept 2026, 22:36 | 22,621 | -9 |
| 12 Sept 2026, 01:59 | 22,630 | -86 |
| 9 Sept 2026, 18:01 | 22,716 | -114 |
| 6 Sept 2026, 13:35 | 22,830 | -12 |
| 4 Sept 2026, 07:15 | 22,842 | -51 |
| 2 Sept 2026, 18:58 | 22,893 | -72 |
| 1 Sept 2026, 15:06 | 22,965 | -23 |
| 31 Aug 2026, 15:37 | 22,988 | -32 |
| 30 Aug 2026, 13:47 | 23,020 | -3 |
| 29 Aug 2026, 13:43 | 23,023 | +1 |
| 28 Aug 2026, 15:15 | 23,022 | no change |
| 28 Aug 2026, 15:00 | 23,022 | first reading |
Engagement
11 posts held, back to 24 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 12 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.16%
- avg views ÷ 22,539 subscribers
- Avg views / post
- 487
- 11 posts measured
- Reaction rate
- 0.142%
- reactions ÷ views · ER floor
- Posts in window
- 11
- of 11 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. It is computed over the 4 of 11 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 3 September 2026 |
|---|---|
| Posts held | 11 (24 August 2026 – 3 September 2026) |
| Views total | 5,353 |
| Reactions total | 4 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 11:56 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.
What this channel posts
- Video runtime
- 1m 01s
- Average length
- 10s
Measured directly from 6 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.
Reaction mix
4 reactions across 3 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 4 | 100.0% |
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 4 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 4 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 24 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.
Recent posts
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❤1
📊 1条车评,综合评分8.67 好评 100% |人照 8 |服务 9.5 中评 0% |颜值 8 |态度 9 差评 0% |身材 8.5 |环境 9 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 古丽娜 💕老师车牌: @gln_27 🔊双向联系:@gln_27_bot 🪯老师频道:https://t.me/gln_gln27 💮老师简介: 年龄 25 岁,身高165cm,体重 45kg #胸C杯 #御姐 #皮肤白 #水蛇腰 #水多 #短发 #娇小 #蜜桃臀 #长腿 #服务系 #身材好 #颜值系 🚪课室位置: #未央区 大风阁地铁口 🔮服务标签: #吸蛋蛋 #花式吹箫 #限时不限次 #可外出 #野战 #角色扮演 #可69 #毒龙 #舌滑 #吞蛋 #制服诱惑 #水中箫 #臀推 #鸳鸯浴 #胸推 #乳交 #车震 #cos #艳舞 #深喉 #上门 #有…
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 曦曦 💕老师车牌: @axiyan666 🔊双向联系:@axiyan666 🪯老师频道:@axiyan6 💮老师简介: 年龄19岁,身高165cm,体重50kg #胸D杯 #兼职 #皮肤白 #水蛇腰 #水多 #三点粉 #学生 #萝莉 #嫩妹 #颜值系 #巨乳 🚪课室位置: #未央区 红庙坡地铁口 🔮服务标签: #可外出 #胸推 #乳交 #上门 #有套爱爱 #按摩 #漫游 #指滑 #角色扮演 #制服诱惑 ⚜️课费课时: #500p(40分钟) #900百pp(90分钟) #夜课2000 (价格/4个小时限制4次)#可上门 (900可上门/路费100) 满园春色 上榜联系: @…
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 🍅🍅番茄🍅🍅 💕老师车牌: @xafanqie 🔊双向联系:@Fanqieobot 🪯老师频道:@FANQIEYUAN 💮老师简介: 年龄25岁,身高168cm,体重44kg #胸a杯 #皮肤白 #水蛇腰 #水多 #玉足 #娇小 #长腿 #长发 #服务系 #身材好 🚪课室位置: #雁塔区 师范大学 🔮服务标签: #吸蛋蛋 #花式吹箫 #限时不限次 #可外出 #角色扮演 #可69 #毒龙 #舌吻 #舌滑 #性玩具 #吞蛋 #制服诱惑 #水中箫 #喷水 #臀推 #鸳鸯浴 #阴推 #深喉 #上门 #有套爱爱 #指划 #足交 #莞式 #抓龙筋 #冰火两重天 #口爆 #按摩 #无…
📊 1条车评,综合评分8.57 好评 100% |人照 8.5 |服务 8.6 中评 0% |颜值 8.5 |态度 8.7 差评 0% |身材 8.6 |环境 8.5 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 骚妲己 💕老师车牌: @saodaji88 🔊双向联系:@saodajyabot 🪯老师频道:@sdji358 💮老师简介: 年龄28岁,身高168cm,体重45kg #胸B杯 #御姐 #皮肤白 #水蛇腰 #水多#不抽烟 #长腿 #长发 #服务系 #身材好 🚪课室位置: #未央区 祥和居 🔮服务标签: #舌吻 #可69 #无套口 #漫游 #过水 #制服诱惑 #姿势配合 ⚜️课费课时: #500p(45分钟) #900pp(90分钟)#包时(1200/2小时) #夜课 (2500/23:00-8:00/不限)#可上门 (pp起步) 满园春色 …
❤1
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 阿狸 💕老师车牌: @alibaby044 🔊双向联系:@albaby06BOT 🪯老师频道:@albaby066 💮老师简介: 年龄22岁,身高160cm,体重50kg #胸B杯 #不吸烟 #长发 #无纹身 #微胖 #过水 #胸推 #指划 🚪课室位置: #未央区 🔮服务标签: #服务系 #陪洗 #鸳鸯浴 #可舌吻 #无套口 #戴套做 #丝袜情趣 #情绪价值 #待客如男友 #姿势配合 ⚜️课费课时: #500p(40分钟) #900pp(80分钟)#包时(两个小时不限) #夜课 (2000六个小时不限) ps: #可上门 #夜课 #包时 满园春色 上榜联系: @BHy…
❤2
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 莲湖汐涵 💕老师车牌: @Xihan720888 🔊双向联系:@dhhxvxvxbx8889bot 🪯老师频道:@Xihan720888 💮老师简介: 年龄19岁,身高167cm,体重46kg #胸b+杯 #兼职 #皮肤白 #水蛇腰 #水多 #三点粉 #娇小 #蜜桃臀 #长发 #服务系 #身材好 #颜值系 🚪课室位置: #莲湖区 高新尊寓小区 🔮服务标签: #吸蛋蛋 #可外出 #可69 #舌吻 #制服诱惑 #上门 #有套爱爱 #无套口 ⚜️课费课时: #600p (50分钟) #1100pp (90分钟) #包时(3h/1800/不限制) #夜课 (25/8h/无限…
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 嘻嘻 💕老师车牌: @xixidcf 🔊双向联系:@xinxingf_bot 🪯老师频道:https://t.me/xixojkcf 💮老师简介: 年龄18岁,身高165cm,体重45kg #胸C杯 #兼职 #御姐 #皮肤白 #水蛇腰 #水多 #三点粉 #萝莉 #娇小 #蜜桃臀 #嫩妹 #长腿 #长发 #服务系 #身材好 #颜值系 #巨乳 🚪课室位置: #未央区 龙首村附近 🔮服务标签: #舌吻 #裸口 #可69 #陪洗 #制服丝袜 ⚜️课费课时: #600p(45分钟) #1100pp(90分钟) 满园春色 上榜联系: @BHyuanyishi_bot
📊 0条车评,综合评分 好评 0% |人照 |服务 中评 0% |颜值 |态度 差评 0% |身材 |环境 点击查看车评 点击写车评 [西安·满园春色]认证榜单收录 💞老师花名: 奈奈 💕老师车牌: @qiqikjjk 🔊双向联系:@qiqigf_bot 🪯老师频道:https://t.me/qiqifd 💮老师简介: 年龄18岁,身高165cm,体重45kg #胸C杯 #兼职 #御姐 #皮肤白 #水蛇腰 #水多 #三点粉 #萝莉 #娇小 #蜜桃臀 #嫩妹 #长腿 #长发 #服务系 #身材好 #颜值系 #巨乳 🚪课室位置: #未央区 龙首村附近 🔮服务标签: #舌吻 #裸口 #可69 #陪洗 #制服丝袜 ⚜️课费课时: #600p(45分钟) #1100pp(90分钟)#包时(1500/2小时/3次)#夜课 (2500/8小时/4次#可上门 (pp起步…
Showing the 11 most recent of 11 posts we hold for @MYCS002. 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.
Mentions
Named by 1 registered channel — 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 17 September 2026 — this entry's latest reading, not the date you are reading this.
“西安满园春色百花榜” (@MYCS002), 22,539 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/MYCS002.
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.