【验证报告模板】 #感觉车 #态度车 #良家感 #身材车 【花名】:#结衣 【车牌】:@HHA686800 【车费】:4P 7PP 【位置】:#理工大学#东郊记忆 【硬件】:年龄:27 身高:162 体重:103 罩杯: D 【软件】:鸳鸯浴,无⭕️口,啪啪,胸推,指滑,亲胸, 【锐评】:按指引进入教室,老师人照有9分,颜值耐看,。身高163,一身黑色情趣,身材微胖。进门闲聊几句,脱衣帮洗,🐻型不错,手感好。上床趴好,妹妹简单按摩,过水,胸推,整个服务都不错,反复几个来回,很是享受。带套女上,老师体力很好,不停摇,里面又湿又紧,不喊停根本停不下来,直接被老师女上带走。老师属于服务控少妇,服务霸道,态度好,价位合适,服务控狼友适合打卡。

Channel
天府机场验证榜
@tianfuairportvf
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
20,206subscribers
+863 since we began measuring on 17 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1002200573381 |
|---|---|
| Type | Channel |
| Username | @tianfuairportvf |
| Created | Between 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 17 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 23 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/tianfuairportvf |
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 10 September 2026 and assigned it the closest of 31 fixed categories, at 56% 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 |
|---|---|---|
| 18 Sept 2026, 17:54 | 20,206 | +212 |
| 16 Sept 2026, 07:01 | 19,994 | +224 |
| 14 Sept 2026, 14:40 | 19,770 | +51 |
| 13 Sept 2026, 03:59 | 19,719 | -94 |
| 11 Sept 2026, 07:55 | 19,813 | +76 |
| 8 Sept 2026, 15:54 | 19,737 | +126 |
| 5 Sept 2026, 07:16 | 19,611 | +13 |
| 3 Sept 2026, 10:02 | 19,598 | -9 |
| 2 Sept 2026, 00:53 | 19,607 | +3 |
| 1 Sept 2026, 01:48 | 19,604 | +7 |
| 31 Aug 2026, 03:14 | 19,597 | +64 |
| 30 Aug 2026, 00:34 | 19,533 | -57 |
| 29 Aug 2026, 01:44 | 19,590 | +25 |
| 27 Aug 2026, 22:13 | 19,565 | +40 |
| 26 Aug 2026, 23:53 | 19,525 | +18 |
| 25 Aug 2026, 20:23 | 19,507 | -19 |
| 24 Aug 2026, 18:15 | 19,526 | +31 |
| 23 Aug 2026, 02:44 | 19,495 | +17 |
| 21 Aug 2026, 14:59 | 19,478 | +32 |
| 20 Aug 2026, 12:03 | 19,446 | first reading |
Engagement
7 posts held, back to 12 July 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 32 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 0.495%
- avg views ÷ 20,206 subscribers
- Avg views / post
- 100
- 1 post measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 1
- of 7 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.
| Window | Rolling 30 days · latest post in window 2 September 2026 |
|---|---|
| Posts held | 7 (12 July 2026 – 2 September 2026) |
| Views total | 100 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 01:03 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
- 12s
- Average length
- 12s
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
7 reactions across 3 posts, in 2 distinct kinds. The most used accounts for 71.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 5 | 71.4% | |
| 🔥 | 2 | 28.6% |
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 3 of the 7 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 7 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 7 most recent posts we hold, published 12 July 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.
Recent posts
【验证报告模板】 感觉车 #态度车 #良家感 #身材车 【花名】:#穗穗 【车牌】:@suisuiai520 【车费】:4P 6PP 【位置】:#理工大 【硬件】:年龄:30 身高:160 体重:100 罩杯: D 【软件】:洗,口,啪啪,胸推,指滑,亲胸,毒龙 【锐评】:身材好,前凸后翘,无纹身,服务又多又专业,一点不敷衍。在同龄人里面算是很好的身材,同价位里面,服务和态度很顶的。附近的老哥可以去无脑体验,性价比很高
❤1
【验证报告模板】 #嫩妹车 #感觉车 #态度车 #良家感 #身材车 【花名】:#可乐 【车牌】:@keleaiqixiaowu 【车费】:7P 12PP 【位置】:天府二街 【硬件】:年龄:18 身高:155体重:90 罩杯:D 【软件】:陪洗,三件套,浅吻 【锐评】:硬件非常给力的嫩妹,皮肤稍微差可点,偏黑,妆容一般有纹身,稍微有点精神小妹的感觉,可能是开的比较久稍稍有点油偷懒,爱抱着手机耍,不会去顾及客人情绪除了刚进门递了一瓶水让你感觉妹子态度还行,但是缺点有,优点还是有日感不错,很骚,喜欢被插。奶子有大又紧这是年轻带给她的资本。
❤3🔥2
【验证报告模板】 #嫩妹车 #感觉车 #态度车 #良家感 #身材车 【花名】:#白虎宝宝 【车牌】:@BB520aaabb 【车费】:7P 12PP 【位置】:#东郊记忆 【硬件】:年龄:18 身高:168 体重:95 罩杯:c 【软件】:陪洗,三件套,浅吻,阴推,叫爸爸 【锐评】:如果说叫爸爸有加分的话,那她可以说是单项加满了,妹妹自聊,进门一个jk情趣装,颜值和视频一致,掩盖不住的是腰线和高高翘起的笋子乳头,进门热情自来熟,叫声亲昵,夹子音,体验不一样的感觉,脱了衣服十分亮眼,蜂腰翘臀笋子胸,肤白腿长还没毛,硬件确实极品,洗了澡到了床上,简单的三件套,这里可忽略不计,戴套阴推,白虎嫩穴自来水,天府异秉,蹭爽了自己插入,白虎小穴果然是有说法的,特别烫,起码高两度,卧槽,意志不坚定的绝对要被秒,女上插入一会,换传教士,这个白虎逼,这个时候威力才大,一夹一夹的,鸡儿差点给夹断,太猛了,天赋异禀,再加上妹…
【花名】正宗泰式抓龙筋(金穗) 【地址】钛公馆 【标签】#代聊 #态度好 #服务全 【车牌】 @jinsui666 【课费】399/699/899/1199 【评价】老师年龄28左右,身高160cm,50kg左右,颜值相似7.5分相似,凶器罩杯C有点坠,服务态度好、手法不错,颜值一般,抓龙筋体验还是不错,想尝鲜的兄弟可以打卡!
【验证报告模板】 #服务车 #少妇车 #态度车 #良家感 【花名】:#南门扶摇大奶 【车牌】:@fuyao189 【车费】:5P 8PP 【位置】:#二街 【硬件】:年龄:27左右 身高:160左右 体重:90左右 罩杯:b 【软件】:ab面莞式服务,指滑过水,舔凶舔蛋,dl,舌吻,69,菜单上的都有,口活舒适 【锐评】:烈日炎炎,进屋一股凉意,没有异味,淡淡的香味,干净整洁。老师声音温柔,进屋备好热水,准备好了进去冲洗,老师全程帮忙擦洗,手也温柔,皮肤不错的,没有疤痕之类的。帮忙擦完水之后,就趴好,开始了今天的课程。老师上课很仔细,舌头柔软,毒龙很舒服,口活也棒,AB两面弄得舒舒服服的,忍不住就开始了今天的战斗。一番翻云覆雨之后,缴了枪,冲洗干净,离开了老师。
❤1
【花名】可乐 【地址】理工大 【标签】#成华区 #自聊 #水多#态度好 #服务全#5p 【车牌】 @kelemm520 【课费】5p/7pp #5/7 【评价】老师28岁左右。165/100,风韵犹存,前凸后翘。良家感女友感很强。妹妹态度很好,服务到位,技术专业。毒龙很顶很舒服。爱爱反馈也好。推荐上牌
Showing the 7 most recent of 7 posts we hold for @tianfuairportvf. 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
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“天府机场验证榜” (@tianfuairportvf), 20,206 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/tianfuairportvf.
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.