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Channel
腿精圈🥂
@lilibaobeico
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
1,799subscribers
+1,718 since we began measuring on 28 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
Register entry
| Telegram ID | -1004345412604 |
|---|---|
| Type | Channel |
| Username | @lilibaobeico |
| Created | 20 August 2026 — measured — dated from the channel’s first post |
| First recorded | 28 August 2026 |
| Last confirmed live | 15 September 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 15 September 2026 |
| On Telegram | t.me/lilibaobeico |
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 16 September 2026 and assigned it the closest of 31 fixed categories, at 77% 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 |
|---|---|---|
| 15 Sept 2026, 19:55 | 1,799 | +185 |
| 12 Sept 2026, 02:00 | 1,614 | +237 |
| 7 Sept 2026, 06:56 | 1,377 | +1,296 |
| 28 Aug 2026, 19:46 | 81 | no change |
| 28 Aug 2026, 07:30 | 81 | first reading |
Engagement
14 posts held, back to 20 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.
- ERR · 30 days
- 26.5%
- avg views ÷ 1,799 subscribers
- Avg views / post
- 476
- 10 posts measured
- Reaction rate
- 0.24%
- reactions ÷ views · ER floor
- Posts in window
- 12
- of 14 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 2 of 10 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 27 August 2026 |
|---|---|
| Posts held | 14 (20 August 2026 – 27 August 2026) |
| Views total | 4,763 |
| Reactions total | 2 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 28 Aug 2026, 07:30 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
- 3s
- Average length
- 3s
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
3 reactions across 3 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 3 | 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 3 of the 14 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 3 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 14 most recent posts we hold, published 20 August 2026 to 27 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
杭州学生会:https://t.me/students_union 评分标准:6分﹣差 7分﹣及格 8分﹣良好 9分﹣优秀 10分﹣极品 我打10分 【出击老师】:#莉莉 @AiJbaobao 【出击留名】:ootd 【出击时间】:8月27日 【大概位置】:滨江 【修车水费】:800p (进门看到老师超级美) 【高端服务】:有的打 ✅ 没有的打 ❎ ① 舌吻 ✅ ② 六九 ③ 毒龙 【人照相似】:人照 9.5分,颜值好美 看起来就是正常 20+ 【身高身材】:真实身高 175 【胸器罩杯】:罩杯 D 脱衣服一看对比纤瘦的身材非常震撼 【湿度紧度】:常见户型 腿张开可以看到小豆豆 下面湿润 滑 老师会夹 让老师夹紧一点非常舒服 【课室环境】:有点小 【个人卫生】:无抽烟 【时间管理】:非常听话 服务不错 【服务点评】:像模特 身材高 匀称身材 但胸实在是大 屁股也大 属于比较耐操的类型 户型舒适 我必须二刷 她不一样…
❤1
性感嘛
黑丝好性感😋
吃完饭 就上课!💗
❤1
莉莉的朋友圈 pinned a photo
Photo, posted without a caption
杭州学生会:https://t.me/students_union 评分标准:6分﹣差 7分﹣及格 8分﹣良好 9分﹣优秀 10分﹣极品 【出击老师】:#莉莉 @AiJbaobao 【出击留名】:三无 产品 【出击时间】:8月25日 【大概位置】:绿都国金中心 【修车水费】:工兵p 【高端服务】:有的打 ✅ 没有的打 ❎ ① 舌吻 ✅ ② 六九 ✅ ③ 毒龙 ❎ 不知道我没提 【人照相似】:人照8.5分,没有照片那么诱惑,但是有邻家小妹那么耐看,颜值中上8.9分,穿搭气质9.9分(这个超级诱惑力) 【身高身材】:身高172以上,腿长超级长,又长又直,无纹身,🐻大小C,A4腰,小腹平摊 【胸器罩杯】:罩杯C,胸型我不太懂,乳头颜色粉色,皮肤手感光滑 【湿度紧度】:户型毛发浓密,紧度一般,我觉得舒服,因为我是软男,湿度完美,做爱反馈积极,叫声放得开,很爽 【课室环境】:公寓,停车方便,出入有门禁 【个人卫生】:无抽烟…
Video, posted without a caption
杭州学生会:https://t.me/students_union 评分标准:6分﹣差 7分﹣及格 8分﹣良好 9分﹣优秀 10分﹣极品 【出击老师】:#莉莉 @AiJbaobao 【出击留名】:bangbang 【出击时间】:8月24日 【大概位置】:滨江上门 【修车水费】:600p 工兵(老师第一次做不懂规则,先给了全价,说我是她第一个客人 震惊脸! 【高端服务】:有的打 ✅ 没有的打 ❎ ① 舌吻 ✅ ② 六九 ③ 毒龙 【人照相似】:人照 8 分,颜值普通 看起来就是正常 20+ 【身高身材】:真实身高 175 比我的假 175 高很多 修长休闲装 【胸器罩杯】:罩杯 C++ 但是实际感觉超级大 大半球型 感觉可能有 38 不太懂 反正脱衣服一看对比纤瘦的身材非常震撼 【湿度紧度】:常见户型 腿张开可以看到小豆豆 下面湿润 滑 老师会夹 让老师夹紧一点非常舒服 【课室环境】:滨江上门 【个人卫生】:无抽烟 【…
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莉莉的朋友圈 pinned Deleted message
Showing the 12 most recent of 14 posts we hold for @lilibaobeico. 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.
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 15 September 2026 — this entry's latest reading, not the date you are reading this.
“腿精圈🥂” (@lilibaobeico), 1,799 subscribers as measured 15 September 2026. Telegram Register, tgregister.com/channel/lilibaobeico.
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.