"这一杯 敬我们 敬相遇 也敬离分" ———来自超有才的群友帅jay https://t.me/teamjay3333
❤1👍1🥰1

Channel
@hhlsml
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
11,677subscribers
+148 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1003979814035 |
|---|---|
| Type | Channel |
| Username | @hhlsml |
| Created | Between 1 April 2026 and 4 August 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 3 September 2026 |
| Measurements held | 26 |
| Confirmed unchanged | 1 time, most recently 3 September 2026 |
| On Telegram | t.me/hhlsml |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 3 Sept 2026, 10:28 | 11,677 | +10 |
| 2 Sept 2026, 03:04 | 11,667 | +9 |
| 1 Sept 2026, 03:45 | 11,658 | +5 |
| 31 Aug 2026, 05:12 | 11,653 | +3 |
| 30 Aug 2026, 05:38 | 11,650 | +4 |
| 29 Aug 2026, 02:13 | 11,646 | +8 |
| 28 Aug 2026, 04:34 | 11,638 | +3 |
| 27 Aug 2026, 06:45 | 11,635 | +17 |
| 26 Aug 2026, 07:15 | 11,618 | -99 |
| 25 Aug 2026, 09:23 | 11,717 | +12 |
| 24 Aug 2026, 06:02 | 11,705 | +14 |
| 22 Aug 2026, 13:46 | 11,691 | +12 |
| 21 Aug 2026, 06:22 | 11,679 | +4 |
| 20 Aug 2026, 06:35 | 11,675 | +4 |
| 19 Aug 2026, 08:27 | 11,671 | +13 |
| 18 Aug 2026, 10:49 | 11,658 | +9 |
| 17 Aug 2026, 10:55 | 11,649 | +12 |
| 15 Aug 2026, 17:43 | 11,637 | +5 |
| 14 Aug 2026, 08:04 | 11,632 | +4 |
| 12 Aug 2026, 23:15 | 11,628 | first reading |
24 posts held, back to 4 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 46 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 5 of 22 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 | 24 (4 August 2026 – 2 September 2026) |
| Views total | 26,994 |
| Reactions total | 6 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 3 Sept 2026, 00:38 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 14 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.
7 reactions across 4 posts, in 3 distinct kinds. The most used accounts for 71.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 5 | 71.4% | |
| 👍 | 1 | 14.3% | |
| 🥰 | 1 | 14.3% |
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 6 of the 24 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 24 most recent posts we hold, published 4 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.
"这一杯 敬我们 敬相遇 也敬离分" ———来自超有才的群友帅jay https://t.me/teamjay3333
❤1👍1🥰1
上海徐汇区31 1号开预约d
👤 名字:#西柚柚 #上海 📇 资料:身高170 胸C 📌 标签: #22P #40PP #嫩妹 #白小纯 #1度白皮 #颜值 #清纯 #女友系 ✈️ 联系:@Xiyoyo_Too 👆 ✈️ 双向:@xiyoyo_too_bot 👆 200优惠券 数量5 积分兑换 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
沪杭目录 pinned a photo
👤 名字:#一口菲酱 #上海 📇 资料:身高163 胸D 📌 标签: #28P #50PP #嫩妹 #白小纯 #cosplay #萝莉控 #jk服 #2度白皮 ✈️ 联系:@feibb2006 👆 ✈️ 双向:@feijiang123bot 👆 900优惠券 数量1 游学团内竞拍 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
大头鸭福利社 主营类型均为个人兼职,因是个人兼职所以接受度普遍都比较高!例如sf,户外,车震,Sm,,3P,3通,主人布置的任务啥的!主要做过夜(12H)、下午茶(3h,5h有两种)包天(24H), 可以指定女孩子出门穿搭,本俱乐部的女孩子普遍文化程度较高,带出门有面子,说话有情商(带脑子),女友感拉满,情绪价值高! 女孩子除了约会还能做什么呢?例如商务洽谈,ktv喝酒,角色扮演...外地旅游的老板不知道哪里玩也可以当地陪! 女孩子不是那种一脱衣服到处都是纹身,🚬不离手的人的,也不是精神小妹! 多半都是知书达理,会穿搭,接受教育程度较高切听话,不事逼! 不建议单次!! 过夜价格7-8k之间 包天1.1-1.4之间 有会费(88)!会费可以抵扣尾款 联系方式 ✈️客服: @dty235 电报群:https://t.me/Q1850888 电报频道:https://t.me/zzzz18580 双向机器人: @dty778899bo
👤 名字:#辣辣 #杭州 #珠海 📇 资料:身高170 胸D 📌 标签: #60PP #70PPP #御姐 #身材 #胸控 #腿控 #大蜜 #蜜桃臀 #白虎 ✈️ 联系:@lala23061 👆 ✈️ 双向:@HH_TomBot 👆 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
👤 名字:#梦兮 #上海 📇 资料:身高172 胸C 📌 标签: #13P #25PP #御姐 #服务 #前列腺保健 #水床 #冰火两重天 #丝袜 #双飞 ✈️ 联系:@Mengxi_2002 👆 ✈️ 双向:@meng_xibot 👆 半价600优惠券 数量2 积分兑换 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
👤 名字:#梦雨 #上海 📇 资料:身高168 胸C 📌 标签: #13P #25PP #御姐 #服务 #前列腺保健 #水床 #冰火两重天 #丝袜 #双飞 ✈️ 联系:@Mengyu_2000 👆 ✈️ 双向:@meng_yu_bot 👆 半价600优惠券 数量2 积分兑换 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
❤1
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👤 名字:#甜甜 #上海 📇 资料:身高175 胸B 📌 标签: #16P #32PP #嫩妹 #颜值 #2度白皮 #高妹 ✈️ 联系:@tiantiandengn 👆 ✈️ 双向:@tiantiandengnbot 👆 ✨ 备注:优质群群友18P/36PP😆 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
👤 名字:#小凡 #上海 #杭州 📇 资料:身高166 胸B 📌 标签: #20P #40PP #颜值 #2度白皮 #身材 #纯欲 #女友系 ✈️ 联系:@xiaofanhz 👆 ✈️ 双向:@xiaofanmm_bot 👆 200优惠券 数量3 积分兑换 ✨ 游学团 👆 ✨ 优质群 👆 (审核制)
Showing the 12 most recent of 24 posts we hold for @hhlsml. 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.
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.
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 3 September 2026 — this entry's latest reading, not the date you are reading this.
“沪杭目录” (@hhlsml), 11,677 subscribers as measured 3 September 2026. Telegram Register, tgregister.com/channel/hhlsml.
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.