✏️艺名:#呦呦 📵📵 📱位置:#拱墅区 👠身材:160 47kg 🧸B 🎀价格:7P 13Pp 🤣服务:陪浴,🈚️🍑口🐍吻调情♥丝袜 制服(超25分钟手打) 📱电报:@youiiiyou0 📱频道: t.me/youiiiyou00 🔖标签: #千元内 #杭州 #拱墅 #舌吻 工兵福利:半价P工兵2名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤1

Channel
@hangzhou_888
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Handles named that no longer answer · Cite this entry
58,711subscribers
+10,535 since we began measuring on 9 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001918623688 |
|---|---|
| Type | Channel |
| Username | @hangzhou_888 |
| Description | 杭州最真实最全面楼凤信息资源大全 @hangzhou_buzhang 唯一管理账号(如遇私信不了请私信传话筒 @hzbuzhang_bot )其他主动私聊的都是冒充的骗子,真实楼凤自聊资源参考,主群 t.me/hangzhou_777 Hello @Telegram My channel has not violated the rules and policy of using Telegram. Please pay attention. @rove |
| Created | Between 1 April 2023 and 31 October 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 7 November 2026 |
| Measurements held | 59 |
| Confirmed unchanged | 1 time, most recently 7 November 2026 |
| On Telegram | t.me/hangzhou_888 |
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 9 September 2026 and assigned it the closest of 31 fixed categories, at 53% 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 the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Nov 2026, 05:31 | 58,711 | +112 |
| 6 Nov 2026, 07:57 | 58,599 | +120 |
| 5 Nov 2026, 07:16 | 58,479 | +163 |
| 4 Nov 2026, 05:27 | 58,316 | +113 |
| 3 Nov 2026, 06:11 | 58,203 | +152 |
| 2 Nov 2026, 06:38 | 58,051 | +111 |
| 1 Nov 2026, 07:35 | 57,940 | +129 |
| 31 Oct 2026, 11:05 | 57,811 | +154 |
| 30 Oct 2026, 07:48 | 57,657 | +137 |
| 29 Oct 2026, 09:51 | 57,520 | +145 |
| 28 Oct 2026, 09:35 | 57,375 | +154 |
| 27 Oct 2026, 07:16 | 57,221 | +116 |
| 26 Oct 2026, 05:47 | 57,105 | +125 |
| 25 Oct 2026, 02:20 | 56,980 | +118 |
| 23 Oct 2026, 23:28 | 56,862 | +147 |
| 22 Oct 2026, 22:39 | 56,715 | +189 |
| 21 Oct 2026, 21:04 | 56,526 | +116 |
| 20 Oct 2026, 18:46 | 56,410 | +121 |
| 19 Oct 2026, 18:21 | 56,289 | +103 |
| 18 Oct 2026, 20:36 | 56,186 | first reading |
275 posts held, back to 6 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 189 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 64 of 102 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 November 2026 |
|---|---|
| Posts held | 275 (6 August 2026 – 7 November 2026) |
| Views total | 106,457 |
| Reactions total | 84 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Nov 2026, 04:25 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.
Lifetime counters from Telegram’s own channel header, read 8 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Measured directly from 200 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.
289 reactions across 150 posts, in 6 distinct kinds. The most used accounts for 88.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 255 | 88.2% | |
| 👍 | 18 | 6.23% | |
| 🎉 | 6 | 2.08% | |
| 🔥 | 5 | 1.73% | |
| 👏 | 3 | 1.04% | |
| 👀 | 2 | 0.692% |
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 178 of the 275 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 289 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 275 most recent posts we hold, published 6 August 2026 to 7 November 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.
Telegram’s own ‘edited’ marker: 4 of 102 posts held from the last 30 days (3.92%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
✏️艺名:#呦呦 📵📵 📱位置:#拱墅区 👠身材:160 47kg 🧸B 🎀价格:7P 13Pp 🤣服务:陪浴,🈚️🍑口🐍吻调情♥丝袜 制服(超25分钟手打) 📱电报:@youiiiyou0 📱频道: t.me/youiiiyou00 🔖标签: #千元内 #杭州 #拱墅 #舌吻 工兵福利:半价P工兵2名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤1
✏️艺名:#瑞瑞 📵📵 📱位置:#拱墅区 👠身材:160 44kg 🧸B 🎀价格:9P 16Pp 🤣服务:陪浴,🈚️🍑口,调情,丝袜制服 📱电报:@AAArr521 📱频道: t.me/AAArr070717 🔖标签: #千元内 #杭州 #拱墅 #三件套 工兵福利:半价P工兵2名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
✏️艺名:#豆豆 📵📵 📱位置:#上城区 👠身材:162 42kg 🧸B 🎀价格:8P(时间允许内可以pp) 🤣服务:舌吻69毒龙 ab面 口爆胸推足交爱爱 喜欢当女S:调教 巴掌口水圣水舔脚 坐脸等可剧情(自带剧本提前发给我) 📱电报:@dou01 📱频道: t.me/dou069 🔖标签: #千元内 #杭州 #上城 #舌吻 #毒龙 #SM 工兵福利:半价P工兵2名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤1
🎰 上城诗雯 @sw1234568 宠粉福利🎟️ 📮 抽奖条件: 🎫 加入-杭州御女阁(免费上榜) 🎫 加入-杭州公开认证榜(御女阁) 🎫 加入-诗雯的朋友圈❤️ 🎁 奖品内容: 半价P优惠卷🎟️ × 1 18.8现金红包🧧 × 2 200优惠卷🎟️ × 3 📅 开奖日期:(UTC+8) 2026年11月09日 16时00分00秒
✏️艺名:#菜菜 📵📵 📱位置:#上城区 👠身材:162 42kg 🧸B 🎀价格:7p 14pp 🤣服务:鸳鸯浴 无套吹箫 舌吻(口臭不行)调情 丝袜制服 姿势配合 📱电报:@caicai06o 📱频道: t.me/caicai06oo 🔖标签: #千元内 #舌吻 #上城 #鸳鸯浴 工兵福利:半价P工兵2名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤2
🎰 拱墅小贝 @xiaobei888777 宠粉福利🎟️ 📮 抽奖条件: 🎫 加入-杭州御女阁(免费上榜) 🎫 加入-杭州公开认证榜(御女阁) 🎫 加入-小贝的小窝 🎁 奖品内容: 半价P优惠卷🎟️ × 1 7折P优惠卷🎟️ × 2 📅 开奖日期:(UTC+8) 2026年11月08日 16时00分00秒
✏️艺名:#眠眠 📵📵 📱位置:#西湖区 👠身材:165 63kg 🧸C+ 🎀价格:5p 9pp 🤣服务:鸳鸯浴 水中萧 波推按摩蚂蚁上树滑指 舔蛋蛋调情 无套口 蛇吻69高跟制服诱惑 事后 萧舔咪咪 水疗会所刚来到杭州服务系 📱电报:@mian898 📱频道: t.me/hdubh8 🔖标签: #千元内 #舌吻 #西湖 #BBW 工兵福利:半价P工兵1名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤1
Telegram必备的搜索引擎,极搜JISOU帮你精准找到,想要的群组、频道、视频、音乐 👉 t.me/jisou2?start=a_6333257
✏️艺名:#云夕 📵📵 📱位置:#上城区 👠身材:167CM 49KG 🧸C+ 🎀价格:7P 14Pp 🤣服务:陪浴、胸推、水中萧、深喉、🐍吻、69、制服丝袜、角色扮演、360度旋转口、爱爱◎拍小视频(不露脸)(2吻、有异味、口臭、不🐍) 📱电报: @Yunxibabay 📱频道: t.me/Yunxibaby888 🔖标签: #千元内 #杭州 #上城 #制服 #舌吻 #69 #深喉 工兵福利:半价P工兵2名,群内竞拍,24h提交出击报告,群内输入“报告,模版”获取御女阁出击报告模版!
✏️艺名:#甜甜 📵📵 📱位置:#上城区 👠身材:163CM 52KG 🧸B 🎀价格:7P 12Pp 🤣服务:制服诱惑 丝袜 无套口 指滑 温柔态度好 可配合各种姿势 📱电报: @tian9691 📱频道: t.me/tiantian66691 🔖标签: #千元内 #杭州 #上城 #制服 #嫩妹 工兵福利:半价P工兵2名,群内竞拍,24h提交出击报告,群内输入“报告,模版”获取御女阁出击报告模版!
✏️艺名:#沫沫 📵📵 📱位置:#临平区 👠身材:160CM 50KG 🧸D 🎀价格:6P 11Pp 🤣服务:鸳鸯浴 水中萧 过水 蚂蚁上树 胸推 舌吻 69 回马枪 毒龙 高跟丝袜情趣内衣 调情 📱电报: @momo13147 📱频道: t.me/fdyfh52 🔖标签: #千元内 #杭州 #临平 #69 #舌吻 #鸳鸯浴 工兵福利:半价P工兵2名,群内竞拍,24h提交出击报告,群内输入“报告,模版”获取御女阁出击报告模版!
❤1
✏️艺名:#兔兔 📵📵 📱位置:#上城区 👠身材:168 45kg 🧸34C 🎀价格:7P 13Pp 🤣服务:特色淋浴 舌吻 口交 口爆 深喉 指滑漫游 胸推69丝袜 高跟 变装制服(服务暂时可能有些生疏,我会慢慢努力学习的 📱电报: @cc1789789 📱频道: t.me/tuturis789789 🔖标签: #千元内 #杭州 #上城 #舌吻 #口爆 👑报告: 工兵报告 工兵报告 工兵报告 工兵报告 工兵福利:半价P工兵1名,群内竞拍,24h内提交报告;群内输入“报告,模版”获取御女阁出击报告模版!
❤2
Showing the 12 most recent of 275 posts we hold for @hangzhou_888. 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.
@hangzhou_888 edited 7 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
We recorded 13 posts on @hangzhou_888 as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
Named by 4 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.
@hangzhou_888 named 13 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
References a handle that is not a live channel — we have no record it ever was one.
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 7 November 2026 — this entry's latest reading, not the date you are reading this.
“杭州公开认证榜(御女阁)” (@hangzhou_888), 58,711 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/hangzhou_888.
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.