📱#小饼干 🌐#武昌 💴【学费】#7p #14pp 🛫【联系】@xiaobinggan188 🥂【双向】@xiaobinggan_bot 📱【频道】 t.me/xiaobinggan166 ❤️【硬件】C+/165cm/48kg/21 🫦【标签】#武昌 #嫩妹 #7p #上门 报告车评查找机器人:@vlchattbot
👍2❤1🥴1

Channel
@wuhanhua1
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
102,688subscribers
+14,448 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1002350445516 |
|---|---|
| Type | Channel |
| Username | @wuhanhua1 |
| Description | 主群 @wuhanlou 聊天 @whlou6 工兵 @whlou7 报告 @whlou4 管理/上榜 @millyne1 学生 @wuhanhu0 评论 @wuhanlou8 东湖 @whlou 狼友 @wuhanlou3 武汉日报 @hijackcar 狼群 @wuhanlou3 报告集 @whlou9 本群为成人话题,禁止政治、未成年等违法话题 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 19 September 2026 |
| Measurements held | 32 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/wuhanhua1 |
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 90% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Sept 2026, 17:14 | 102,688 | +602 |
| 17 Sept 2026, 05:37 | 102,086 | +570 |
| 15 Sept 2026, 03:41 | 101,516 | +406 |
| 13 Sept 2026, 13:02 | 101,110 | +290 |
| 11 Sept 2026, 14:37 | 100,820 | +859 |
| 9 Sept 2026, 00:57 | 99,961 | +1,232 |
| 5 Sept 2026, 19:21 | 98,729 | +453 |
| 3 Sept 2026, 16:37 | 98,276 | +380 |
| 2 Sept 2026, 11:04 | 97,896 | +272 |
| 1 Sept 2026, 10:12 | 97,624 | +1,181 |
| 31 Aug 2026, 07:24 | 96,443 | +230 |
| 30 Aug 2026, 07:55 | 96,213 | +170 |
| 29 Aug 2026, 04:35 | 96,043 | +368 |
| 28 Aug 2026, 01:24 | 95,675 | +196 |
| 27 Aug 2026, 02:56 | 95,479 | +366 |
| 26 Aug 2026, 02:39 | 95,113 | +168 |
| 25 Aug 2026, 00:29 | 94,945 | +298 |
| 23 Aug 2026, 14:23 | 94,647 | +320 |
| 22 Aug 2026, 01:37 | 94,327 | +552 |
| 20 Aug 2026, 17:24 | 93,775 | first reading |
530 posts held, back to 7 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 104 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 262 of 279 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 25 September 2026 |
|---|---|
| Posts held | 530 (7 August 2026 – 25 September 2026) |
| Views total | 433,320 |
| Reactions total | 1,023 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 25 Sept 2026, 11:48 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 25 September 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 156 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.
1,972 reactions across 385 posts, in 52 distinct kinds. The most used accounts for 40.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 795 | 40.3% | |
| 👍 | 223 | 11.3% | |
| 🔥 | 149 | 7.56% | |
| 🥰 | 137 | 6.95% | |
| 🎉 | 88 | 4.46% | |
| 😍 | 72 | 3.65% | |
| 🤩 | 69 | 3.50% | |
| ❤🔥 | 66 | 3.35% | |
| 💯 | 57 | 2.89% | |
| 👏 | 24 | 1.22% | |
| 🍓 | 19 | 0.963% | |
| 😁 | 16 | 0.811% | |
| 🙏 | 15 | 0.761% | |
| 😘 | 13 | 0.659% | |
| 😨 | 13 | 0.659% | |
| ☃ | 11 | 0.558% | |
| 🍌 | 11 | 0.558% | |
| 🏆 | 11 | 0.558% | |
| 🤯 | 11 | 0.558% | |
| ⚡ | 10 | 0.507% | |
| 32 further kinds | 162 | 8.22% |
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 399 of the 530 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 1,972 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 530 most recent posts we hold, published 7 August 2026 to 25 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.
📱#小饼干 🌐#武昌 💴【学费】#7p #14pp 🛫【联系】@xiaobinggan188 🥂【双向】@xiaobinggan_bot 📱【频道】 t.me/xiaobinggan166 ❤️【硬件】C+/165cm/48kg/21 🫦【标签】#武昌 #嫩妹 #7p #上门 报告车评查找机器人:@vlchattbot
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📱#榨汁机 🌐#光谷 💴【学费】#6p #11pp 🛫【联系】@Wh2026888 🥂【双向】@Wh2026888bot 📱【频道】 t.me/zhazhimeng ❤️【硬件】C/165cm/50kg/25 🫦【标签】#甜妹 #潮喷 #颜射 #外出 #洪山 #光谷 #6p #11pp #服务 #莞式 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 💫学生群 @whbbb6 报告车评查找机器人:@vlchattbot
❤4👍1🙏1
📱#海蒂 🌐#武昌 💴【学费】#6p #10pp 🛫【联系】@xdhaidi 🥂【双向】@Haidizzzbot 📱【频道】 t.me/xudonghaidi ❤️【硬件】E/163cm/49kg/25 🫦【标签】#武昌 #6p #御姐 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 💫学生群 @whbbb6 报告车评查找机器人:@vlchattbot
🥰2😍2❤1🎉1
#汉口养生 #武汉会所 公寓模式 一房一老师 可挑可选 主打高颜值-服务一流-性价比高 诚信靠谱 无需定金 没有任何隐形消费 看中付款 武汉市区:汉口香港路 养生95/98会所 在岗的妹子多 🔥❤️🔥🔥🔥 火爆预约中 武汉不坑不雷 营业至凌晨3:00 黑丝 肉丝 巨乳 足交 技师号码:(上面都有)每日在岗很多 95 550 口一手一 70分钟 98 550 650 40 70分钟一次两次 服务内容: 阴推毛扫💥贴身艳舞💥与顾客互动💥情趣挑逗💥果冻漫游💥风油全身💥吸皮刮痧💥滚水漫游💥泰式揉💥激情臀腿💥冰火两重天💥暴力撕袜💥舌化全身💥玉女吞珠💥龙入仙境💥深喉销魂💥猴子摘桃💥等等😀 ✈️客服:@HK857333 QQ号:1134147712 微信号:HK857322 双向:https://t.me/wh857bot ☀️技师资料图: https://t.me/whvip123/458 (资料图仅供参考)
❤5🥱2
📱#小琪 🌐#洪山 💴【学费】#6p #11pp 🛫【联系】@hfkghfkytukty 🥂【双向】@xiaoqu2331_bot 📱【频道】 t.me/xiaoqu233 ❤️【硬件】B/160cm/46kg/18 🫦【标签】#洪山 #6p #新人 #嫩妹 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 报告车评查找机器人:@vlchattbot
🥰2❤1🎉1
【武汉黄鹤楼】中秋节特惠 2. 洪山百合 @annie3690 优惠一张,原价7p, 现价6p, 活动三天 3. 光谷瑶瑶 @siss951 优惠一张,原价5p, 现价4p, 活动三天 4. 洪山珍珍 @cc6688913 优惠一张,原价5p, 现价4p, 活动一周 5. 洪山小喵 @xytxyt520 优惠两张,原价6p, 现价4p, 活动三天 10. 木木老师 @Mumu0405 优惠一张,原价6p, 现价5p 11. 米娜老师 @MiNababy2 优惠一张,共十张券,原价6p, 发完为止 12. 珞涵 @hanhan3318 优惠一张,原价7p,现价6p 13. 光谷可可 @kekeit153 优惠一张,原价6p, 现价5p 16. 光谷萱萱 @xuanxuan072 优惠一张,原价5p, 现价4p 活动三天 18. 奈子 @naizi77889 优惠一张,原价9p, 现价8p, 活动三天 20. 洪山大白 @Heat…
❤3🤝2🥰1
📱#金莎 🌐#洪山 #南湖 💴【学费】#6p #10pp 🛫【联系】@jinshababy123 🥂【双向】@jinshabeen_bot 📱【频道】 t.me/shashawu235 ❤️【硬件】D/172cm/53kg 🫦【标签】#洪山 #御姐 #大胸 #6p #舌 #服务 #三通 #SM #上门 #包夜 #南湖 报告车评查找机器人:@vlchattbot
❤3🔥1
📱#欢欢 🌐#洪山 💴【学费】#6p #11pp 🛫【联系】@ildp1649 🥂【双向】 📱【频道】 t.me/vjj68856 ❤️【硬件】B/166cm/40kg/19 🫦【标签】#洪山 #6p #新人 #嫩妹 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 报告车评查找机器人:@vlchattbot
❤1🔥1
📱#雅琴 🌐#洪山 #光谷 💴【学费】#7p #14pp 🛫【联系】@yaqing1314lov 🥂【双向】@yaqing1314bot 📱【频道】 t.me/yaqing1314520 ❤️【硬件】C+/173cm/50kg/19 🫦【标签】#洪山 #光谷 #7p #14pp #嫩妹 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 报告车评查找机器人:@vlchattbot
❤2✍1
📱#暖暖 🌐#武昌 💴【学费】#6p #10pp 🛫【联系】@xiong5202 🥂【双向】@xiaoyiling_bot 📱【频道】 t.me/xioobot ❤️【硬件】C/165cm/47kg/28 🫦【标签】#武昌 #6p #10pp #剧情 #反差 #回归 #御姐 报告车评查找机器人:@vlchattbot
👏2🦄1
📱#小柒 🌐#洪山 #杨家湾 💴【学费】#6p #11pp 🛫【联系】@xiaoqie4 🥂【双向】@xiaoqie1_bot 📱【频道】 t.me/xiaoqie2 ❤️【硬件】D/172cm/62kg/19 🫦【标签】#洪山 #大胸 #新人 #SM #6p #舌 #BBW ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 💫学生群 @whbbb6 报告车评查找机器人:@vlchattbot
❤2🎉1🔥1😘1
📱#西施 🌐#武昌 💴【学费】#10p #16pp 🛫【联系】@h1467js 🥂【双向】@xsxgj_bot 📱【频道】 t.me/xsbb1314520 ❤️【硬件】A+/164cm/47kg/20 🫦【标签】#武昌 #嫩妹 #上门 #10p #高端 ❓上榜管理 @millyne1 🛍技术交流 @whlou6 🕯武汉东湖 @whlou 🎉工兵优惠 @whlou7 💫学生群 @whbbb6 报告车评查找机器人:@vlchattbot
❤3🔥2⚡1🥰1
Showing the 12 most recent of 530 posts we hold for @wuhanhua1. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 154 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. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
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.
@wuhanhua1 named 7 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 19 September 2026 — this entry's latest reading, not the date you are reading this.
“🅥 武汉老师榜 🅥” (@wuhanhua1), 102,688 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/wuhanhua1.
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.