https://t.me/+NLP_JRkgYboyY2M1

Channel
红馆全国{上·门}服务
@hgsmfw
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
7,796subscribers
+1,537 since we began measuring on 14 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
Register entry
| Telegram ID | -1003858153689 |
|---|---|
| Type | Channel |
| Username | @hgsmfw |
| Created | Between 1 February 2026 and 18 June 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 14 August 2026 |
| Last confirmed live | 31 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 31 August 2026 |
| On Telegram | t.me/hgsmfw |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 31 Aug 2026, 00:15 | 7,796 | +305 |
| 27 Aug 2026, 17:06 | 7,491 | +1,067 |
| 24 Aug 2026, 22:34 | 6,424 | +18 |
| 21 Aug 2026, 14:39 | 6,406 | +116 |
| 18 Aug 2026, 09:12 | 6,290 | -5 |
| 15 Aug 2026, 18:07 | 6,295 | +36 |
| 14 Aug 2026, 07:48 | 6,259 | first reading |
Engagement
9 posts held, back to 18 June 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 2 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 73.2%
- avg views ÷ 7,796 subscribers
- Avg views / post
- 5,710
- 5 posts measured
- Reaction rate
- 0.082%
- reactions ÷ views · ER floor
- Posts in window
- 6
- of 9 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 4 of 5 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 | 9 (18 June 2026 – 27 August 2026) |
| Views total | 28,550 |
| Reactions total | 22 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 28 Aug 2026, 22:30 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
- 26s
- Average length
- 9s
Measured directly from 3 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.
Reaction mix
79 reactions across 7 posts, in 5 distinct kinds. The most used accounts for 92.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 73 | 92.4% | |
| 👎 | 3 | 3.80% | |
| ⚡ | 1 | 1.27% | |
| 👍 | 1 | 1.27% | |
| 🤔 | 1 | 1.27% |
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 7 of the 9 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 79 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 9 most recent posts we hold, published 18 June 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
冲喜宝宝 长沙 09/162/90 甜甜🌸客服:@ttcosw 🤖限制私聊 :@ttcoswbot
❤9
红馆全国{上·门}服务 pinned «欢迎光临甜甜🌸全国精品各类资源, 模特,空姐,学生,白领,网红,洋妞,演员明星,萝莉,cos,jk,SM,调教,护士,教师,瑜伽健身教练,少妇,御姐等。 一、服务安排流程 报所在城市→等待客服查询可接单妹子和报价价格→入会188人民币发图→确定约妹子时间→面付→开始服务 1.女孩多数都有工作室,环境安全卫生绝对👌,需要上门服务,确定时间,发地址房号。 【为什么要先入会员】 特此阐述为何要收取会费? 网上人群众多 参差不齐 鱼龙混杂 1,会费是检测客户消费实力的唯一标准…»
门槛188定金安排 唯一客服咨询安排 @ttww777 私聊限制 @ttcoswbot 外围各地方导航频道 t.me/tiantianwwcos2 包养地区导航 t.me/tutubaobei06 全国处女导航频道 t.me/tiantianwwcn
❤2
上海晏熙雨5 日本 #长宁 硬五🦜 真实🇯🇵 可看护照 日本妹在深圳,短期签证停留 身長:1.65 胸囲:真实C+翘翘臀+白🐯 体重:48kg 年齢:24歳 専攻:医師在就读/インフルエンサー 語言:日、英、中 纯日式服务 好评神器 全程日语沟通 无整容颜、温柔甜美系、高素质 体验不同的日本本土サービス 情绪价值拉满 🈲 无🍑/字母/🧊K /小🏨 勿扰⚠️
❤5
冲喜宝宝 北京 09/160/82 甜甜🌸客服:@ttcosw 🤖限制私聊 :@ttcoswbot
❤6
🌹甜甜🌸外wei欢迎各位老板 🏅十年老字号 🔥小圈800/次,夜2000, ▶️小圈自己开房(或去工作室),见面满意付款 🔥中圈2400-2800两次《夜6000+》 🔥大圈 3000/次 《夜8000》 5000/次 《夜15000》 10000/次 《夜30000》 👇服务流程: ➡️入会选人《会费188,消费抵消200》 ➡️安排妹子与您见面 ➡️当面确认对该妹子满意才付款 ➡️可接受Usdt支付宝微信消费现金 联系客服安排:https://t.me/ttcosw 私聊限制:https://t.me/ttcoswbot 安排记录:https://t.me/tiantianfk 🫵全国选妃导航:https://t.me/tiantianwaiwei0 ‼️消费大圈的顾客,10次送一次,永久有效‼️ 认准甜甜🌸客服:@ttww7
❤7👎3⚡1🤔1
甜甜🌸国际 高端品质 专属尊享 极致体验 🤗😳🤗🤗🤗 双向限制 @ttcoswbot 咨询客服 @ttww777 @ttcosw 甜甜🌸总群: @tiantianwaiwei0
❤9
欢迎光临甜甜🌸全国精品各类资源, 模特,空姐,学生,白领,网红,洋妞,演员明星,萝莉,cos,jk,SM,调教,护士,教师,瑜伽健身教练,少妇,御姐等。 一、服务安排流程 报所在城市→等待客服查询可接单妹子和报价价格→入会188人民币发图→确定约妹子时间→面付→开始服务 1.女孩多数都有工作室,环境安全卫生绝对👌,需要上门服务,确定时间,发地址房号。 【为什么要先入会员】 特此阐述为何要收取会费? 网上人群众多 参差不齐 鱼龙混杂 1,会费是检测客户消费实力的唯一标准 2,会费是检测客户诚信度的一道分水岭 3,为了避免客户动动嘴我们就要忙活半天 您说不要就不要了 可我们的努力全白费了 二、服务价格和服务项目 🔥中圈2400-2800两次《夜6000+》 🔥大圈 3000/次 《夜8000》 5000/次 《夜15000》 100…
❤35👍1
Showing the 9 most recent of 9 posts we hold for @hgsmfw. 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.
Citation-graph rank
Citation-graph rank — 827,197 of 1,629,362 entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
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.
Mentions
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 31 August 2026 — this entry's latest reading, not the date you are reading this.
“红馆全国{上·门}服务” (@hgsmfw), 7,796 subscribers as measured 31 August 2026. Telegram Register, tgregister.com/channel/hgsmfw.
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.