#番禺 #市桥 #97场 #海悦湾 #磨棒 番禺市桥97场,推荐18【温柔型】10【颜值高】11:【萝莉型】36【模特型】06【学生妹】02【御姐型】 消费: A牌———858预约优惠后799 S牌———958预约优惠后899 👉广佛深会所报告群:@GZmingge003 👉广州交流群:@mingge9529 👉资源联系:@dabi520
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Channel
@fage7899
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry
29,562subscribers
-4,723 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001789178294 |
|---|---|
| Type | Channel |
| Username | @fage7899 |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/fage7899 |
Other / unclassifiable — 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 79% 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 |
|---|---|---|
| 18 Sept 2026, 18:36 | 29,562 | -140 |
| 16 Sept 2026, 11:58 | 29,702 | -202 |
| 14 Sept 2026, 17:41 | 29,904 | -116 |
| 13 Sept 2026, 06:40 | 30,020 | -274 |
| 11 Sept 2026, 11:18 | 30,294 | -124 |
| 8 Sept 2026, 15:42 | 30,418 | -120 |
| 5 Sept 2026, 11:02 | 30,538 | -81 |
| 3 Sept 2026, 14:20 | 30,619 | -15 |
| 2 Sept 2026, 11:03 | 30,634 | -167 |
| 1 Sept 2026, 10:24 | 30,801 | -88 |
| 31 Aug 2026, 13:19 | 30,889 | -100 |
| 30 Aug 2026, 12:57 | 30,989 | -131 |
| 29 Aug 2026, 14:44 | 31,120 | -139 |
| 28 Aug 2026, 15:41 | 31,259 | -21 |
| 27 Aug 2026, 18:14 | 31,280 | -10 |
| 26 Aug 2026, 14:53 | 31,290 | -44 |
| 25 Aug 2026, 13:06 | 31,334 | -160 |
| 24 Aug 2026, 13:57 | 31,494 | -24 |
| 22 Aug 2026, 22:18 | 31,518 | -46 |
| 21 Aug 2026, 11:18 | 31,564 | first reading |
13 posts held, back to 17 July 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 41 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.
| Window | Rolling 30 days · latest post in window 25 August 2026 |
|---|---|
| Posts held | 13 (17 July 2026 – 25 August 2026) |
| Views total | 167 |
| Reactions total | 4 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 26 Aug 2026, 06:02 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.
15 reactions across 7 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 15 | 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 9 of the 13 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 15 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 13 most recent posts we hold, published 17 July 2026 to 25 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.
#番禺 #市桥 #97场 #海悦湾 #磨棒 番禺市桥97场,推荐18【温柔型】10【颜值高】11:【萝莉型】36【模特型】06【学生妹】02【御姐型】 消费: A牌———858预约优惠后799 S牌———958预约优惠后899 👉广佛深会所报告群:@GZmingge003 👉广州交流群:@mingge9529 👉资源联系:@dabi520
❤4
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#白云 #97场 #磨棒 白云97场 超A--803预约优惠后---773 A牌--683预约优惠后---653 B牌--599预约优惠后---569 👉广佛深会所报告群:@GZmingge003 👉广州交流群:@mingge9529 👉资源联系:@dabi520
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Showing the 12 most recent of 13 posts we hold for @fage7899. 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.
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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“广州会所” (@fage7899), 29,562 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/fage7899.
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.