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Channel

小霸王的频道

@HliNaa99999

On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Cite this entry

528subscribers

+285 since we began measuring on 31 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1004250143613
TypeChannel
Username@HliNaa99999
Created31 August 2026measured — dated from the channel’s first post
First recorded31 August 2026
Last confirmed live19 September 2026
Measurements held5
Confirmed unchanged1 time, most recently 19 September 2026
On Telegramt.me/HliNaa99999

Topic

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 21 September 2026 and assigned it the closest of 31 fixed categories, at 52% 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.

Growth

24356540431 August 2026 — 243 subscribers31 August 2026 — 243 subscribers1 September 2026 — 565 subscribers10 September 2026 — 496 subscribers19 September 2026 — 528 subscribers52831 August 202619 September 2026
5 measurements spanning 19 days, net +285. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 195–613 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
19 Sept 2026, 06:38528+32
10 Sept 2026, 21:16496-69
1 Sept 2026, 10:05565+322
31 Aug 2026, 16:15243no change
31 Aug 2026, 16:01243first reading

Engagement

6 posts held, back to 31 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.

ERR · 30 days
27.2%
avg views ÷ 528 subscribers
Avg views / post
144
4 posts measured
Reaction rate
0.696%
reactions ÷ views · ER floor
Posts in window
6
of 6 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 3 of 4 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 31 August 2026
Posts held6 (31 August 202631 August 2026)
Views total574
Reactions total3
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken31 Aug 2026, 16:15 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.

Reaction mix

3 reactions across 3 posts, in 2 distinct kinds. The most used accounts for 66.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
266.7%
👍133.3%

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 3 of the 6 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 3 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 6 most recent posts we hold, published 31 August 2026 to 31 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

31 Aug 2026, 12:22 UTC175 views1 reactionsread 31 August 2026
Forwarded from @GZNMMTL

【嫩妹报告】https://t.me/GZNMHEJI 【老师艺名】#小霸王 【联系方式】@Yaoyaoffy 【所在位置】花都 【验证留名】树 【验证时间】20260828 【修车费用】1500po 【颜值身材】美,嫩,顶,一字绝(除了🐻小,美人微瑕哈哈哈哈哈) 【服务内容】 课表 【服务态度】好!非常好! 【优点缺点】 我个人体验下来全是优点没有缺点 【推荐程度】 必吃榜,懂我意思吧 【体验细节】我必须得写个好评,谁不愿意跟美女一起玩啊,太美了,从一进门开始,惊艳的颜值,完美的妆容,苗条的身材,真是超值超值,老师又美又好吃,服务也好,配合也好,老师反馈也足,情绪价值给的也特别的满,休息时聊天也有意思,我真想不到什么形容词形容了,实在是太绝了,谁说谎话谁再也遇不到美女!出门腿都软了,美的让我恍惚,广州之行完美收官!如果我还有机会回广州一定会再回来!!! ♥️温馨提示: 以上仅代表个人体验,报告仅供参考! 👎🏿府上专员严查假报

1

31 Aug 2026, 11:49 UTC136 views1 reactionsread 31 August 2026
Forwarded from @GZSJGB

#神教出击报告https://t.me/GZSJQ) 出击留名:火箭批发马总 出击时间:7.28 老师艺名:#小霸王 联系方式:@HliNaa99 地理位置:#花都 上课支出:800P 颜值身材:人照9成A4腰 服务内容:全套 优点缺点:全是优点 推荐程度:满分 出击体验:高档小区,遥控上楼,到达楼层后,老师已经开门迎接,颜值超出预期,妆容精致嫩妹,A4腰翘臀,虽然是小馒头B胸,但老师的颜值身材摆上来,可以忽略了。服务态度超级好,陪洗后换皮开始课程,口技灵活无齿感,舌吻到饱,服务过程中老师一直抛媚眼勾魂,再配合体力超好的女上,差点就交代了,赶紧换姿势冷静一下,但看着老师欲求不满的样子,忍不住又疯狂输出,一泻千里。事后还会帮洗聊天,必须循环返吃。 报告仅供参考,请理性选择,切勿对号入座的 报告提交私聊👉🏻伍佰@GZWuBai_Bot

1

31 Aug 2026, 11:15 UTC143 viewsread 31 August 2026
Photo

🎰 花都小霸王奶茶红包抽奖 📮 抽奖条件: 🎫 加入-广州资源榜פּוּמְבֵּי『备胎』 🎫 加入-广州仙女宫中转站 🎫 加入-广州仙女宫 🎫 加入-小霸王的频道 🎫 加入-广州资源榜 🎫 加入-广深出师报告 🎫 加入-广州戒色宫『备胎』 🍀 每10分中奖几率加1倍 ⤷在群中输入「喜+1」 🎁 奖品内容: 18.8元奶茶红包 × 6 💡 活动说明: 1:兑奖联系 @HliNaa99 2:水群率越高中奖率越高喔 3:更多红包关注 @QMqun 4:半价出击需在仙女宫写报告否则拉黑抽奖 5:群内严禁胡言乱语禁用脚本刷信息 📅 开奖日期:(UTC+8) 2026年09月03日 19时13分54秒

31 Aug 2026, 09:09 UTC120 views1 reactionsread 31 August 2026
Photo

原来号炸啦,以后这个新号找我哟😋

👍1

Showing the 6 most recent of 6 posts we hold for @HliNaa99999. 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.

Forward network

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

Named by 3 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.

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 19 September 2026 — this entry's latest reading, not the date you are reading this.

“小霸王的频道” (@HliNaa99999), 528 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/HliNaa99999.

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.