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Channel

橙橙的基地

@hbhbn45678

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

1,068subscribers

-7 since we began measuring on 26 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1002994436848
TypeChannel
Username@hbhbn45678
CreatedBetween 1 August 2025 and 31 October 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded26 August 2026
Last confirmed live6 September 2026
Measurements held4
Confirmed unchanged1 time, most recently 6 September 2026
On Telegramt.me/hbhbn45678

Growth

1,0681,0751,071.526 August 2026 — 1,075 subscribers26 August 2026 — 1,075 subscribers30 August 2026 — 1,073 subscribers6 September 2026 — 1,068 subscribers26 August 20266 September 2026
4 measurements spanning 11 days, net -7. 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 1,067–1,076 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
6 Sept 2026, 17:591,068-5
30 Aug 2026, 05:441,073-2
26 Aug 2026, 22:571,075no change
26 Aug 2026, 16:001,075first reading

Engagement

20 posts held, back to 10 January 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
0.437%
avg views ÷ 1,068 subscribers
Avg views / post
4.7
3 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
3
of 20 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 26 August 2026
Posts held20 (10 January 202626 August 2026)
Views total14
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken26 Aug 2026, 16:00 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

1 reaction across 1 post, in 1 kind.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1100.0%

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 1 of the 20 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 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 10 January 2026 to 26 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

17 Aug 2026, 09:06 UTC10 viewsread 26 August 2026
Forwarded from @SHDD333

💃老师:#橙橙 🤖链接: https://t.me/shanghaidadao/11695 💒位置:#闵行 ------------------ 💌出击报告:周天cj过了,念念不忘,一直想和橙橙再续前缘,忍不了那么久,就又来找她了,真是太吃她的颜了,实在是太美。第二次来多少有点熟络了,聊了聊帮忙洗完澡后开始正题,重点讲一下,口活真的是我见过的天花板,太有感觉,而且边做边🐍,想🐍多久都可以,正面耳朵贴上去ls还会把舌头伸进去一直舔,配合度满分,真的很有做爱的感觉,这次终于对着镜子狠狠后入,看着ls的骚样,在橙橙小穴又吸又夹的攻势下射了出来,太满足了,做完时间差不多刚刚好,大脑整个是一片空白,感觉腿都要软掉了,上次她就跟我讲过年不回家,我赶过年前一定要多来几次,md,感觉要沦陷了

2 Aug 2026, 13:28 UTC16 viewsread 26 August 2026
Forwarded from @SHDD333

💃老师:#橙橙 🤖链接: https://t.me/shanghaidadao/11695 💒位置: #闵行 ------------------ 💌出击报告:第一眼看到看到就喜欢上了 照片跟本人也一样比照片更加让我心动,ls说话温柔可爱,直接给了水费老师贴心的帮我洗了,ls的🐻又大又软很舒服

27 Jul 2026, 15:31 UTC18 viewsread 26 August 2026
Forwarded from @SHDD333

💃老师:#橙橙 🤖链接: https://t.me/shanghaidadao/10058 💒位置: #闵行 ------------------ 💌出击报告:老师一靠近就用气音调情,声音又软又直接,说的话很快就把气氛拉起来。她先低头专心舔蛋,舌头又软又细致,把两边都吸得发胀,还时不时抬眼看我反应。胸推是接着做的。软肉把整根完全夹住,上下套弄的时候还会用力挤压,顶端一次次从乳肉里露出来又被吞回去。全程调情都没停,边做边用骚话刺激,又色又密。舔蛋的耐心、胸推的扎实、再加上不停的调情,短短一场就很有感觉。结束时她还笑着问我舒不舒服。

17 Jul 2026, 10:56 UTC27 viewsread 26 August 2026
Forwarded from @SHDD333

💃老师:#橙橙 🤖链接: https://t.me/shanghaidadao/10058 💒位置:#闵行 ------------------ 💌出击报告:声音软软糯糯地说:“哥哥今晚想让橙橙用嘴巴和骚逼好好伺候你吗?”直接把我撩得硬邦邦。前戏她跪在我面前,先用小嘴给我口爆预热。吸得又深又紧,舌头灵活地缠着龟头,深喉的时候直接吞到根部,喉咙收缩按摩,口水拉丝流到下巴,看得特别骚。她边吸边抬头用媚眼看我,调情功力满分。我忍不住把她抱上床,她主动跨坐上来。会吸会夹名不虚传,小穴又紧又热,一坐下去就死死夹住我的鸡巴,里面一缩一缩地用力吸吮。她自己上下套弄,腰扭得又骚又浪,一边骑还一边骚叫:“哥哥……橙橙吸得你爽吗?想射哪里都可以哦~”我换成后入猛干,她屁股翘得老高,下面依然用力夹吸我,爽得我腰都发酸。最后我把她按跪在床上,抓住头发猛操她的小嘴,口爆收尾。她喉咙放松得很好,任我深深操干,最后我把浓精全部射进她嘴里。橙橙全部吞下去,还伸

7 Jul 2026, 15:34 UTC31 viewsread 26 August 2026
Forwarded from @SHDD333

💃老师:#橙橙 🤖链接: https://t.me/shanghaidadao/10058 💒位置: #闵行 ------------------ 💌出击报告:橙橙整体给人一种清纯又带骚劲的感觉。主动贴上来调情,手指轻轻划过我身体,耳边吹气撒娇,把前戏氛围搞得火热。很快她跪在我面前开始服务,先是舔蛋,舌头又软又热,从蛋蛋下面仔细舔弄,吸吮得特别舒服,边舔边用眼睛抬头看我,骚浪得要命。胸推玩得也很顶,C杯左右(身材比例不错)夹得又软又紧,涂上润滑后上下滑动顺滑无比,奶头故意蹭龟头,视觉和触感双重刺激,爽得我腰都软了。她一边胸推一边调情,声音又甜又媚,技术熟练又投入。最后口爆环节,她深喉到底,吸吮力度越来越强,舌头灵活绕着马眼打转,直到我忍不住直接射在她嘴里。她没有躲,全部吞下还舔干净,服务态度满分。整个过程她全程主动调情,配合度高,没有敷衍,玩得非常舒服过瘾!

Showing the 12 most recent of 20 posts we hold for @hbhbn45678. 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 1 registered channel — 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.

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

“橙橙的基地” (@hbhbn45678), 1,068 subscribers as measured 6 September 2026. Telegram Register, tgregister.com/channel/hbhbn45678.

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.