#小鱼儿 频道链接:https://t.me/hahbj8/64074
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Channel
@qicherj
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Cite this entry
13,129subscribers
+1,473 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002195932528 |
|---|---|
| Type | Channel |
| Username | @qicherj |
| Created | Between 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 17 September 2026 |
| Measurements held | 33 |
| Confirmed unchanged | 1 time, most recently 17 September 2026 |
| On Telegram | t.me/qicherj |
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 11 September 2026 and assigned it the closest of 31 fixed categories, at 72% 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 |
|---|---|---|
| 17 Sept 2026, 11:37 | 13,129 | +71 |
| 15 Sept 2026, 09:36 | 13,058 | +55 |
| 13 Sept 2026, 21:21 | 13,003 | +70 |
| 12 Sept 2026, 02:56 | 12,933 | +108 |
| 9 Sept 2026, 19:14 | 12,825 | +134 |
| 6 Sept 2026, 13:55 | 12,691 | +70 |
| 4 Sept 2026, 06:36 | 12,621 | +42 |
| 2 Sept 2026, 17:45 | 12,579 | +64 |
| 1 Sept 2026, 15:46 | 12,515 | +26 |
| 31 Aug 2026, 17:24 | 12,489 | +28 |
| 30 Aug 2026, 17:48 | 12,461 | +36 |
| 29 Aug 2026, 20:47 | 12,425 | +37 |
| 28 Aug 2026, 18:28 | 12,388 | +39 |
| 27 Aug 2026, 19:03 | 12,349 | +36 |
| 26 Aug 2026, 22:03 | 12,313 | +28 |
| 25 Aug 2026, 23:46 | 12,285 | +15 |
| 25 Aug 2026, 02:45 | 12,270 | +52 |
| 23 Aug 2026, 16:56 | 12,218 | +39 |
| 22 Aug 2026, 05:59 | 12,179 | +26 |
| 20 Aug 2026, 23:37 | 12,153 | first reading |
131 posts held, back to 31 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 50 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 75 of 81 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 2 September 2026 |
|---|---|
| Posts held | 131 (31 July 2026 – 2 September 2026) |
| Views total | 261,500 |
| Reactions total | 980 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 2 Sept 2026, 21:27 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.
Measured directly from 1 video 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.
1,392 reactions across 116 posts, in 24 distinct kinds. The most used accounts for 23.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🤡 | 332 | 23.9% | |
| ❤ | 259 | 18.6% | |
| 👎 | 254 | 18.2% | |
| 👍 | 155 | 11.1% | |
| 💩 | 154 | 11.1% | |
| 🤮 | 71 | 5.10% | |
| 👀 | 39 | 2.80% | |
| 🤣 | 33 | 2.37% | |
| 🔥 | 23 | 1.65% | |
| 😁 | 16 | 1.15% | |
| 👏 | 12 | 0.862% | |
| 😭 | 8 | 0.575% | |
| 🌚 | 6 | 0.431% | |
| 🤪 | 6 | 0.431% | |
| 😢 | 5 | 0.359% | |
| 🙊 | 4 | 0.287% | |
| 🤯 | 4 | 0.287% | |
| 😨 | 3 | 0.216% | |
| 💯 | 2 | 0.144% | |
| 🖕 | 2 | 0.144% | |
| 4 further kinds | 4 | 0.287% |
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 124 of the 131 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 1,392 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 131 most recent posts we hold, published 31 July 2026 to 2 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.
#小鱼儿 频道链接:https://t.me/hahbj8/64074
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大佬们有没有出击过CC的?怎么样?
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这个哥哥本来就迟到了20分钟 ,进门我也给他拿拖鞋拿浴巾拿水了 他的各种要求我都做了 凭良心说话 这样也给差评 我真不知道该咋做啦 水也是我自己拿给他的怕他路上渴 不爱请别伤害😂。为了安全清空记录是一般哥哥到了或者洗澡的时候我就清空了 没有特意针对他😂
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#夏沫 人照高度一致,妆后算漂亮,胸大软。 陪浴仔细,AB面服务,床感都不错。 一直到出水 给人感受都很好。 可惜后面收尾不太行,射完后态度有点冷,我自己洗澡,擦干,穿衣服,钱放桌上,说声走了,一直到开门,老师都窝在沙发。最后象征的起身送了下。 可能排课满吧,对了我还罚站了10分钟。
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#lucky 频道链接:https://t.me/hahbj8/58273
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💃老师:#小宫 🤖链接: https://t.me/bjwyls/25582?single 🏠位置: #朝阳 #双井 ------------------ 🖋课后作业:你好,反应一下小宫货不对板,去了之后人照差异非常大,电报上问他说自己是经纪人,怀疑是中介假冒
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成寿寺的小曼有人去过吗?怎么样?@chengshousxiaom
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今天晚上约了蔡蔡@xybbzka999的外出,之前一直关注她频道,觉得妹子太喜人了。今天实在没忍住,怀着巨大的期待等了好久。妹子出发前收了我200口令红包,我也再次确认是不是本人,对面斩钉截铁说是本人。终于等到人到了,我出去接人,一看这什么鬼,差的也太远了。这时候其实我也没觉得有啥,人照不一致也时有发生,虽然我不满意,但想着这么远来都来了。紧接着妹子一开口我震惊了,说自己今天第一天上班,看我是不是满意,不满意可以让那边换人?我震惊了。难道我一直跟代聊沟通,我非常生气,让妹子回去,妹子不走,要我把剩下的路费报销,因为在马路上,我不想人来人往被人围观,想着息事宁人,赶紧转了剩下的一百多。事后我越想越气,耽误这么多时间不说,我本来满怀期待,然后白白花了三百多,被人当🐒耍了。真是不得不投诉,千万不要让更多人受骗了。以上所说,句句属实,我以后半辈子的幸福担保,如果有半句假话,让我不得好死
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#蔡蔡 #百子湾 https://t.me/PMXKT/29089
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想问下有没有认识这个的 之前叫鹿鹿在亚运村开课 求联系方式
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💃老师:#芝芝VS霉霉 🤖链接: https://t.me/bjwyls/22143?single 🏠位置: #朝阳 #百子湾 ------------------ 🖋课后作业:来北京出差,在无忧群看到了芝芝莓莓的双✈️,约了时间,兴致勃勃的赶到地方,万万没想到接下来半小时像吃屎了一样恶心。进屋之后两个人在吃外卖,我还好心的说了句你们还没吃饭啊,挺辛苦啊。接着就是刷手机等她们吃外卖,然后我自己去洗澡,洗完澡坐床上等她们,颜值路人水平,来了之后躺床上接着聊天,说好累啊,下面的不想接了,摆着一副死鱼脸,给我搞的一脸懵,期间我说了一句你们两个谁口一下啊,竟然问我能不口吗?口活很糙。一上来口10秒硬了,就用手搓。哦想着可能嫩妹不太会,我说那我来点前戏吧,想亲个熊还躲不让亲,我也是醉了,路边的快餐也没这么臭脸啊。 自己啥都不会就算了,还不给别人亲。就这样尬着,瞬间没兴致了,我说带雨伞吧,你们累的话快点结束,做的时候完全死鱼躺在床上,赶紧出…
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💃老师:#喵喵酱 🤖链接: https://t.me/bjwyls/25031?single 🏠位置: #朝阳 #百子湾 ------------------ 🖋课后作业:校长,今天cj喵喵酱,第一次留差评,一脸臭,配合度差,过程中还滑手机,建议绕道
Showing the 12 most recent of 131 posts we hold for @qicherj. 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.
@qicherj edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
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.
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 17 September 2026 — this entry's latest reading, not the date you are reading this.
“骑车日记” (@qicherj), 13,129 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/qicherj.
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.