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Channel

成都爱情公寓修车(车库表)

@u3z9cvME_BwxYmY1

Has discussion group

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

6,444subscribers

+3,242 since we began measuring on 11 September 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1004296414286
TypeChannel
Username@u3z9cvME_BwxYmY1
CreatedBetween 1 May 2026 and 31 August 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded11 September 2026
Last confirmed live4 October 2026
Measurements held6
Confirmed unchanged1 time, most recently 4 October 2026
On Telegramt.me/u3z9cvME_BwxYmY1

Topic

Adult — 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 4 October 2026 and assigned it the closest of 31 fixed categories, at 89% 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

3,2026,4444,82311 September 2026 — 3,202 subscribers11 September 2026 — 3,202 subscribers13 September 2026 — 3,398 subscribers16 September 2026 — 4,022 subscribers25 September 2026 — 4,972 subscribers4 October 2026 — 6,444 subscribers11 September 20264 October 2026
6 measurements spanning 23 days, net +3,242. 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 2,716–6,930 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
4 Oct 2026, 20:366,444+1,472
25 Sept 2026, 11:214,972+950
16 Sept 2026, 14:414,022+624
13 Sept 2026, 02:563,398+196
11 Sept 2026, 23:283,202no change
11 Sept 2026, 23:223,202first reading

Engagement

4 posts held, back to 8 September 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 1 page of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.37%
avg views ÷ 6,444 subscribers
Avg views / post
475
4 posts measured
Reaction rate
0.211%
reactions ÷ views · ER floor
Posts in window
4
of 4 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 10 September 2026
Posts held4 (8 September 2026 – 10 September 2026)
Views total1,900
Reactions total4
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Sept 2026, 23:22 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
15s
Average length
15s

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.

Reaction mix

4 reactions across 4 posts, in 1 kind.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🌭4100.0%

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 4 of the 4 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 4 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 4 most recent posts we hold, published 8 September 2026 to 10 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.

Recent posts

10 Sept 2026, 13:49 UTC151 views1 reactionsread 11 September 2026
Photo

📊 车评数据|0条|综合- 👍 好评 0%|人照-|服务- 👌 中评 0%|颜值-|态度- 👎 差评 0%|身材-|环境- 👇【点击查看评价】 ──────────── 【成都爱情公寓认证】 ⭐️编号: #A140 🌈花名: #芸豆 🌎联系:@yunxi533 👜上牌推荐人:@qxyb66 📍地址: #武侯区石羊场 💦课费:#8p 13pp 🎹标签: #武侯区 #模特身材 #服务车 #嫩妹车 #腿控车 #性感车 #代聊 🌟硬件:24岁 身高:168cm 体重95 🐻B 🌊服务:,浅吻,口,舔🥚,指划都有, ✅验证: #已验证 🍆评价:成都御姐,24岁,168高,体重95,肚子没有赘肉,🐻b,户型🦋,御姐车和服务车,值得一提妹妹很懂男人,情绪价值很高,合理要求都可以满足,特别听话,陪洗认真,到床上妹妹先来舔胸,然后就开始口,口技可以很舒服也口了很久,还玩了会指划,还会舔🥚,女上开始,很会动,一边…

🌭1

Signed 青森

9 Sept 2026, 06:55 UTC≈1,190 views1 reactionsread 11 September 2026
Photo

📊 车评数据|0条|综合- 👍 好评 0%|人照-|服务- 👌 中评 0%|颜值-|态度- 👎 差评 0%|身材-|环境- 👇【点击查看评价】 ──────────── 【成都爱情公寓认证】 ⭐️编号: #A139 🌈花名: #潮喷天后 🌎联系:@cpth5 👜上牌推荐人:@qazqazmm 📍地址: #武侯区新乐路 💦课费:#7p/50min 13pp/ 90min 🎹标签: #武侯区 #少妇车 #潮喷 #少妇 #大奶 #服务车 #SM #大蟒蛇 #白虎 🌟硬件:26-28左右岁 身高:168cm 体重110 🐻D, 人工白虎 🌊服务:,69,无套吹,大蟒🐍,爱爱,课表都有,前列腺高潮、抓龙筋、 独家毒龙 ✅验证: #已验证 🍆评价:看频道老师今天刚开、价格合理、看频道感觉应该是人照不会相差很大的,按老师导航上楼,一进门,人照合一不虚此行,钱包也不心疼了,交了水费抓紧冲凉,潮喷天后宝宝的服务…

🌭1

Signed 青森

9 Sept 2026, 06:31 UTC293 views1 reactionsread 11 September 2026
Photo

🎉 5 步完成・轻松搞定・简单高效 🎉 💡分步操作 + 秒速参考: ✍️从「爱情公寓」开始 进入对应群聊,找到爱情公寓专属入口 ✍️ 在群输入框输入「提交报告」发送 在聊天框打字,点击右侧发送按钮 ✍️点击消息里的「提交报告」按钮 找到机器人推送的消息,点下方按钮 ✍️ @心爱老师 开始填写 助手弹出后,输入 @心爱老师 触发填写入口 ✍️填写内置模板后提交 按顺序填完所有字段,可上传图片,最后点提交 ⚠️ 重要提醒:请按顺序填写全部字段,不要漏项 💞有你在,爱更有温度 💞

🌭1

Signed 青森

8 Sept 2026, 12:37 UTC266 views1 reactionsread 11 September 2026
Photo

📊 车评数据|0条|综合- 👍 好评 0%|人照-|服务- 👌 中评 0%|颜值-|态度- 👎 差评 0%|身材-|环境- 👇【点击查看评价】 ──────────── 【成都爱情公寓认证】 ⭐️编号: #A138 🌈花名: # 小七 🌎联系:@xiaoqi1836 👜上牌推荐人:河马言 📍地址: #金牛区驷马桥 💦课费:#7p/ #11pp/ 夜#2200 🎹标签: #嫩妹车 #颜值车 #舌吻 #性感车 #丝袜 #皮肤滑 #身材车 #代聊 🌟硬件:22岁 身高:168cm 体重90 🐻c+, 🌊服务: 陪洗,69,无套吹,大蟒🐍,爱爱,课表都有。 ✅验证: #已验证 🍆评价:老师的优点:颜值少御,腿长身材好,大C和翘臀,大蟒🐍,态度好服务好。和妹子约好时间,进门妹妹很热情,因为最近天气比较热,进门先到沙发坐下聊会儿,还会主动的拿着纸巾帮忙擦汗,休息一会就一起脱衣去洗澡,脱掉衣服看到老…

🌭1

Signed 青森

Showing the 4 most recent of 4 posts we hold for @u3z9cvME_BwxYmY1. 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.

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 4 October 2026 — this entry's latest reading, not the date you are reading this.

“成都爱情公寓修车(车库表)” (@u3z9cvME_BwxYmY1), 6,444 subscribers as measured 4 October 2026. Telegram Register, tgregister.com/channel/u3z9cvME_BwxYmY1.

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.