👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…

Channel
全国修车联盟
@langtianxia
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
55,832subscribers
-21,535 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
Register entry
| Telegram ID | -1002246243677 |
|---|---|
| Type | Channel |
| Username | @langtianxia |
| Description | 狼友出击 水流成河 全国修车同城 https://t.me/qg8888889 北京,天津, 上海, 重庆,河北,山西,辽宁,吉林,黑龙江,江苏,浙江,安徽,福建,江西,山东,河南,湖北,湖南,广东,海南,四川,贵州,云南,陕西,甘肃,青海,台湾,内蒙古,广西,西藏,宁夏,新疆,香港,澳门。 |
| 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 | 7 September 2026 |
| Measurements held | 27 |
| Confirmed unchanged | 1 time, most recently 7 September 2026 |
| On Telegram | t.me/langtianxia |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 7 Sept 2026, 18:41 | 55,832 | -1,427 |
| 4 Sept 2026, 16:41 | 57,259 | -464 |
| 2 Sept 2026, 23:36 | 57,723 | -285 |
| 1 Sept 2026, 21:54 | 58,008 | -189 |
| 31 Aug 2026, 18:17 | 58,197 | -98 |
| 30 Aug 2026, 20:57 | 58,295 | -252 |
| 29 Aug 2026, 20:44 | 58,547 | -135 |
| 28 Aug 2026, 21:52 | 58,682 | -114 |
| 27 Aug 2026, 21:24 | 58,796 | -25 |
| 27 Aug 2026, 00:04 | 58,821 | -102 |
| 25 Aug 2026, 23:48 | 58,923 | -218 |
| 24 Aug 2026, 20:52 | 59,141 | -141 |
| 23 Aug 2026, 08:44 | 59,282 | -187 |
| 21 Aug 2026, 21:06 | 59,469 | -83 |
| 20 Aug 2026, 14:42 | 59,552 | -72 |
| 19 Aug 2026, 13:17 | 59,624 | -87 |
| 18 Aug 2026, 10:53 | 59,711 | -123 |
| 17 Aug 2026, 09:27 | 59,834 | -382 |
| 15 Aug 2026, 15:22 | 60,216 | -238 |
| 14 Aug 2026, 00:27 | 60,454 | first reading |
Engagement
26 posts held, back to 12 February 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 74 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.47%
- avg views ÷ 55,832 subscribers
- Avg views / post
- 1,380
- 8 posts measured
- Reaction rate
- 0.123%
- reactions ÷ views · ER floor
- Posts in window
- 8
- of 26 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 8 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 7 September 2026 |
|---|---|
| Posts held | 26 (12 February 2025 – 7 September 2026) |
| Views total | 11,052 |
| Reactions total | 6 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Sept 2026, 10:05 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
- Photos
- 11
- Links
- 13
Lifetime counters from Telegram’s own channel header, read 9 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
Reaction mix
440 reactions across 20 posts, in 10 distinct kinds. The most used accounts for 48.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 215 | 48.9% | |
| 💩 | 70 | 15.9% | |
| 👍 | 38 | 8.64% | |
| 👏 | 32 | 7.27% | |
| 🥰 | 30 | 6.82% | |
| 🤮 | 27 | 6.14% | |
| 🔥 | 18 | 4.09% | |
| ❤🔥 | 6 | 1.36% | |
| 🤩 | 3 | 0.682% | |
| 👎 | 1 | 0.227% |
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 21 of the 26 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 440 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 26 most recent posts we hold, published 12 February 2025 to 7 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
👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…
👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…
👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…
👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…
❤1
👍查婚姻.开房记录.手机定位.银行微信流水.车辆轨迹.模糊找人 ❤️👨🏻✈️个户全户🔥婚姻记录👨🏻✈️✅ ❤️👨🏻✈️开房记录🔥同住记录👨🏻✈️✅ ❤️👨🏻✈️名下号码🔥名下房产👨🏻✈️✅ ❤️👨🏻✈️犯罪在逃🔥模糊找人👨🏻✈️✅ ❤️👨🏻✈️冻结原因🔥社保地址👨🏻✈️✅ ❤️👨🏻✈️宽带地址🔥关联一切👨🏻✈️✅ ❤️👨🏻✈️三网位置️🔥三网机主👨🏻✈️✅ ❤️👨🏻✈️三网话单🔥定位找人👨🏻✈️✅ ❤️👨🏻✈️手机轨迹🔥快递地址👨🏻✈️✅ ❤️👨🏻✈️淘宝地址🔥微信反查👨🏻✈️✅ ❤️👨🏻✈️微信流水🔥支付流水👨🏻✈️✅ ❤️👨🏻✈️微好友提🔥Q 好友提👨🏻✈️✅ ❤️👨🏻✈️护照正反🔥名下车档👨🏻✈️✅ ❤️👨🏻✈️三页汽车🔥汽车违章👨🏻✈️✅ ❤️👨🏻✈️支付码反🔥历史户籍👨🏻✈️✅ ❤️👨🏻✈️身份轨迹🔥小档汽车👨🏻✈️✅ ❤️👨🏻✈️车辆轨迹🔥企业法人👨…
以下是各市区中转站 南宁 @al_qunfudhah 南京 @ajinkya 珠海 @toncoins101 东莞 @jukedeckai 长沙 @livejournall 重庆 @hhggjjj451 义乌 @louvrehotels 海南 @nhgjjnbjk58745 苏州 @g_40000 福建 @xiuc584855455666 成都 @cd475445 广东 @ejettons 沈阳 @qwwassaa22332 北京 @sdgftyhueerh2 长春 @dfdd453f 贵阳 @nhdfhd223 郑州 @ssdfd2311 昆明 @wsaa1231 温州 @ddss33241 上海 @iq748 宁波 @dashboardapp 嘉兴 @dangzaiwoxinzhong 杭州 @dwflabz 哈尔滨 @ssdds321e 武汉 @eldor_khalliyev 天津 @lj…
❤5
https://t.me/zltal 加总群
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❤4
以下是各市区中转站 南宁 @al_qunfudhah 南京 @ajinkya 珠海 @toncoins101 东莞 @jukedeckai 长沙 @livejournall 重庆 @hhggjjj451 义乌 @louvrehotels 海南 @nhgjjnbjk58745 苏州 @g_40000 福建 @xiuc584855455666 成都 @cd475445 广东 @ejettons 沈阳 @qwwassaa22332 北京 @sdgftyhueerh2 长春 @dfdd453f 贵阳 @nhdfhd223 郑州 @ssdfd2311 昆明 @wsaa1231 温州 @ddss33241 上海 @iq748 宁波 @dashboardapp 嘉兴 @dangzaiwoxinzhong 杭州 @dwflabz 哈尔滨 @ssdds321e 武汉 @eldor_khalliyev 天津 @lj…
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第一步:点击 @spambot 此为唯一官方解限制bot,其余均为假冒,有盗号风险 第二步:点击 Start 第三步:点击 But I can't message non-contacts! 第四步:点击 No,I'll never do any of this! 第五步:回复 accident #无法私聊 #限制解除
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近期出现很多冒充管理员,他们的ID,头像,名字一般都会设置的跟管理员很接近,以可以帮助上榜,广告低价为由收取费用,一般管理员是不会主动私信你们的,私信基本百分之99都是骗子,不要相信突然莫名的私信,已经有人被骗。
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Showing the 12 most recent of 26 posts we hold for @langtianxia. 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
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.
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.
@cr12388 · 30,0078 posts南京中转站
@ajinkya · 7,2685 posts南宁中转站
@al_qunfudhah · 6,2135 posts成都中转站
@cd475445 · 5,2675 posts温州中转站
@ddss33241 · 4,8775 posts长春中转站
@dfdd453f · 5,1155 posts杭州中转站
@dwflabz · 7,3595 posts广东地区中转站
@ejettons · 6,6505 posts武汉中转站
@eldor_khalliyev · 8,0315 posts苏州中转站
@g_40000 · 7,8955 posts西安中转站
@hiailand · 6,3515 posts上海中转站
@iq748 · 6,8615 posts长沙中转站
@livejournall · 6,5125 posts天津中转站
@ljhjikknnj8875 · 5,0955 posts贵阳中转站
@nhdfhd223 · 4,9345 posts海南地区修车中转
@nhgjjnbjk58745 · 5,0045 posts广州中转站
@opros_siki · 6,6945 posts沈阳中转站
@qwwassaa22332 · 5,1955 posts南昌中转站
@sddfss22222 · 4,9905 posts北京昆明成都中转站
@sdgftyhueerh2 · 5,3405 posts哈尔滨中转站
@ssdds321e · 5,0965 posts珠海中转站
@toncoins101 · 5,8515 posts昆明中转站
@wsaa1231 · 4,9745 posts福建省地区修车中转站
@xiuc584855455666 · 6,2245 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.
Handles this channel named that no longer answer
- Dead references
- 1
- handles named in this channel’s posts, vacant today
- Evidenced gone
- 0
- we ourselves saw one of these resolve, at some point
- Never seen alive
- 1
- vacant every time we have ever looked
@langtianxia named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
named in 9 posts, 20 August 2026 – 9 September 2026
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 7 September 2026 — this entry's latest reading, not the date you are reading this.
“全国修车联盟” (@langtianxia), 55,832 subscribers as measured 7 September 2026. Telegram Register, tgregister.com/channel/langtianxia.
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.