Telegram RegisterThe public register of Telegram
Telegram profile photo for 全国修车联盟

Channel

全国修车联盟

@langtianxia

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts recorded as deleted · Citations · Handles named that no longer answer · Cite this entry

81,147subscribers

+3,780 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
TypeChannel
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。
Created5 June 2024 — measured — dated from the channel’s first post
First recorded7 August 2026
Last confirmed live13 November 2026
Measurements held63
Confirmed unchanged1 time, most recently 13 November 2026
On Telegramt.me/langtianxia

Topic

Hacking & security — 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 94% 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 the other topics.

Growth

42,23381,14761,6907 August 202613 November 2026
63 measurements spanning 98 days, net +3,780. 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 36,396–86,984 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 63
Measured (UTC)SubscribersChange
13 Nov 2026, 01:4681,147+599
10 Nov 2026, 18:2780,548+476
9 Nov 2026, 08:1180,072+535
8 Nov 2026, 00:0679,537+410
6 Nov 2026, 20:0779,127+412
5 Nov 2026, 16:4478,715+315
4 Nov 2026, 15:5778,400+541
3 Nov 2026, 12:3977,859+3,255
2 Nov 2026, 10:2674,604+16,932
1 Nov 2026, 09:5457,672+10,581
31 Oct 2026, 12:0047,091+338
30 Oct 2026, 12:3546,753+263
29 Oct 2026, 09:1846,490+307
28 Oct 2026, 10:4946,183+278
27 Oct 2026, 10:3545,905+195
26 Oct 2026, 09:4545,710+515
25 Oct 2026, 06:4645,195+246
24 Oct 2026, 08:4644,949+452
23 Oct 2026, 06:5544,497+279
22 Oct 2026, 07:5044,218first reading

Engagement

52 posts held, back to 5 June 2024 — the reader has reached the start of this channel’s public history, so this is the full archive Telegram still exposes. Read across 204 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
1.12%
avg views ÷ 81,147 subscribers
Avg views / post
911
5 posts measured
Reaction rate
0.138%
reactions ÷ views · ER floor
Posts in window
5
of 52 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 2 of 5 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 2 November 2026
Posts held52 (5 June 2024 – 2 November 2026)
Views total4,553
Reactions total5
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken14 Nov 2026, 00:58 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
8
Links
4

Lifetime counters from Telegram’s own channel header, read 14 November 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

2,076 reactions across 34 posts, in 11 distinct kinds. The most used accounts for 29.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤61329.5%
🔥33716.2%
🥰33416.1%
🎉30714.8%
🤩30714.8%
💩723.47%
👍391.88%
👏321.54%
🤮281.35%
❤‍🔥60.289%
👎10.048%

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 36 of the 52 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 2,076 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 52 most recent posts we hold, published 5 June 2024 to 2 November 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

Telegram’s own ‘edited’ marker: 0 of 5 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

2 Nov 2026, 12:28 UTC187 viewsread 3 November 2026
Photo

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…

22 Oct 2026, 09:13 UTC≈3,200 views4 reactionsread 14 November 2026

👍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👨…

❤4

20 Oct 2026, 16:43 UTC325 viewsread 21 October 2026

👍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👨…

18 Oct 2026, 17:42 UTC427 viewsread 20 October 2026

👍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👨…

16 Oct 2026, 18:42 UTC414 views1 reactionsread 18 October 2026

👍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

14 Oct 2026, 19:42 UTC428 viewsread 16 October 2026

👍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👨…

12 Oct 2026, 20:42 UTC392 views1 reactionsread 14 October 2026

👍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

10 Oct 2026, 21:42 UTC511 views1 reactionsread 12 October 2026

👍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

8 Oct 2026, 22:42 UTC519 views1 reactionsread 10 October 2026

👍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

6 Oct 2026, 23:42 UTC490 viewsread 8 October 2026

👍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👨…

5 Oct 2026, 00:42 UTC486 views4 reactionsread 6 October 2026

👍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👨…

❤4

3 Oct 2026, 01:42 UTC492 viewsread 4 October 2026

👍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👨…

Showing the 12 most recent of 52 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.

Posts recorded as deleted after publishing

We recorded 5 posts on @langtianxia as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
21 August 2026
Most recent seen missing
29 August 2026
Median time before seen missing
2 days
Seen missing in the last 30 days
0

Mentions

Named by 4 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 13 November 2026 — this entry's latest reading, not the date you are reading this.

“全国修车联盟” (@langtianxia), 81,147 subscribers as measured 13 November 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.