✖️ CNC热点 从习明泽做川普的翻译来看,习明泽应该是个美国人!具有美国国籍。同时她又是中国局级领导人!

Channel
☠️三战快报☠️一级战备
@endofxi
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
21,592subscribers
+558 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1001487088872 |
|---|---|
| Type | Channel |
| Username | @endofxi |
| Description | 投稿 @xibibibot 投稿方法 https://t.me/endofxi/98162 👻十八国联军👻 https://t.me/endofchina 庆丰包子铺 https://t.me/bao200 |
| Created | 11 March 2020 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 26 September 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 26 September 2026 |
| On Telegram | t.me/endofxi |
Topic
National news — 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 10 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 why it has no browse page of its own yet.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 26 Sept 2026, 17:01 | 21,592 | +116 |
| 19 Sept 2026, 17:41 | 21,476 | +28 |
| 17 Sept 2026, 06:01 | 21,448 | +12 |
| 15 Sept 2026, 08:02 | 21,436 | +43 |
| 13 Sept 2026, 16:40 | 21,393 | -12 |
| 11 Sept 2026, 23:40 | 21,405 | -15 |
| 9 Sept 2026, 17:35 | 21,420 | +22 |
| 6 Sept 2026, 11:57 | 21,398 | +10 |
| 4 Sept 2026, 02:18 | 21,388 | -1 |
| 2 Sept 2026, 18:45 | 21,389 | -9 |
| 1 Sept 2026, 14:34 | 21,398 | +11 |
| 31 Aug 2026, 14:58 | 21,387 | +7 |
| 30 Aug 2026, 17:33 | 21,380 | -2 |
| 29 Aug 2026, 20:42 | 21,382 | +14 |
| 28 Aug 2026, 19:47 | 21,368 | +11 |
| 26 Aug 2026, 13:58 | 21,357 | +31 |
| 25 Aug 2026, 10:45 | 21,326 | -2 |
| 24 Aug 2026, 11:34 | 21,328 | -4 |
| 22 Aug 2026, 20:35 | 21,332 | -2 |
| 21 Aug 2026, 11:45 | 21,334 | first reading |
Engagement
324 posts held, back to 6 August 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 59 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 1.08%
- avg views ÷ 21,592 subscribers
- Avg views / post
- 234
- 27 posts measured
- Reaction rate
- 0.277%
- reactions ÷ views · ER floor
- Posts in window
- 27
- of 324 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 4 of 27 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 27 September 2026 |
|---|---|
| Posts held | 324 (6 August 2026 – 27 September 2026) |
| Views total | 6,323 |
| Reactions total | 3 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 27 Sept 2026, 04:17 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
- ≈162,000
- Videos
- ≈55,700
- Links
- ≈143,000
Lifetime counters from Telegram’s own channel header, read 27 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
- Video runtime
- 1h 18m
- Average length
- 1m 13s
Measured directly from 64 videos 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
39 reactions across 31 posts, in 9 distinct kinds. The most used accounts for 76.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 30 | 76.9% | |
| 👍 | 2 | 5.13% | |
| 🎉 | 1 | 2.56% | |
| 👏 | 1 | 2.56% | |
| 🔥 | 1 | 2.56% | |
| 🖕 | 1 | 2.56% | |
| 😁 | 1 | 2.56% | |
| 🤨 | 1 | 2.56% | |
| 🤪 | 1 | 2.56% |
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 42 of the 324 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 39 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 324 most recent posts we hold, published 6 August 2026 to 27 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
✖️ CNC热点 对牛腾宇这么狠!看眼神就知道结果了。 https://22.chinanewscenter.com/archives/25779
✖️ CNC热点 https://chinanewscenter.com/archives/62316 --------- 📥 Download IT Bot — download almost any media to your mobile or computer.
✖️ CNC热点
✖️ 李老师不是你老师 网友投稿:名为“枣庄正能量”的账号发布视频,显示枣庄一中学的优秀毕业生在等待领奖时刷题。
Posted without readable text
❤1
✖️ 李老师不是你老师 9月24日,澳大利亚广播公司(ABC)中文网发文《人工智能如何重塑全球力量平衡》,该文指出美国在数据中心、私人投资和领先模型方面占主导地位,中国则在发电量、机器人技术、研究产出和制造能力方面领先。 但同时该文也强调,虽然美国或许在软件或芯片设计方面占主导地位,但在制造最先进芯片方面,主导全局的是台湾,而台湾高度依赖来自荷兰、日本和韩国的专用设备、材料和化学品。 文章表示:“因此,即便美国在设计或软件上领先,仅仅是日本化学材料的短缺,或是荷兰光刻机关键部件的匮乏,就可能让整个系统陷入瘫痪。”
❤1
✖️ 李老师不是你老师 9月25日,《华尔街日报》发文回顾习近平此次访美,披露多个值得关注的细节。 习近平在白宫国宴上没有穿常见西装,而是身着“毛装”。报道援引学者分析称,这一选择可以被理解为:北京接受华盛顿给予的最高规格礼遇,却并不意味着中国政治制度本身发生任何改变。 值得注意的是,习近平2015年参加奥巴马举行的白宫国宴时也曾身穿“毛装”,因此这一象征性解读属于媒体和学者分析。 白宫国宴官方材料中,对中共使用了北京偏好的英文简称“CPC”,而不是美国政治语境中常见的“CCP”。《华尔街日报》称,这给了北京一个小小的象征性收获。 与此同时,白宫内外形成鲜明反差。 报道援引国宴参与者称,习近平进入白宫后,有约30至40名宾客要求与他合影;但在白宫和美国国家档案馆外,抗议者则高喊“Down with Xi Jinping”,举起反对习近平和中共的标语。 《华尔街日报》还注意到,国家档案馆附近出现一些身穿深色衣服、戴口罩和…
✖️ 李老师不是你老师 9月25日,《华尔街日报》报道,习近平此次访美期间宣布,两只大熊猫“平平”和“福双”将在几天后抵达亚特兰大动物园,并称熊猫是“中美两国人民友谊的使者”。文章认为,曾在中美外交中具有特殊象征意义的“熊猫外交”,如今已不复昔日分量。 不过,这并不是此次习特会才达成的新协议。亚特兰大动物园早在今年4月就已宣布与中方达成合作,并公布“平平”和“福双”的名字;习近平此次真正新增的信息,是两只熊猫将在几天内抵美。 《华尔街日报》将今天与1972年进行了对比:当年中国直接向美国赠送“兴兴”和“玲玲”,熊猫成为中美关系破冰的重要象征;进入1980年代后,中国逐渐改为向外国动物园租借熊猫。 文章认为,在贸易、科技、军事和安全竞争已经成为中美关系重要议题的今天,熊猫继续能够制造公众好感,但已经很难再承担半个世纪前那种改变中国国家形象的外交作用。 此次即将赴亚特兰大的“平平”和“福双”,也因此形成了一个颇具时代意味的对…
✖️ 李老师不是你老师 9月25日,《经济学人》刊文评论习近平此次访美,称三天国事访问充满精心设计的礼仪和排场,但实质成果有限。 文章称,特朗普此次接待方式“模仿并超越了中国偏爱的国事访问模式”:亲自到机场迎接习近平,安排大量仪仗和拍照环节,却没有举行可供记者自由提问的联合记者会。 《经济学人》还称,对习近平而言,被特朗普以“平等者”规格接待不只是面子问题。文章预计习近平将在明年中共党代会上寻求第四个任期,并认为此次美国给予的高规格礼遇,有助于塑造其国际地位进一步上升的形象。 文章同时指出,习近平试图推动美国将台湾政策表述从“不支持台湾独立”改为“反对台湾独立”,但截至离美,没有证据显示特朗普接受了这一要求。 《经济学人》最后讽刺称,在台湾、AI和战争等重大问题仍悬而未决之际,美中领导人至少在一件事上找到了共同利益——“排场”。
✖️ 李老师不是你老师 9月26日,《日经亚洲》发文总结习近平此次访美,称特朗普为习近平安排了机场亲迎、军事仪式、21响礼炮、国宴等高规格礼遇,但三天峰会在贸易、台湾、稀土、投资和科技管制等核心问题上并未出现重大突破,许多争议只是被延后处理。路透社此前也将此次峰会概括为“象征意义浓厚,但实质突破有限”。 其中最值得关注的是台湾问题。 中国外交部公布的会谈通稿显示,习近平当面要求特朗普“坚持反对‘台独’的正确立场,慎重处理台湾问题”。 但白宫9月25日公布的峰会成果清单详细列出了贸易、稀土、伊朗、军控和AI等内容,却没有提到台湾。此前路透社还披露,习近平访美前预计会要求特朗普停止或减少对台军售。 《日经亚洲》因此分析称,北京“可能”在台湾问题上取得了此次峰会最大的收益。 另一条值得关注的线索是稀土。白宫成果清单承认,中美仍在继续处理美国对稀土和其他关键矿产供应短缺的关切。《日经亚洲》称,中国目前仍生产全球超过90%的关…
✖️ 李老师不是你老师 中国政府的另一场抢险动员:报道话语权 9月1日,《对话》发表研究文章称,尼藏边境洪灾发生后,中国政府的信息管控方式引发国际舆论争议。 BBC中国报道记者称,吉隆口岸遭洪水吞噬的视频在微信和小红书上遭到删除,外国及独立记者亦被拒于西藏灾区门外,无法独立核实伤亡数字。
Showing the 12 most recent of 324 posts we hold for @endofxi. 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
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 2 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.
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.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
@XueXi_China · 87,102#1
@times001 · 84,754#2
@shixun160 · 22,955#3
@mightyflame · 26,081#4
@warmilitary · 4,444#5
@pelosi3 · 71,125#6
@ww3ch · 9,012#7
@Ruters0615 · 25,532#8
@tnews365 · 164,267#9
@chinafounder · 14,833#10
@voachinese · 34,696#11
@CHlNA011 · 1,954#12
@xingchen0829 · 7,479#13
@CallVoter · 2,307#14
@Taiwannews1101218 · 3,651#15
@lilaoshibushinilaoshi · 48,585#16
@zaihuapd · 283,183#17
@RFI_Cn · 7,143#18
@LudeMediaMP3 · 917#19
@fangshimin · 16,154#20
@iyouport · 27,814#21
@antiTrumpTopic · 498#22
@renminbao1 · 2,717#23
@RidingWaveWithQ · 6,708#24
@nbgzd · 10,411#25
@ruldophwest17 · 15,470#26
@DNSPODT · 72,064#27
@JShangrong · 24,735#28
@chinaupdate · 6,274#29
@VoiceofPooh · 22,612#30
@titan_pain · 20,773#31
@xhqcankao · 163,238#32
@Mandark_5678 · 4,835#33
@luojisw001 · 3,755#34
@pingrangTV · 29,178#35
@im_RORIRI · 3,838#36
@NFSCHimalayaNews · 9,457#37
@yuedu · 4,950#38
@KarenMoeMoe · 9,385#39
@ttsmr · 1,992#40
@fucku_idiot · 40,459#41
@dragonsden2k · 2,483#42
@VoPMilkTea · 1,853#43
@record_history · 1,117#44
@personal_hub · 11,556#45
@myym6886 · 2,538#46
@europechinese · 7,159#47
@cdtchinesefeed · 44,158#48
@NewsFW · 6,823#49
Read from Telegram’s recommendation API, most recently 23 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
@shixun160 · 22,955
Telegram ranks this channel #5 of 60 here — alongside 59 others — read 19 September 2026
@Ruters0615 · 25,532
Telegram ranks this channel #11 of 40 here — alongside 39 others — read 14 September 2026
@voachinese · 34,696
Telegram ranks this channel #14 of 63 here — alongside 62 others — read 3 September 2026
@times001 · 84,754
Telegram ranks this channel #18 of 66 here — alongside 65 others — read 18 August 2026
@newszg_official · 162,958
Telegram ranks this channel #21 of 52 here — alongside 51 others — read 12 August 2026
@VoiceofPooh · 22,612
Telegram ranks this channel #28 of 53 here — alongside 52 others — read 20 September 2026
@JShangrong · 24,735
Telegram ranks this channel #28 of 60 here — alongside 59 others — read 15 September 2026
@XueXi_China · 87,102
Telegram ranks this channel #28 of 48 here — alongside 47 others — read 17 August 2026
@pelosi3 · 71,125
Telegram ranks this channel #31 of 51 here — alongside 50 others — read 20 August 2026
@TwtVideoOfChina · 74,789
Telegram ranks this channel #42 of 54 here — alongside 53 others — read 20 August 2026
@hkposter777 · 22,381
Telegram ranks this channel #75 of 78 here — alongside 77 others — read 20 September 2026
This channel appears in 11 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 26 September 2026 — this entry's latest reading, not the date you are reading this.
“☠️三战快报☠️一级战备” (@endofxi), 21,592 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/endofxi.
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.