6歲女童自稱是外星人:因犯錯被送到地球 11月13日,中國一位網紅博主在一次訪談中談到,6歲的女兒說自己是外星人,描述了她所在星球的生活,並表示因犯了錯誤才被送到地球來吃苦,令他震驚。 該博主表示,女兒說她所在的星球上,所有的動物都是好朋友,沒有殺戮,只有愛。更讓他震撼的是女兒接下來的話: 「她說那個星球上百分之九十九點九九九的都是好人,然後大家會把自己的財寶都放到一起,由專門的人看管,但是有極其個別的人會想佔有別人的財寶,一旦這樣的人被發現,他們就會被送到地球上去吃苦」。 博主表示:「你感受一下,我們是犯了錯才被送到地球上來的,所以很多人說地獄在哪?地獄就在這,地獄在人間」 不過,在視頻留言區,許多網友認為這是小孩子天真的胡言亂語:「小朋友天真,想像力豐富。」「小孩不會說謊,但是會胡說八道。」「哈哈哈哈,有孩子的我真的感觸很深。」 https://x.com/yingtan04410735/status/21211…

Channel
你本該知道
@nbgzd
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
10,250subscribers
-358 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 | -1001404342216 |
|---|---|
| Type | Channel |
| Username | @nbgzd |
| Created | 25 February 2020 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 November 2026 |
| Measurements held | 63 |
| Confirmed unchanged | 1 time, most recently 12 November 2026 |
| On Telegram | t.me/nbgzd |
Topic
Religion — 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 93% 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
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Nov 2026, 18:01 | 10,250 | -11 |
| 10 Nov 2026, 13:44 | 10,261 | -9 |
| 9 Nov 2026, 03:41 | 10,270 | -4 |
| 7 Nov 2026, 22:48 | 10,274 | +1 |
| 6 Nov 2026, 17:49 | 10,273 | -3 |
| 5 Nov 2026, 17:30 | 10,276 | -5 |
| 4 Nov 2026, 17:16 | 10,281 | -1 |
| 3 Nov 2026, 15:55 | 10,282 | -7 |
| 2 Nov 2026, 16:55 | 10,289 | +2 |
| 31 Oct 2026, 13:05 | 10,287 | -4 |
| 30 Oct 2026, 11:46 | 10,291 | -4 |
| 29 Oct 2026, 10:14 | 10,295 | -3 |
| 28 Oct 2026, 05:57 | 10,298 | -3 |
| 27 Oct 2026, 08:48 | 10,301 | -4 |
| 26 Oct 2026, 05:37 | 10,305 | -2 |
| 25 Oct 2026, 02:27 | 10,307 | -6 |
| 24 Oct 2026, 01:26 | 10,313 | -3 |
| 23 Oct 2026, 02:25 | 10,316 | +5 |
| 22 Oct 2026, 01:27 | 10,311 | -9 |
| 20 Oct 2026, 20:39 | 10,320 | first reading |
Engagement
1,066 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 60 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 0.438%
- avg views ÷ 10,250 subscribers
- Avg views / post
- 44.9
- 409 posts measured
- Reaction rate
- 2.09%
- reactions ÷ views · ER floor
- Posts in window
- 411
- of 1,066 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 62 of 409 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 13 November 2026 |
|---|---|
| Posts held | 1,066 (6 August 2026 – 13 November 2026) |
| Views total | 18,361 |
| Reactions total | 56 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 13 Nov 2026, 23:29 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
- ≈61,500
- Videos
- ≈120,000
- Links
- ≈69,200
Lifetime counters from Telegram’s own channel header, read 13 November 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
- 91h 37m
- Average length
- 7m 17s
Measured directly from 755 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
98 reactions across 93 posts, in 4 distinct kinds. The most used accounts for 75.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 74 | 75.5% | |
| ❤ | 22 | 22.4% | |
| 👏 | 1 | 1.02% | |
| 😢 | 1 | 1.02% |
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 104 of the 1,000 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 98 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 1,000 most recent posts we hold (the sample is capped at 1,000), published 8 August 2026 to 13 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: 2 of 411 posts held from the last 30 days (0.487%) 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).
這樣的高層關係網,普通人能有什麼機會? 中國能有什麼未來?
📷突發新聞:在一個震撼全美的爆炸性舉動中,德克薩斯州州長格雷格·阿博特已正式根據德州法律,將全國最大的穆斯林民權組織——美國伊斯蘭關係委員會(CAIR)——指定為外國恐怖組織。
中共好 为什么还有那么多贪官?一个初中生的思考和成长 https://www.minghui.org/mh/articles/2026/11/13/中共好-为什么还有那么多贪官--516422.html
🚨 根据 Polymarket,黄仁勋在 2027 年底前重返 Nvidia 的机率为 33%。 一个因素可能对他有利:他在内部享有特别强大的支持,有 88% 的员工愿意信任他。 历史上曾见证过伟大的回归: → 史蒂夫·乔布斯于 1997 年重返苹果,距他离开已 12 年 → 霍华德·舒兹于 2008 年重掌星巴克 → 麦可·戴尔于 2007 年重掌戴尔 三次,公司都陷入困境。三次,都是为了拯救它。 希望 Nvidia 不必等到陷入困境,才与黄仁勋重修旧好。
北京紀念孫中山大會多處細節 引習健康疑雲 中共11月12日在北京舉行紀念孫中山誕辰160周年大會,與10年前的同類紀念大會相比,出現多處異常。類似的情況也出現在今年10月舉行的紀念長征大會上,被指或印證了外界對習近平健康大不如前的猜測。 大紀元記者梳理官方公開信息發現,中共黨魁習近平在紀念孫中山誕辰160周年大會上的講話,篇幅較2016年精簡了約三分之一。 另外,2016年11月11日,紀念孫中山誕辰150周年大會,習近平是站著講話;2026年紀念孫中山誕辰160周年大會上,習近平則坐著講話。 2016年當局舉行的紀念孫中山的大會,電視與網絡同步全程直播;2026年的大會則以錄播新聞與新媒體實時圖文和短視頻滾動報導,央視綜合頻道並未全程同步直播。 中國問題專家李林一表示,結合早前習近平外訪的異常狀況來看,習講話由站著改為坐著,且講話長度相比10年前大幅縮短,可能是習近平的健康出了問題,官方因此特意不安排他長時間講話。 …
據估算,黃仁勳白手起家,竟然創造了25,000至32,000名員工變成百萬富翁,比人類歷史上任何人都多。 據統計,英偉達的員工,核心來自這30所高校,29所美國理工強校,和一所加拿大數學與計算機名校。
三十歲的加拿大人奧利維婭(Olivia)生活在如詩如畫的馬斯科卡鄉村,從事園藝設計。然而,她的童年並不溫馨。家庭像紛爭不斷的戰場,父母互相傾瀉怒火。生來靦腆聽話的奧利維婭將痛苦壓抑在心裏,長期的情緒累積讓她變得抑鬱、焦慮和憤怒。長大後,奧利維婭重蹈了父母的覆轍,與丈夫吵得不可開交。幸運的是,一封郵件幫助她打破了惡性循環。 資料來源:明慧之窗 https://mhwindow.org/20260916-8024/ #法輪功 #法輪大法 #加拿大
台灣總體經濟學家吳嘉隆針對輝達執行長黃仁勳捲入的間諜風暴發表重磅看法,指出這起事件絕對不只是單純的桃色緋聞,背後真正的關鍵在於是否涉及違背美國對中共的晶片出口管制。 他直言這些親密照片早不爆晚不爆,偏偏選在現在公開,很可能是美國情報單位準備全面收網的強烈訊號。 吳嘉隆表示:如果你敢違背美國的管制,那麼你最好先衡量一下,因為美國人修理你的手段多去了,你只要看看現在的黃仁勳就知道了!美國人不會對你直接拍桌子,而是會用精緻的手段修理你! 他補充道:「在反共上,你最好跟美國配合,美國現在是回到冷戰時期,與共產主義陣營對抗,你最好早一點覺悟!總之,別再資匪通匪了!」 詳情請見: https://epochtim.es/7sb5df
❤1
11月12日,打假博主上官正義在微博曝光,河南新鄉輝縣占城鎮南靳村發現一名女子,疑似多年前遭人拐賣至當地。女子自稱來自陝西漢中城固。 網傳視頻顯示,當天正午氣溫只有11℃,女子卻赤著雙腳,身上僅披著一件男式外套,所在位置竟然是羊圈,居住環境十分惡劣。 當地鄰居透露,女子長期住在羊圈裡,幾乎從未離開過院子,偶爾有同村人提供衣物照應。 該女子引發關注後,當地政府回應稱,該女子平時不住那裡。上官正義指責當局「狡辯、否認事實」,呼籲提級或異地調查,避免被輝縣地方歪曲事實。
重大新聞 🚨:眾議院以 218–213 票通過《保護美國法案》。 該法案將要求在選舉中實施選民身份證明,最早可能從即將到來的十一月開始。 所有出席的共和黨人投了贊成票;一名民主黨人亨利·奎亞爾也投了贊成票。
這才是真正的中式恐怖
Showing the 12 most recent of 1,066 posts we hold for @nbgzd. 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.
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 · 22,693
Telegram ranks this channel #2 of 60 here — alongside 59 others — read 19 September 2026
@TwtVideoOfChina · 78,507
Telegram ranks this channel #4 of 54 here — alongside 53 others — read 20 August 2026
@ZTX95 · 58,409
Telegram ranks this channel #9 of 11 here — alongside 10 others — read 18 September 2026
@hkposter777 · 22,588
Telegram ranks this channel #17 of 78 here — alongside 77 others — read 20 September 2026
@endofxi · 22,085
Telegram ranks this channel #25 of 49 here — alongside 48 others — read 23 September 2026
@voachinese · 42,222
Telegram ranks this channel #33 of 63 here — alongside 62 others — read 3 September 2026
@appledailyhk26 · 29,339
Telegram ranks this channel #34 of 47 here — alongside 46 others — read 7 September 2026
This channel appears in 7 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 12 November 2026 — this entry's latest reading, not the date you are reading this.
“你本該知道” (@nbgzd), 10,250 subscribers as measured 12 November 2026. Telegram Register, tgregister.com/channel/nbgzd.
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.