我觉得这个东西顶多10块钱。我儿子认为这个80块钱还是挺值的,即使是二手货。 #代沟
❤5

Channel
@huruanhuying
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
2,683subscribers
+8 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001400891266 |
|---|---|
| Type | Channel |
| Username | @huruanhuying |
| Created | Between 1 April 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 16 August 2026 |
| Measurements held | 5 |
| Confirmed unchanged | 1 time, most recently 16 August 2026 |
| On Telegram | t.me/huruanhuying |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Aug 2026, 06:26 | 2,683 | +1 |
| 10 Aug 2026, 01:12 | 2,682 | +3 |
| 7 Aug 2026, 03:13 | 2,679 | +4 |
| 6 Aug 2026, 09:32 | 2,675 | no change |
| 6 Aug 2026, 09:24 | 2,675 | first reading |
20 posts held, back to 21 October 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 2 pagesof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 30 July 2026 |
|---|---|
| Posts held | 20 (21 October 2025 – 30 July 2026) |
| Views total | 740 |
| Reactions total | 5 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 16:15 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.
162 reactions across 15 posts, in 4 distinct kinds. The most used accounts for 69.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 113 | 69.8% | |
| 👍 | 46 | 28.4% | |
| 💔 | 2 | 1.23% | |
| 🙏 | 1 | 0.617% |
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 15 of the 20 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 162reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 21 October 2025 to 30 July 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.
我觉得这个东西顶多10块钱。我儿子认为这个80块钱还是挺值的,即使是二手货。 #代沟
❤5
#世界观 过去这一年最大的收获,是完成了对世界的‘祛魅’。曾经觉得高不可攀的人和事,扒开底牌,多半是勉力维持的草台班子。以前读《史记》里项羽那句‘彼可取而代之’,以为是狂妄;现在看,全是写实。既然大家都是赶鸭子上架的草台班子,凭什么我不行?过去面对未知,第一反应是‘我不配’;现在的信条是‘试试看’。尤其在AI的杠杆加持下,试错的门槛和障碍已被大幅降低。简单粗暴点说:告别精神内耗,别怂,躬身入局就是干。
👍20❤3🙏1
https://youtu.be/HwHVvI4ljWI 回忆小时候的事情
https://youtu.be/fphJxYd5hpo 80年代的小孩,没有才艺辅导,也得表演节目
https://youtu.be/dcxdQy-SZt0 在初中的时候,男生,发育快的已经开始谈恋爱了,我,发育慢的,还在玩洋火枪,但是,因为一次奇怪的事情,我跟发育快的,同时被老师K了一顿!当然,我被K的更惨。
👍2
https://youtu.be/Iwp1BiVi3GI 这个故事是我初中有一次考历史,记忆非常的深刻。
我不知道是不是人到了一定年龄,比如我现在40+,就开始回忆小时候的事情。 前几天,我发现了答案,在网上看到了一个介绍“怀旧回峰”(Reminiscence Bump)的理论,理论是这样的:成年人在忆一生中印象最深刻的事,他们的记忆并不会均匀分布,而是会集中在两个时间段:一是最近发生的事;二是10岁到30岁之间的事情(也就是童年晚期、青少年期到青年早期)。 这个在生命早期形成的记忆高峰,就被称为“怀旧回峰”。即使到了七八十岁,人们对十几岁时听过的歌、经历过的夏夜、童年的玩伴,记忆的清晰度往往远超过他们四五十岁时的中年生活。 在生物学上,是这样讲的:在10到25岁之间,大脑中负责记忆编码的海马体和负责情感处理的杏仁核功能最活跃,神经元连接的塑造性极强。那个时期,大脑分泌的多巴胺等神经递质也处于高水平。这就意味着,那时候的经历自带“高清重置”和“情感放大”滤镜,存储得最深、最牢固。随着年龄增长,中年的记忆往往因为日复一日的重复工…
❤18
我7岁的时候,我爸妈,我三叔要去泰山求一个儿子,我闹着要跟着去。结果又是误车,又是赶路,那天,我走了47公里。记录一下。 https://youtu.be/mhFkmQNieys
❤5
https://youtu.be/I9Vn8tMl9aA 我每年都要去N次泰山,我是山东人,我的身份证号可以买年卡。每当有朋友来找我,我都会问,爬过泰山么? 前前后后,我觉得我就算没去100次,也有80次了。昨天,我又去了泰山,我就录了好多视频,我对泰山最感兴趣的是碑文。泰山上的碑文有两大类,一类是“到此一游”类,另一类是“宣传政治理想类”。 因为大多的碑文都是上百年历史,500年,甚至1500多年(比如经石峪那边,是北齐时期的),很多都已经看不清了。我就拍了一些照片,我剪一下,上传上来。 我发现网上并没有专门介绍泰山石碑的,我就抛砖引玉了。 #泰山 #五岳独尊 #石碑
❤10
https://www.youtube.com/watch?v=youtjGM7jNI 以前我录视频都是瞎JB录的,这次我是想自己学习大语言模型的底层知识了,想通过录视频,整理自己的思路。算是用了心的。 这个视频讲了2013年Word2vec的历史,这是目前大语言模型的祖宗。如果有人想玩一下视频里的模型,可以在我的网站上玩。我部署了一个小小的服务。 https://liuyandong.com/ai/
❤4
我访谈了一个嘉宾,这哥们从重庆飞加拿大,在多哈转机,结果,美伊战争开打了,哥们在那边滞留了10来天。他还算幸运的,只有一班飞机起飞了,还有人至今留在那里,或者回中国了。 https://www.youtube.com/watch?v=9DTjmdyiNBY
https://www.youtube.com/watch?v=6R-YCEkSSfo 最近研究大模型,把学到的都记录一下。
❤6
Showing the 12 most recent of 20 posts we hold for @huruanhuying. 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.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
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 16 August 2026 — this entry's latest reading, not the date you are reading this.
“汗牛充栋” (@huruanhuying), 2,683 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/huruanhuying.
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.