# 读记|二〇二六・二~五:文学史、投资、风险等 幸与不幸,我们目睹了它的破灭 👉 https://quaily.com/rambling/p/read-202602-05?utm_source=telegram&utm_medium=social&utm_campaign=newsletter
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Channel
@what_to_read_today
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
3,063subscribers
+14 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001690170577 |
|---|---|
| Type | Channel |
| Username | @what_to_read_today |
| Created | Between 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 14 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 14 August 2026 |
| On Telegram | t.me/what_to_read_today |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 14 Aug 2026, 00:38 | 3,063 | +7 |
| 10 Aug 2026, 18:31 | 3,056 | +6 |
| 7 Aug 2026, 17:45 | 3,050 | +1 |
| 6 Aug 2026, 22:02 | 3,049 | first reading |
20 posts held, back to 17 May 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 3 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 31 May 2026. An engagement rate over an empty window would be a number about nothing.
182 reactions across 17 posts, in 10 distinct kinds. The most used accounts for 61.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 111 | 61.0% | |
| 👍 | 48 | 26.4% | |
| 🤔 | 6 | 3.30% | |
| 🎉 | 5 | 2.75% | |
| 😢 | 5 | 2.75% | |
| 👏 | 2 | 1.10% | |
| 😁 | 2 | 1.10% | |
| 🍌 | 1 | 0.549% | |
| 🔥 | 1 | 0.549% | |
| 😐 | 1 | 0.549% |
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 17 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 182reactions 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 17 May 2025 to 31 May 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.
# 读记|二〇二六・二~五:文学史、投资、风险等 幸与不幸,我们目睹了它的破灭 👉 https://quaily.com/rambling/p/read-202602-05?utm_source=telegram&utm_medium=social&utm_campaign=newsletter
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216/N #木心 #陈丹青 #回忆录 我对木心的印象,此前仅局限于他那首《从前慢》: 记得早先少年时 大家诚诚恳恳 说一句 是一句 清早上火车站 长街黑暗无行人 卖豆浆的小店冒着热气 从前的日色变得慢 车,马,邮件都慢 一生只够爱一个人 从前的锁也好看 钥匙精美有样子 你锁了 人家就懂了 最近几个月,读完了关于他的书《文学回忆录》,这是陈丹青听木心讲文学的笔记,从 1989 年至 1994 年,厚厚一大本。 读完后,彻底对木心改观了——浪漫、洒脱、独具个性的一个人,喜欢。这本书让我想起很多年前读的《歌德谈话录》,阅读的时候就像和大师促膝长谈,又像是和大师散步走了很长一段路。 读了很久,时不时停下来去翻阅他推荐的作者或者书籍,然后再继续读,如此反复。 木心喜欢古典,喜欢老庄、魏晋风骨,喜欢莎士比亚、陀思妥耶夫斯基,喜欢贝多芬、莫扎特,关于近现代的中国文学,他评为:琳琅满目,却一片荒凉。 有偏见,但我还挺喜欢这种偏见…
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215/N #女性主义 最近读完了弗吉尼亚·伍尔夫的《一间只属于自己的房间》,这本书是木心在《文学回忆录》里推荐的,《文学回忆录》是陈丹青推荐的,实际上这本书是他在听木心讲课的听课笔记。 伍尔夫听起来是男的,实际上她是女性,伍尔夫是她的夫姓,我还是叫她弗吉尼亚吧。这本书是她在剑桥讲「女性与小说」的讲座整理而成。 性别这个极具争议的话题是很难谈的,但我认为她讲得很好,有客观的事实,有主观的判断,不说服谁,只是表达自己的观点。 「女性与小说」这个话题听起来和「房间」好像没啥关系,实际上这就是弗吉尼亚想要表达的主旨:一个女人想要写小说,就必须拥有两样东西:金钱和一间自己的房间。 金钱和房间决定了物质和空间的自由,进而决定了心智的自由,进而决定了小说(文学)的诞生。 她希望,无论通过什么办法,挣到足够的钱,去旅行,去闲着,去思考世界的过去和未来,去看书做梦,去街角闲逛,让思绪的钓线深深沉入街流之中…… 最后,不禁感慨,还有…
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# 读记|二〇二六・一:纳瓦尔、孟岩、刘震云等 好资产 + 好价格 + 长期持有 👉 https://quaily.com/rambling/p/read-202601?utm_source=telegram&utm_medium=social&utm_campaign=newsletter
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214/N #魔幻现实 朋友前几天给我发了一篇文章,介绍书籍的,说感觉是我会喜欢的书,我看了下,是刘震云的《温故一九四二》,讲河南大饥荒的。我回复:太惨,不看。 今天看完了,的确很惨。所谓的亲情、道德观念,在饥饿面前都将被抛弃——买卖子女,甚至人相食,「一个母亲把她两岁的孩子煮吃了;一个父亲为了自己活命,把他两个孩子勒死,然后将肉煮吃了」…… 我长大一些了才逐渐理解爷辈他们的节俭,他们经历过的时代,当下的我们无法感同身受。 但物资的缺乏,被官僚掐住脖子的体验,我们这几年倒是「有幸」体验过。谁也不会忘记武汉封城、上海封城时那些夜里的哭喊声吧?法治更加健全的一线城市都如此,其他地方的瞒报、一刀切的情况想必也不少。
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# 读记|二〇二五・十~十二:刘未鹏、李笑来、投资等 做山里的投资者 👉 https://quaily.com/rambling/p/read-202512?utm_source=telegram&utm_medium=social&utm_campaign=newsletter
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213/N #顺风车故事 国庆接了一单顺风车,一个七〇后大叔,他回老家,于是闲聊起来。 他说每个月都回去,我惊讶问为什么,他说在建房子,国庆要浇筑封顶了,我说现在建,一般是建别墅吧,他回答是的。 于是,顺着这个话题展开,他说包给别人做,一平方六百五十块,一层一百四十平方左右,现在三层做好框架,三十万投进去了。地基还是自己搞的,算上拆旧房子,填地基,花了十二万。这么一算,四十多万已经进去了。 他没想到要花这么多,还以为十多万就能起个大概了。我笑着说,现在人工物价不一样了,只要动工,没几十万是做不起来的。 他在广州三十年了,为啥还要在老家起房子呢?儿女都在外面成家立业,村里都没人住,何必掏这个钱?他说儿子们和我是一样的疑问。他的回答是,再过几年要退休了,老婆不习惯住城里,觉得孤独,说不上话,还是村里开心,干脆起个房子。我猜,是老一辈落叶归根的思想,也是为了面子,不然何必起那么大一个房子。 他顺风车是儿子帮订的,我估计是买…
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# 读记|二〇二五・九:陀思妥耶夫斯基、白先勇 阅读不是目标,也不是作业,不用非得读多少的 👉 https://quaily.com/rambling/p/read-202509?utm_source=telegram&utm_medium=social&utm_campaign=newsletter
212/N #观影 #昆汀塔伦蒂诺 最近又看了一遍昆汀塔伦蒂诺的经典电影《低俗小说》,相比多年前,这次多了许多新感受。 比如文森特这个角色,可以聊很多。当年,印象最深的是,文森特作为前半场出场最多的、以为是主角的人,中途竟然被枪打死了?这种不按套路出牌的情节简直让我干瞪眼。当时觉得文森特很酷,抽烟、和米娅的对舞、飙车……但现在不觉得了,甚至可以说,这个角色在我内心的形象彻底崩塌。 他的死,可以说早有伏笔——他就是这样一个无脑的、不称职的、沉迷低级趣味且没有边界的人。可以从好几处体现:第一个场景,去拿手提箱时,没有检查厕所是否有人,差点导致自己和朱尔斯被枪杀;海洛因随意放口袋,差点害死了米娅;米娅吸食过量,竟然在电话里说这个事情,不怕被监视;和朱尔斯争论神迹,争论不过后,在车上逼迫马文表达观点,竟然用手枪指着马文,而且没开保险,甚至食指已经扣住扳机,最后一个颠簸错把马文爆头了;把朱尔斯的车爆得全是脑浆,去了吉米家处理,朱尔斯…
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211/N #陀思妥耶夫斯基 从某种意义上说,娜斯塔霞和金庸笔下的周芷若有许多相似之处。类似的出身,同样渴望身份认同,明教教主张无忌之于周芷若,如同公爵梅诗金之于娜斯塔霞。 不同的是,周芷若为了成为教主夫人不择手段,而娜斯塔霞则在高贵与自卑里反复挣扎,变成他人眼中的「疯子」。 她们俩其实都没有获得真正的爱,张无忌对周芷若更多的是感激和敬畏,公爵对娜斯塔霞则是怜悯。 本书有个情节印象深刻,那就是阿格拉雅发起的那场对决——娜斯塔霞让公爵选择走向她或者阿格拉雅——阿格拉雅是娜斯塔霞渴望的另一面,她出生于将军之家,美丽,聪明,善良。 公爵的犹豫,让已经与他订婚的阿格拉雅脸面尽失,她捂着脸夺门而出。 这一幕让我想起我非常喜欢的菲茨杰拉德的《了不起的盖茨比》,盖茨比向汤姆摊牌与黛西的感情,而黛西不敢踏出这一步,最后她也是夺门而出。 公爵想要救赎娜斯塔霞的心让他产生了犹豫,黛西不愿舍弃上流社会的地位让她不敢直面汤姆的逼问。发出对…
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210/N #陀思妥耶夫斯基 #救赎 读完了《白痴》,荣如德译本,陆陆续续读了将近二十小时,从上个月到这个月。 这本书整体给我的感觉,没有《罪与罚》来得强烈。里面的梅诗金公爵,圣母般的人物,至善至美,他希望救赎的两个人——出身低微,聪明,高傲,美丽的娜斯塔霞,以及陷入娜斯塔霞的罗果仁,最终都没有救赎成功,甚至促使了两人的灭亡。 娜斯塔霞渴望摆脱「低贱」的身份,当善良而没有任何歧视的公爵出现时,仿佛一道光照向她,但她的高傲与自卑,又让她无法真正接受公爵——似乎,她是怕玷污了公爵的名声。 于是,娜斯塔霞在公爵、罗果仁之间反复挣扎。为了摆脱自己的「低贱」,选择与一掷千金的罗果仁结婚,但又在结婚时逃跑;最后,她意识到所爱之人是公爵,同时,公爵为了救赎陷入「癫狂」的她,选择与她结婚。然而,她又选择在结婚的当天与罗果仁逃走…… 当公爵寻到罗果仁家,在罗果仁暗黑的屋子里,看到床上冰冷的娜斯塔霞,听着罗果仁是如何用尖刀刺进她的胸口,他…
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209/N #陀思妥耶夫斯基 #翻译 近期在读陀思妥耶夫斯基的《白痴》,荣如德译本,有个翻译很不舒服,书中有个叫Щ公爵的人,我的第一反应——"Щ"是什么鬼?查了下才知道是俄文名字常用的字母,读音介于"sh" 和"ch" 之间,翻了其他译本,耿济之翻译为「施公爵」,臧仲伦翻译为「希公爵」,遵从音译的原则。 我觉得荣如德对这个人物名字的翻译方式是很糟糕的,翻译的目的是让读者能够使用母语理解他国作家的作品,如果作品翻译过来还是让人摸不着头脑,那算什么好的翻译呢? 试想一下,如果读者想要朗读含Щ公爵的段落,是不是得去查一下怎么读"Щ",这个字代表什么意思,这对读者的要求未免太高。
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Showing the 12 most recent of 20 posts we hold for @what_to_read_today. 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.
Citation-graph rank — 656,425 of 1,481,217entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
Named by 1 registered channel — 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.
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.
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 14 August 2026 — this entry's latest reading, not the date you are reading this.
“随机漫谈” (@what_to_read_today), 3,063 subscribers as measured 14 August 2026. Telegram Register, tgregister.com/channel/what_to_read_today.
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.