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Channel

小声读书 🙈

@weekly_books

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry

38,219subscribers

+175 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001134924499
TypeChannel
Username@weekly_books
Description在喧嚣的 AI 浪潮中,我们选择回到最朴素的成长逻辑:通过阅读沉淀思考,通过思考驱动行动,最终在技术的折叠中,重塑个体的厚度。
Created30 August 2017 — measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live26 September 2026
Measurements held31
Confirmed unchanged1 time, most recently 26 September 2026
On Telegramt.me/weekly_books

Topic

Other / unclassifiable — 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 60% 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

38,03238,21938,125.56 August 2026 — 38,044 subscribers6 August 2026 — 38,048 subscribers8 August 2026 — 38,041 subscribers9 August 2026 — 38,044 subscribers10 August 2026 — 38,037 subscribers11 August 2026 — 38,033 subscribers12 August 2026 — 38,032 subscribers13 August 2026 — 38,035 subscribers14 August 2026 — 38,041 subscribers16 August 2026 — 38,053 subscribers17 August 2026 — 38,062 subscribers18 August 2026 — 38,072 subscribers19 August 2026 — 38,083 subscribers20 August 2026 — 38,095 subscribers22 August 2026 — 38,098 subscribers23 August 2026 — 38,108 subscribers25 August 2026 — 38,109 subscribers26 August 2026 — 38,114 subscribers27 August 2026 — 38,118 subscribers28 August 2026 — 38,119 subscribers29 August 2026 — 38,127 subscribers31 August 2026 — 38,119 subscribers1 September 2026 — 38,116 subscribers2 September 2026 — 38,105 subscribers6 September 2026 — 38,110 subscribers9 September 2026 — 38,114 subscribers11 September 2026 — 38,120 subscribers13 September 2026 — 38,117 subscribers15 September 2026 — 38,113 subscribers17 September 2026 — 38,148 subscribers26 September 2026 — 38,219 subscribers6 August 202626 September 2026
31 measurements spanning 52 days, net +175. 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 38,004–38,247 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 31
Measured (UTC)SubscribersChange
26 Sept 2026, 22:2038,219+71
17 Sept 2026, 11:5838,148+35
15 Sept 2026, 08:1638,113-4
13 Sept 2026, 18:3938,117-3
11 Sept 2026, 21:1538,120+6
9 Sept 2026, 13:5938,114+4
6 Sept 2026, 09:4038,110+5
2 Sept 2026, 17:3638,105-11
1 Sept 2026, 19:2538,116-3
31 Aug 2026, 19:1538,119-8
29 Aug 2026, 14:3838,127+8
28 Aug 2026, 12:2638,119+1
27 Aug 2026, 09:0638,118+4
26 Aug 2026, 06:4838,114+5
25 Aug 2026, 03:5238,109+1
23 Aug 2026, 23:5738,108+10
22 Aug 2026, 09:1638,098+3
20 Aug 2026, 22:1138,095+12
19 Aug 2026, 19:2738,083+11
18 Aug 2026, 17:1338,072first reading

Engagement

52 posts held, back to 4 July 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 94 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
12.0%
avg views ÷ 38,219 subscribers
Avg views / post
4,580
21 posts measured
Reaction rate
0.297%
reactions ÷ views · ER floor
Posts in window
21
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.

What these figures were computed from
WindowRolling 30 days · latest post in window 27 September 2026
Posts held52 (4 July 2026 – 27 September 2026)
Views total96,257
Reactions total286
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken28 Sept 2026, 02:51 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
641
Videos
9
Links
969

Lifetime counters from Telegram’s own channel header, read 28 September 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

1,145 reactions across 50 posts, in 25 distinct kinds. The most used accounts for 36.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤42136.8%
👍18616.2%
😡938.12%
🖕675.85%
🍌605.24%
😈524.54%
👎514.45%
💩514.45%
🥱393.41%
👏353.06%
🤡353.06%
🌚141.22%
💋70.611%
🔥40.349%
😁40.349%
😱40.349%
🤔40.349%
🤣40.349%
🥰40.349%
🎉20.175%
5 further kinds80.699%

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 50 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 1,145 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 4 July 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.

Telegram Stars

Stars received
11
across the posts below
Posts paid on
2
of 52 we hold a reading for · 4%
Most on one post
10
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @weekly_books. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 52 most recent posts we hold for this entry, published 4 July 2026 to 27 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

27 Sept 2026, 15:56 UTC947 views15 reactionsread 28 September 2026

以前总觉得,一个地方“四季分明”是一件很美好的事。 春天看花,夏天听雨,秋天看落叶,冬天看雪。 但后来越来越觉得,四季分明更像一种审美优势,不一定是居住优势。 真正决定一个地方宜不宜居的,其实是: 一年有多少天,你愿意主动走出家门。 很多四季特别分明的城市,代价也很明显。 冬天接近 0℃甚至零下,夏天又能冲到 35℃以上。春秋虽然舒服,可往往短得可怜。 一年下来,几个月靠暖气,几个月靠空调,换季还要面对干燥、花粉、湿冷、暴晒、冰雪天气。 所谓“四季分明”,换个角度看,其实就是温差够大。 真正舒服的气候,反而往往没那么戏剧化。 冬天不用硬扛冷,夏天不用硬扛热,大多数时候穿件薄外套就能出门,这才是真正影响生活质量的东西。 所以如果只看中国,我反而会优先看两类地方: 一类是低纬度 + 高海拔。 最典型就是云南。 昆明、大理、腾冲、西昌这类地方,纬度低,所以冬天不会太冷;海拔又够高,把夏天的炎热压了下去。 …

👍15

Signed mastergo

25 Sept 2026, 12:18 UTC≈2,520 views26 reactionsread 28 September 2026
Photo

中秋快乐🎑

❤17👍8😭1

Signed mastergo

24 Sept 2026, 04:50 UTC≈3,560 views19 reactionsread 28 September 2026
File

File, posted without a caption

👍13🔥4❤1👎1

Signed mastergo

22 Sept 2026, 07:34 UTC≈4,560 views2 reactionsread 28 September 2026

这个好玩 https://gordensun.github.io/little-critters/

❤2

Signed mastergo

21 Sept 2026, 06:22 UTC≈4,690 views18 reactionsread 28 September 2026

周末去 Apple Store 摸了一圈 iPhone 18 系列。 本来没准备换手机,结果 iPhone 18 Pro Max 的手感意外不错,刘海也明显小了一圈。A20 Pro 性能提升很大,只是日常用起来,我确实感知不到多少。😂 这颗芯片很大一部分价值还是端侧 AI。对于暂时用不上完整 Apple Intelligence 的国行用户来说,多少有点英雄无用武之地。 而且今年有个很搞笑的地方: iPhone 18 居然还能套 iPhone 17 的手机壳。 保护壳一戴,隔壁老王看半天可能都不知道你换手机了。😂 真正在意辨识度,我反而更期待 10 月份的 iPhone Duo。那个造型一眼就能看出来是新东西,也更好玩。 至于怎么买,我今年的思路依然很明确:澳门。 主要考虑三个东西:eSIM、Apple Intelligence 和价格。 国行是双实体 Nano-SIM;港澳版则更适合有 eSIM 需求的人。对…

❤16🤡2

Signed mastergo

19 Sept 2026, 17:46 UTC≈4,570 views7 reactionsread 28 September 2026

我使用在 cloufdflare 上搭建了一个邮箱服务 https://mail.monk.party ,支持 monk.party 和 ssggo.net 后缀邮箱注册,同时支持绑定 GitHub 找回。 用处多多。

❤5👌2

Signed mastergo

18 Sept 2026, 09:29 UTC≈4,870 views10 reactionsread 28 September 2026

Jev 火了:AI 支出或砍掉 60% Jev 怎么用呢?🤔 问题一旦提出来,就开始研究,发现了好多新东西,探索的乐趣啊!🏄 拿到 API 后研究了一圈,发现大家已经把 Jev 玩出花了。给同样拿到 API、但还不知道怎么玩的人,整理一份 Jev 使用清单: 0. fast-jev-compaction 给 Claude Code 做上下文压缩,用精准裁剪替代默认的摘要压缩。 1. jev-ultrafast Browser Use 做的高速浏览器 Agent,让 Jev 判断下一步做什么、点哪个元素。Google Flights 搜航班完整跑完约 7.1 秒。 2. typesafe-mcp 把 Jev 接进 Claude Code、Claude Desktop 和 Codex,随时做 Choice / Score 这类结构化判断。 3. jev-mcp 封装事实核验、Prompt Injection 检测和语义…

👍7❤3

Signed mastergo

18 Sept 2026, 06:18 UTC≈3,800 views1 reactionsread 28 September 2026
Photo

Jev 用上了,用来做 LLM 判定。

🙏1

Signed mastergo

17 Sept 2026, 08:29 UTC≈5,820 views6 reactionsread 28 September 2026
Photo

最新的 stealth/union-alpha 模型已经在 monk.party 中的 monk-coding 中上线。 参数详见 https://openrouter.ai/stealth/union-alpha#providers 说明一下:monk-coding 本身是一个基于以 deepseek 4.1 flash 和 gemin 3.8 flash 为主和其它各类 beta 模型为辅的融合模型,一次请求会生产多次上游请求,然后取质量最好的结果返回,这个过程中会有 RTK 在其中做优化。(详见封面图) 还使用到的技术有 👇 HeadRoom 本地上下文压缩与优化层 https://github.com/headroomlabs-ai/headroom CaveMan 让 Agent 像穴居人一样说话,精简输入输出 https://github.com/JuliusBrussee/caveman PonyTai…

❤5🤔1

Signed mastergo

15 Sept 2026, 22:57 UTC≈4,270 views3 reactionsread 28 September 2026
Photo

专为 monk.party 打造的 macOS 极致体验菜单栏用量与限流监控小工具。纯原生 Swift + SwiftUI 打造。 - 🌐 GitHub 仓库: 👉 https://github.com/yaoleifly/monk-bar - 📦 Releases 发布页: 👉 https://github.com/yaoleifly/monk-bar/releases

❤3

Signed mastergo

15 Sept 2026, 14:06 UTC≈4,430 views11 reactionsread 28 September 2026
Photo

上海人工智能实验室 Atria 团队发布 Atria Dawn Preview,一款面向科学研究、工程开发与专业工作流的智能体基础模型。 该模型基于 7440 亿参数 MoE GLM-5.2 基座模型构建,支持 256K 上下文,重点解决开放任务中的持续环境理解、工具调用、多步骤执行与失败恢复。 其可验证经验流水线将任务目标、智能体轨迹、中间产物和外部证据连接起来,让任务完成从“生成答案”推进到“产出可执行、可验证、可复现的结果”。 在覆盖真实研究、工程和数字工作的 16 项基准测试中,Atria Dawn Preview 在 5 项取得已报告的最高分,另有 3 项位列第二。 官网 https://atria-asi.ai/ 这个模型已经接入了 https://monk.party 服务中,在模型列表中切换到 monk 模型即可使用。(不是 monk-fast 或 monk- coding)。

❤11

Signed mastergo

14 Sept 2026, 07:43 UTC≈4,590 views10 reactionsread 28 September 2026
Photo

下午盘算了一下,手头的 Token 有点用不完了,增加额度给大家。使劲蹬吧。👇 https://monk.party

💋7👍2❤1

Signed mastergo

Showing the 12 most recent of 52 posts we hold for @weekly_books. 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 edited after publishing

@weekly_books edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
27 August 2026
Most recent edit
27 August 2026

Forward network

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.

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.

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.

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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.

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@AppDoDo · 40,725
Telegram ranks this channel #11 of 57 here — alongside 56 others — read 30 August 2026
中国数字时代
@cdtchinesefeed · 44,158
Telegram ranks this channel #17 of 60 here — alongside 59 others — read 29 August 2026
Newlearnerの自留地
@NewlearnerChannel · 67,563
Telegram ranks this channel #17 of 62 here — alongside 61 others — read 21 August 2026
黑洞资源笔记
@piracy6 · 77,423
Telegram ranks this channel #18 of 61 here — alongside 60 others — read 19 August 2026
科技&趣闻&杂记
@kejiqu · 36,473
Telegram ranks this channel #21 of 48 here — alongside 47 others — read 1 September 2026
 Apple Nuts
@AppleNuts · 37,006
Telegram ranks this channel #24 of 56 here — alongside 55 others — read 1 September 2026
Iyouport
@iyouport · 27,814
Telegram ranks this channel #25 of 55 here — alongside 54 others — read 10 September 2026
Widget🏂软件工具资源分享
@WidgetChannel · 100,285
Telegram ranks this channel #31 of 44 here — alongside 43 others — read 17 August 2026
极客分享
@geekshare · 64,804
Telegram ranks this channel #32 of 44 here — alongside 43 others — read 21 August 2026
油油の科技软件资源分享
@youyousharechannel · 90,145
Telegram ranks this channel #38 of 43 here — alongside 42 others — read 17 August 2026
ahhhhfs|A姐分享
@abskoop · 306,126
Telegram ranks this channel #39 of 50 here — alongside 49 others — read 17 August 2026
Time经济观察
@TimeHorizonX · 20,875
Telegram ranks this channel #48 of 54 here — alongside 53 others — read 25 September 2026
看鉴中国 OutsightChina
@OutsightChina · 41,309
Telegram ranks this channel #50 of 58 here — alongside 57 others — read 29 August 2026

This channel appears in 20 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.

“小声读书 🙈” (@weekly_books), 38,219 subscribers as measured 26 September 2026. Telegram Register, tgregister.com/channel/weekly_books.

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.