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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

38,148subscribers

+104 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 2017measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live17 September 2026
Measurements held30
Confirmed unchanged1 time, most recently 17 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

38,03238,14838,0906 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 subscribers6 August 202617 September 2026
30 measurements spanning 42 days, net +104. 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 38,015–38,165 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 30
Measured (UTC)SubscribersChange
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, 17:1338,072+10
17 Aug 2026, 14:0438,062first reading

Engagement

48 posts held, back to 4 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 82 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
11.3%
avg views ÷ 38,148 subscribers
Avg views / post
4,320
18 posts measured
Reaction rate
0.284%
reactions ÷ views · ER floor
Posts in window
18
of 48 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 17 of 18 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 21 September 2026
Posts held48 (4 July 202621 September 2026)
Views total77,830
Reactions total213
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Sept 2026, 01:25 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
640
Videos
9
Links
968

Lifetime counters from Telegram’s own channel header, read 22 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

1,065 reactions across 45 posts, in 22 distinct kinds. The most used accounts for 36.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
38736.3%
👍14813.9%
😡938.73%
🖕676.29%
🍌605.63%
😈524.88%
💩514.79%
👎504.69%
🥱393.66%
👏353.29%
🤡353.29%
🌚141.31%
💋70.657%
😁40.376%
😱40.376%
🤔40.376%
🤣40.376%
🥰40.376%
🎉20.188%
😐20.188%
2 further kinds30.282%

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 45 of the 48 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 1,065 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 48 most recent posts we hold, published 4 July 2026 to 21 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 48 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 48 most recent posts we hold for this entry, published 4 July 2026 to 21 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

21 Sept 2026, 06:22 UTC≈1,600 views11 reactionsread 22 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

9🤡2

Signed mastergo

19 Sept 2026, 17:46 UTC≈2,580 views6 reactionsread 22 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👌1

Signed mastergo

18 Sept 2026, 09:29 UTC≈3,010 views7 reactionsread 22 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

👍61

Signed mastergo

18 Sept 2026, 06:18 UTC≈2,850 viewsread 22 September 2026
Photo

Jev 用上了,用来做 LLM 判定。

Signed mastergo

17 Sept 2026, 08:29 UTC≈4,600 views6 reactionsread 22 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≈3,450 views3 reactionsread 22 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≈3,630 views10 reactionsread 22 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)。

10

Signed mastergo

14 Sept 2026, 07:43 UTC≈3,830 views10 reactionsread 22 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👍21

Signed mastergo

14 Sept 2026, 02:36 UTC≈3,660 views9 reactionsread 22 September 2026

Homebrew 7.0.0 发布了。🍺 如果你平时在 Mac 上写代码、折腾 AI、装各种 CLI,Homebrew 大概率属于那种每天都在用,却很少专门打开看一眼的东西。 我自己的 Mac 更是离不开它。 Ghostty、各种 Agent CLI、开发环境、Cloudflare 工具……换一台新 Mac,我第一批装的软件里一定有 Homebrew。一句 brew install 下去,很多麻烦事就不用自己操心了。 所以这次 7.0,我还挺感兴趣。 最直接的变化就是:更快了。 brew install、brew upgrade、brew bundle 都进一步优化了并行处理,下载、准备、安装可以更充分地一起跑。 安全方面也补了不少东西。 比如新增 brew vulns,可以直接检查通过 Homebrew 安装的软件有没有已知漏洞,还维护了一套 advisory database,并接入 OSV。 另外一个挺有

9

Signed mastergo

13 Sept 2026, 13:41 UTC≈3,690 views3 reactionsread 22 September 2026
Photo

一键运行 (推荐): npx monk-pi

2👎1

Signed mastergo

12 Sept 2026, 13:56 UTC≈4,120 views4 reactionsread 22 September 2026
Photo

deepseek-v4.1-flash 已在 monk.party 的 monk-coding 模型中上线。

4

Signed mastergo

11 Sept 2026, 09:17 UTC≈4,360 views3 reactionsread 22 September 2026
Photo

可以去这个页面查询用量和规则事情 https://monk.party/account/

👍2🎉1

Signed mastergo

Showing the 12 most recent of 48 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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Iyouport
@iyouport · 27,837
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 · 99,840
Telegram ranks this channel #31 of 44 here — alongside 43 others — read 17 August 2026
极客分享
@geekshare · 64,669
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 · 306,586
Telegram ranks this channel #39 of 50 here — alongside 49 others — read 17 August 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 18 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 17 September 2026 — this entry's latest reading, not the date you are reading this.

“小声读书 🙈” (@weekly_books), 38,148 subscribers as measured 17 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.