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👍3
Signed mastergo

Channel
@weekly_books
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry
38,254subscribers
+210 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001134924499 |
|---|---|
| Type | Channel |
| 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。 |
| Created | 30 August 2017 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 5 November 2026 |
| Measurements held | 56 |
| Confirmed unchanged | 1 time, most recently 5 November 2026 |
| On Telegram | t.me/weekly_books |
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 the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 5 Nov 2026, 15:36 | 38,254 | +3 |
| 4 Nov 2026, 19:10 | 38,251 | +15 |
| 3 Nov 2026, 21:00 | 38,236 | -1 |
| 2 Nov 2026, 19:49 | 38,237 | +5 |
| 1 Nov 2026, 22:00 | 38,232 | +3 |
| 31 Oct 2026, 18:48 | 38,229 | +8 |
| 30 Oct 2026, 18:08 | 38,221 | +4 |
| 29 Oct 2026, 16:56 | 38,217 | +2 |
| 28 Oct 2026, 15:56 | 38,215 | +12 |
| 27 Oct 2026, 14:17 | 38,203 | +9 |
| 26 Oct 2026, 17:17 | 38,194 | +4 |
| 25 Oct 2026, 19:30 | 38,190 | +17 |
| 24 Oct 2026, 18:56 | 38,173 | +6 |
| 23 Oct 2026, 21:06 | 38,167 | -5 |
| 23 Oct 2026, 00:17 | 38,172 | -8 |
| 21 Oct 2026, 22:59 | 38,180 | +7 |
| 20 Oct 2026, 16:55 | 38,173 | -5 |
| 19 Oct 2026, 14:46 | 38,178 | +4 |
| 18 Oct 2026, 12:29 | 38,174 | +1 |
| 16 Oct 2026, 14:27 | 38,173 | first reading |
87 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 172 pages of 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. It is computed over the 26 of 29 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 6 November 2026 |
|---|---|
| Posts held | 87 (4 July 2026 – 6 November 2026) |
| Views total | 133,490 |
| Reactions total | 505 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Nov 2026, 10:50 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.
Lifetime counters from Telegram’s own channel header, read 6 November 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.
Measured directly from 3 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.
1,727 reactions across 80 posts, in 32 distinct kinds. The most used accounts for 36.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 632 | 36.6% | |
| 👍 | 381 | 22.1% | |
| 🤡 | 110 | 6.37% | |
| 😡 | 93 | 5.39% | |
| 🖕 | 67 | 3.88% | |
| 💩 | 61 | 3.53% | |
| 🍌 | 60 | 3.47% | |
| 👎 | 57 | 3.30% | |
| 😈 | 52 | 3.01% | |
| 👏 | 42 | 2.43% | |
| 🥱 | 39 | 2.26% | |
| 🌚 | 22 | 1.27% | |
| 😭 | 22 | 1.27% | |
| 😁 | 19 | 1.10% | |
| 🥰 | 17 | 0.984% | |
| 🙈 | 8 | 0.463% | |
| 💋 | 7 | 0.405% | |
| 🔥 | 4 | 0.232% | |
| 😱 | 4 | 0.232% | |
| 🤔 | 4 | 0.232% | |
| 12 further kinds | 26 | 1.51% |
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 81 of the 87 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 1,727 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 87 most recent posts we hold, published 4 July 2026 to 6 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.
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 87 most recent posts we hold for this entry, published 4 July 2026 to 6 November 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.
Telegram’s own ‘edited’ marker: 6 of 30 posts held from the last 30 days (20.0%) 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).
Photo, posted without a caption
👍3
Signed mastergo
这个项目好 https://answer-me-with-html.com/zh/
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Signed mastergo
硅谷大跃进:人有多大胆,卡有多大产!🌚 AI 泡沫,是由特朗普错误发动,被黄仁勋、达里奥、奥特曼、马斯克等人错误利用的一场轰轰烈烈的「大炼模型」运动。 由于对通用人工智能建设缺乏经验,急于求成,片面夸大 Scaling Law 的作用,没有经过充分的商业化验证,以高估值、刷跑分、放卫星为主要标志的右倾盲动主义,开始在硅谷严重泛滥。 一时间,各路资本纷纷成立「万卡公社」,大搞算力大跃进,妄图依靠「人有多大胆,卡有多大产」的革命热情,在短时间内跑步进入 AGI 社会。 今天宣布百万 GPU 集群,明天放出万亿美元投资计划,后天又说超级智能只差最后一座核电站。 各大科技企业层层加码,砸锅卖铁囤积 B300,大搞「土法炼模」。数据中心遍地开花,电力指标层层摊派,资本市场捷报频传。 谁质疑 Scaling Law,谁就是技术保守派;谁不相信 AGI 明年到来,谁就是阻碍生产力发展的反动学术权威。 然而,模型跑分年年翻番,商业…
🙈8😁6👎3🤪1
Signed mastergo
今天三件事,串起来看挺有意思 🤔 1️⃣ 民主党横扫两院,道指不跌反涨 1500 多点,油价跌了 8% 多。摩根大通统计,1950 年以来中期选举后 12 个月,标普全是正收益。担心的事往往已经 price in 了。 2️⃣ Grok Bot 会替人打电话砍账单了($84 砍到 $45),但额度几天就烧光 🤡 少拉群聊、定时任务只在工作日跑,能省不少。 3️⃣ Claude Code 的 PM Cat Wu 去了 OpenAI,Anthropic 正在冲 IPO。
Signed mastergo
今天三件事,一条说完 👇 1️⃣ 美股:民主党「横扫」概率冲到 91%,期货跌了快 1%。但 AMD 营收 133.8 亿美元(+45%),Q4 指引 170 亿,盘后最多 +9%。选举是一次性冲击,AI 算力业绩才是主线。 2️⃣ 安全:币安 63 亿美元被盗,起点只是一场假面试加一个「面试作业」代码仓库。陌生代码一律进虚拟机跑,令牌别放日常电脑上。 3️⃣ 省钱:99 刀的苹果开发者账号送了 Xcode Cloud、CloudKit 和端侧大模型,小企业计划还能把抽成从 30% 降到 15%。
Signed mastergo
🤔 苹果财报超预期,盘后还是跌了;B300 租价一个月从 $7.9 掉到 $4.6。AI 是不是要崩? 我捋了一下,结论:还没崩,但钱的流向在变👇 1️⃣ 算力:$4.6 已经贴着成本线,更像新卡集中交付后的短期过剩。公开指数过去一个月 B300 其实还涨了 10%。我现在看 AI 需求,只看三样有人真掏钱的数据:GPU 租价、云厂商 capex、云收入。 2️⃣ 苹果:营收 $114.48B,iPhone 和服务都是新高,但大中华区只有 $17.54B。估值太贵,好消息也扛不住 🤡 今晚 AMD 出财报,美国又是中期选举,我不追跌也不抄底。 3️⃣ token:算力便宜下来,最先受益的是咱们写代码的。DeepSeek V4.1 Flash 好几个地方能免费用,美团 LongCat-2.5 新用户送 1000 万 token。简单活交给便宜模型,难的再上 Opus。 4️⃣ 用法:Open Duet 开源了「AI 同事…
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Signed mastergo
🤔 这周你最该先守住的是什么? 1️⃣ 服务器:Linux 内核漏洞 CVE-2026-98432,CVSS 9.8,远程不用认证就能打。截至 11/1,Ubuntu 和 RHEL 的补丁还没推全,今晚花 10 分钟 uname -r 查一下。 2️⃣ 仓位:美股科技一周蒸发 3 万亿美元,苹果今晚交卷,对冲基金在加速去杠杆。不抄底,也不割肉,先看指引。 3️⃣ 数据:yetone 开源了 Alma 中继服务器,Cloudflare Workers 免费部署,端到端加密,远程用 AI 不用把数据交给别人。 AI 跑得越快,越要自己守好家门 🤡
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Signed mastergo
武汉超级大环线 🤣
❤9👎1
Signed mastergo
DeepSeek V4.1 Flash,真是越来越香了 最近对比了四家平台,发现各有各的路数。 OpenCode Go,10 美元给 100 美元额度,诚意很足,但高峰期卡顿、429 比较多。 Command Code 也是 10 美元给 100 美元额度,虽然规则改来改去,但实际体验下来,我反而觉得可能比 OpenCode Go 更舒服一些。 OpenDesign Go,号称 120 美元额度,实际扣费没那么划算,规则也不够透明。 Ollama Pro,20 美元给 60 美元额度,最贵,但网友实测能跑到 202 tok/s,首字只要 0.3 秒,确实快。 我自己的 Monk 服务也接入了 DeepSeek V4.1 Flash 1017,因为价格便宜,对速度倒没有那么苛刻。 最近还拿这个模型做了一批视频,效果相当不错,画面清晰、动画流畅,成本又低,非常适合工业化、批量化的视频生产。 最近在抖音做 AI 视频,…
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Signed mastergo
这周我发现,账户、Agent、模型额度,三样东西都在骗我。 1️⃣ 指数在骗我 标普还在高位附近,但据统计超过 60% 的成分股已经从高点跌了 20% 以上。指数没崩,个股早就熊了 🤡 这周有苹果、Palantir、AMD 的财报,还有中期选举和非农。美国切冬令时了,美股改成北京时间 22:30 开盘,仓位别满。 2️⃣ Agent 在骗我 前 Anthropic 的 Thariq 说的「coding agent 狂热」我也中过:开几个 Agent 同时跑,忙了一整天,晚上一看啥也没上线。现在我每天开工前只写一件「今天要交付什么」。 3️⃣ 额度在骗我 有人实测了 26 个模型:Kimi K3 约 3 分钟就烧完 5 小时额度,MiMo-V2.6-Flash 能撑 62 小时。官方额度看看就好,杂活交给 Flash,难题再上贵的。
🌚8👍8❤2💩1
Signed mastergo
如果中国只有1亿人,会挤在哪? 我把全国县级单位按人口密度排了一遍,一块块点亮,加到1亿为止。 最后是 95个市辖区,散在 34座城市,只占国土 0.093%。 第一不是深圳,是天津和平区:10平方公里,35.5万人。😂️️️️
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Signed mastergo
前额叶,你自由了。 🧠 全体地球人,都应该感谢字节跳动。 感谢它开发了抖音、TikTok、红果短剧这些伟大的产品。 它们用无穷无尽的短视频、恰到好处的情绪刺激,以及永远刷不完的信息流,成功帮助你拖垮了无数竞争对手。 别人不读书了,不思考了,不愿意做长期投入的事情了。注意力被切成碎片,耐心被一点点消磨,连安静地坐半个小时,都成了一种奢侈。 你原本应该非常高兴。 毕竟,竞争对手都在刷短视频,而你只需要保持一点点专注,就有机会跑赢很多人。 可惜,算法一视同仁。 你本来只是想进去观察一下对手是怎么堕落的,结果一抬头,两个小时过去了。 对手确实躺下了。 你也躺下了。🤡 不过,我最近又想到了一个更有意思的问题。 我们每天和 AI 对话,获得的那些即时反馈,又何尝不是另一种文字形式的短视频呢? 以前刷抖音,手指往上一划,就有新的刺激。 现在刷 AI,手指敲下一句话,就有新的答案。 而且 AI 还特别会提供情绪价值…
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Signed mastergo
Showing the 12 most recent of 87 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.
@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.
We recorded 4 posts on @weekly_books as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Named by 5 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.
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.
Read from Telegram’s recommendation API, most recently 31 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
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.
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 5 November 2026 — this entry's latest reading, not the date you are reading this.
“小声读书 🙈” (@weekly_books), 38,254 subscribers as measured 5 November 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.