Anthropic 让 HTML 再次伟大。 lol.hanyue.io
❤2
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
38,252subscribers
+208 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 | 11 November 2026 |
| Measurements held | 60 |
| Confirmed unchanged | 1 time, most recently 11 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 |
|---|---|---|
| 11 Nov 2026, 03:20 | 38,252 | -10 |
| 9 Nov 2026, 09:36 | 38,262 | +1 |
| 7 Nov 2026, 22:56 | 38,261 | +16 |
| 6 Nov 2026, 19:20 | 38,245 | -9 |
| 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 | first reading |
96 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 184 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 33 of 35 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 12 November 2026 |
|---|---|
| Posts held | 96 (4 July 2026 – 12 November 2026) |
| Views total | 154,151 |
| Reactions total | 610 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Nov 2026, 11:09 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 12 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 4 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.
1,854 reactions across 88 posts, in 33 distinct kinds. The most used accounts for 36.3% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 673 | 36.3% | |
| 👍 | 416 | 22.4% | |
| 🤡 | 136 | 7.34% | |
| 😡 | 93 | 5.02% | |
| 👎 | 68 | 3.67% | |
| 🖕 | 67 | 3.61% | |
| 💩 | 66 | 3.56% | |
| 🍌 | 60 | 3.24% | |
| 😈 | 52 | 2.80% | |
| 👏 | 43 | 2.32% | |
| 🥱 | 39 | 2.10% | |
| 😭 | 23 | 1.24% | |
| 🌚 | 22 | 1.19% | |
| 😁 | 22 | 1.19% | |
| 🥰 | 17 | 0.917% | |
| 🙈 | 9 | 0.485% | |
| 💋 | 7 | 0.378% | |
| 🔥 | 4 | 0.216% | |
| 😱 | 4 | 0.216% | |
| 🤔 | 4 | 0.216% | |
| 13 further kinds | 29 | 1.56% |
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 90 of the 96 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 1,854 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 96 most recent posts we hold, published 4 July 2026 to 12 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 96 most recent posts we hold for this entry, published 4 July 2026 to 12 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 36 posts held from the last 30 days (16.7%) 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).
Anthropic 让 HTML 再次伟大。 lol.hanyue.io
❤2
Signed mastergo
一起来自习吧,最近要学习的东西太多了。 https://coffes.cn/study
Signed mastergo
Anthropic 路演太炸裂了 Dario 上来就放狠话:不到十年,癌症、阿尔茨海默病、心脏病等重大疾病,可能成为历史。 听着像科幻小说,但看看 Anthropic 交出的成绩单,确实有点吓人。 第三季度营收接近 170 亿美元,同比增长约 12 倍。 工程师人均代码量,是 2024 年的 45 倍! 人均营收从 140 万美元,干到了 660 万美元以上。 最离谱的是,超过一半的财富 500 强企业已经开始大规模采购 AI,但投入占总支出的比例还很低。 换句话说,这场游戏可能才刚刚开始。 我自己天天用 Opus 写代码、做产品、做视频,感受非常深。 Anthropic 和 Opus,真的在彻底颠覆生产力,也在重新定义智能的价格。 以前一个团队干几个月的活,现在一个人带着几个 Agent,可能几天就做完了。 而且 AI 不请假、不摸鱼、不闹情绪,半夜三点还在给你修 Bug。赛博牛马甚至不用交社保。🌚 以…
🤡22❤6
Signed mastergo
Claude 又送重置,这谁顶得住? Anthropic 这家公司,真是让人又爱又恨。 Fable 5.2 刚发布,编程能力又往上蹿了一截,结果 Claude 又开始送重置额度了。🤣 连续订阅的 Max、Ultra 用户,按时间送 1~3 次重置。连每月 20 美元的 Pro 用户,也能限时体验 Fable 5.2,还送一次重置。 这谁顶得住啊?本来准备睡觉的,结果又得起来干活了。 最近我一直用 Claude 做视频,体感越来越好了。 以前做视频,写脚本、做动画、剪辑,折腾得要死。现在直接把想法扔给 Claude,它就能写代码做动画、组织画面,甚至直接给你交付成片。 我自己做的视频,播放量已经破百万了。这个方向确实有点东西。🏄 另一边,字节也没闲着。 Seedream 5.5 发布了,主打像素级可控编辑。哪里不满意改哪里,上百轮修改还能保持稳定,甚至能直接输出透明图层。 价格便宜了 60%,速度还快了近 40…
👎8❤3
Signed mastergo
有时候很想活得像村上春树小说里的男主,普通但是品味良好,情绪淡淡但是经常被卷入突发事件,又出人意料得很容易进入异性关系。 然后我会遇到美丽的、古怪的、充满奇幻色彩但带着神秘创伤的女主们,像鹅卵石一样打破生活。 比如一位穿着红色风衣的女士在咖啡店拦下我,问我是否相信鸟类拥有前世记忆。接着我们开始约会,雀子会在凌晨四点打电话给我,问我是否在家附近见到一只衔着白头鹎的赤色狐狸。 在荒诞且幽默的剧情之后,她们毫无征兆地消失或是死亡。给我的心留下淡淡的伤痕。我已经不再是那个山田了。 原文 https://www.threads.com/share/GFfv6fZex
👍7🤡3👎2
Signed mastergo
实在太好玩啦,我感觉到快乐幸福和平静。🏄 https://monk.party/office/3d/
👍8❤4👾2🤡1
Signed mastergo
全网还在研究黄乐和黄仁勋的桃色新闻,讨论人家到底有多漂亮。 我倒觉得,大家可能搞错了重点。🤡 这位女士曾任职的公司,在不到两年时间里,采购了至少 48 亿美元的 AI 设备,其中包括大量英伟达高端芯片。 48 亿美元啊!别人研究黄仁勋的感情生活,人家研究怎么从黄仁勋手里拿货。🤣 很多普通人可能还没意识到,现在中国有多缺高端算力。 国产芯片正在追赶,但顶级 AI 算力依然是短板。有钱不一定买得到,买到了也不一定能用得好。 当年徐增平买瓦良格航母,光回国就折腾了两年多。如今高端 GPU 同样是战略资源,只不过航母停在港口,GPU 藏在机房。 当然,买到芯片和最终向中国提供多少算力,还是两回事。 至此,老黄也算在网友的段子里实现了某种地缘政治平衡: 本人来自台湾,太太是美国人,绯闻对象据传来自长沙。 一张家庭关系图,硬是被网友画出了国际关系的味道。🌚 不过玩笑归玩笑,这几十亿美元的算力生意,才是真正值得研究的事情…
❤11💩5
Signed mastergo
全是赛博牛马 OpenDesign Go 这波是真舍得给。🤣 DeepSeek V4.1 Flash Eco,首月只要 $8,却给了等价 $220 的月度使用额度。 于是乎,我给 monk.party 一口气接入了 5 个基础订阅账号。 结果发现,这玩意儿是真耐造啊! 上一轮额度还没用到一半,今早又重置了。🤡 这下好了,赛博牛马的饲料根本吃不完。 大家放开蹬,别替我省 Token。 🏄 另外,最近还干了一件很有意思的事。 我把整个 Monk API 服务的日常运营交给 Agent 了。 更好玩的是,我把这些 Agent 的工作状态全部可视化,搞了一个「AI 员工办公室」。 以前是我盯着服务器、看日志、处理问题,现在是一群赛博牛马在办公室里各司其职,我偶尔进去视察一下。🤣 这种感觉真的很奇妙,自己做的产品,慢慢开始自己照顾自己了。 大家有空也可以来我的 AI 办公室逛逛,看看什么叫 AI Native 的…
👍10
Signed mastergo
刚刚开始
❤6👏1
Signed mastergo
Photo, posted without a caption
👍5
Signed mastergo
这个项目好 https://answer-me-with-html.com/zh/
👍12❤1
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…
😁9🙈9👎4🤪2
Signed mastergo
Showing the 12 most recent of 96 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 11 November 2026 — this entry's latest reading, not the date you are reading this.
“小声读书 🙈” (@weekly_books), 38,252 subscribers as measured 11 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.