Channel
@lingqibasui42
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
4subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002335345797 |
|---|---|
| Type | Channel |
| Username | @lingqibasui42 |
| Description | 随便什么都可以 |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 13 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/lingqibasui42 |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 07:47 | 4 | no change |
| 7 Aug 2026, 19:16 | 4 | first reading |
16 posts held, back to 12 August 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 1 pageof 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.
| Window | Rolling 30 days · latest post in window 13 August 2026 |
|---|---|
| Posts held | 16 (12 August 2026 – 13 August 2026) |
| Views total | 16 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 13 Aug 2026, 07:47 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 13 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
页的证明，3 个审稿人看半年都可能漏掉一个隐含错误。AI 生成的证明尤其难审——因为它逻辑太长、步骤太细、完全不符合人类的直觉思维。 这次 OpenAI 彻底换了打法：每道都附带了 Lean 4 机器验证证书。 Lean 是什么？你可以把它理解为一个“数学编译器”。你把证明写成 Lean 能读懂的格式，它会一步一步按公理严丝合缝地检查。 中间跳了一步？报错。 逻辑有个缝？报错。 它不关心证明者是人类还是 AI，也不关心思路漂不漂亮——你的证明步骤严不严密，编译器说了算。 通过就是铁案，不通过就是废纸。没有灰色地带，不需要权威背书。 249 页论文全部公开，10 个 Lean 证书全部开源在 GitHub，连 AI 的推理路径都完整放了出来。你拿一台普通笔记本，跑两行命令就能亲自验证： lake exe cache get && lake build All Thomas Bloom这次亲自下场评价："Big news。也许不比单…
别人用 AI 写周报，OpenAI 用 AI 解了 10 道数学世纪难。 8 月 1 日，OpenAI 发了一篇博客，说他们一个还没对外发布的内部模型——代号 Astra——在研发过程中顺手做了一件事： 解决了 10 道至少卡了 10 年以上的数学开放问。 不是“给出了一种可能的方向”，不是“缩小了搜索范围”。是真的解了，给出了完整证明，而且每道的证明都附了一份机器验证证书。 一、这 10 道，到底意味着什么？ 这 10 道横跨了 8 个不同的数学分支——群论、编码理论、量子计算复杂性、格密码（后量子密码学的基础）、极值图论、高维几何……不是在一个方向上猛钻，而是全面铺开。 挑几个说人： 非 sofic 群是否存在？ 1999 年，数学大神 Gromov 问了一个问：是否所有群都可以被“有限置换群”近似？（这就是“sofic”的意思。）27 年没人能回答。Astra 给出了一种具体的构造，证明了“不是所有群都 sofic”。 C…
我再多说一句，昨天直播时提了一句， 在全中国范围内，除了清华、北大、复旦、交大、浙大，剩下所有学校的本科“人工智能专业”，都是诈骗，都是李鬼专业。 简单来说，这个档次的大学，开不了LLM的课，开不了AI Agent的课，开不全mlsys的课， 就开始往里疯狂兑水，什么传统deep learning、 baysian ML、各种古典RL、各种模式识别、图像处理、信号处理、各种垃圾数学、上个世纪的古典AI、蒙特卡洛、随机森林、贪心算法、模糊控制、大数据，还有一堆数学里面的各种分析和代数几何， 全往本科生专业的培养计划里面添加，让孩子学一堆没用的1960~2020年的所谓“AI”专业课，真正有用的课一门都没有上， 结果就是，这种本科专业彻底把孩子全毁了。 记住，全中国只有清华、北大、复旦、上交、浙大能大概开出来，剩下的学校没有一所有能力、有师资、有水平、有时间把这些课程开出来，那么就可以认为全都是垃圾，不如老老实实学个计算机。 AI…
要是早点发现这个数据整理做图神器就好了！ 很多同学在写论文、做学术报告或整理实验数据时，都遇到过这样的烦恼：Excel 默认生成的图表样式生硬、标签容易重叠；而使用 Python（Matplotlib）或 R 语言画图，又需要花大量时间写代码调试格式、修改配色和坐标轴，效率往往被卡在最后的排版上。 如果你想快速做出符合学术出版和报告标准的高质感图表，推荐你使用 Datawrapper。 Datawrapper是一个无需安装软件、免代码的在线数据可视化工具，专门用于将常规表格数据转化为结构清晰、视觉优雅的图表、地图和数据表格。 核心功能与实用特性： 1️⃣ 零门槛导入：直接复制 Excel 或 CSV 数据粘贴到网页中，系统会自动识别列属性并推荐匹配的图表类型。 2️⃣ 丰富的可视化类型：支持折线图、柱状图、散点图、瀑布图等20多种基础与高级图表，同时包含高精度地图和带热力效果的综合数据表。 3️⃣ 智能化排版与标注：内置专业的图…
大学 | 原文
这两本就是逻辑学界的“双子星”。 《一本小小的蓝色逻辑书》——最通俗的逻辑入门，把思维漏洞、论证结构、语言陷阱讲得像聊天一样轻松。看完后你会突然发现：原来逻辑随手可用。 《小逻辑》——黑格尔的压轴之作，深到夸张，却强到震撼。 🔗：https://pan.quark.cn/s/9c4c3499bc5a @meiriyishu
微信能直接渲染 Markdown 文件了！ 刚刚，编辑部的小伙伴突然发现，微信开启了 Markdown 文件的预览。 如果你在任意聊天中发送一个 .md 文件，点开发现能直接看到正确渲染的内容，那么你已经收到灰测资格了 如果显示「暂不支持打开此类文件」，或者「用其他应用打开」，则为暂不支持。可以稍等片刻，我们有同事半小时前还不行，现在已经可以打开了。 目前我们发现，包括层级标题、加粗、斜体、有序/无序列表、表格等经典语法，都能直接打开并正常渲染。 另外，微信公众号后台也在前两周支持了 Markdown 语法，并且支持转换格式。 只是现阶段测试来看，公众号后台的格式转换效果比较初级，对于不同层级字体的字号、颜色的转换效果并不好看，需要后续手动调整。
#Word #文本替换 #开源 Bulk Text Replacement for Word Word 文档批量文本替换工具，可同时处理多个 DOCX 文件，能够安全替换正文、超链接、文本框、页眉、页脚等位置的内容，并提供 Diff 预览、实时匹配计数、多文件批量处理等功能，标准模式速度较快且可保留原有格式，高级模式则借助 Word COM 自动化实现更全面的替换能力，单文件免安装即可运行，免费开源。 🐙GitHub 频道 @WidgetChannel
Bento Slides 真正的动画、视频、实时图表和网页级设计，全都汇聚在一个 HTML 文件中——它既是文档，也是编辑器，同时还是播放器。把一份陈旧的 PowerPoint 丢进去，让 AI 为现代网页重新构建；然后在编辑器里完善细节。无需账号，无需云端，自动保存。 点击访问 #Markdown #PowerPoint #skill
研究发现：有了AI后，人们变得迷之自信 | 原文
被AI废掉的一代 | 原文
【拒绝框架崇拜：一份把 Agent 拆解到原子级的“源码小说”】| BLOG Antinomie Lab 推出的 pi-agent 源码导读本周正式上线。 这并非传统意义上的说明书，而是一份将 pi-agent 这种非框架、积木式 Agent 循环彻底拆解的技术手册。其最硬核之处在于每个技术论断都精准关联了源码行号，这种逐行溯源的写法极大提升了文档的可验证性。 针对目前开发者普遍存在的“LangChain 疲劳”，它的核心逻辑是：Agent 不该是沉重的框架套娃，而应是模块化的积木组合。 这种文档风格兼具了代码深度与阅读的轻快感，对于想从零构建智能体而非被黑盒框架束缚的开发者来说，这种回归代码本质的剖析是极其稀缺的养分。它不仅在讲透一个项目，更在传递一种构建 Agent 的底层思想：成为系统架构的建筑师，而非臃肿框架的搬运工。目前该书保持每周更新频率，正逐步放出从 pi 迁移到任何 Agent 的通用方法论。
Showing the 12 most recent of 16 posts we hold for @lingqibasui42. 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 — 1,251,831 of 1,480,944entries 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.
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 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.
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.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“零七八碎” (@lingqibasui42), 4 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/lingqibasui42.
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.