部署过程参考Blog:LXC 搭建 K8s 集群计划通 #Linux #LXC #k8s
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Channel
@CyunreiNotebook
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
8subscribers
+0 since we began measuring on 11 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003511162492 |
|---|---|
| Type | Channel |
| Username | @CyunreiNotebook |
| Description | 记录一些每天看到的好玩的东西 |
| Created | Between 1 December 2025 and 31 May 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 12 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/CyunreiNotebook |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 14:32 | 8 | no change |
| 11 Aug 2026, 10:15 | 8 | first reading |
20 posts held, back to 15 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 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. It is computed over the 4 of 20 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 12 August 2026 |
|---|---|
| Posts held | 20 (15 July 2026 – 12 August 2026) |
| Views total | 244 |
| Reactions total | 6 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 14:32 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 August 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.
6 reactions across 4 posts, in 3 distinct kinds. The most used accounts for 66.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 4 | 66.7% | |
| 😁 | 1 | 16.7% | |
| 🤡 | 1 | 16.7% |
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 4 of the 20 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 6reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 15 July 2026 to 12 August 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.
部署过程参考Blog:LXC 搭建 K8s 集群计划通 #Linux #LXC #k8s
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昨天给 LXC 配置 K8s 的时候发现了一条配置: lxc.cgroup2.devices.allow = a lxc.cgroup2.devices.deny = c *:* m lxc.cgroup2.devices.deny = b *:* m lxc.cgroup2.devices.deny = c 1:3 rwm 其中 1:3 一开始没太搞懂,查了一下才发现这是 Linux device number 的 major。 比如这里的: c 1:3 rwm 其中 c 表示 character device,1:3 就是 major=1、minor=3,rwm 分别表示 read/write/mknod 权限。 1:3 实际对应 /dev/null。可以通过 ls -l /dev/ 看到设备文件对应的 major。 之前只知道 /dev/nvme0n1p1 这种设备名,没意识到 Linux 内部还有一套独立的…
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Docker Sandboxes | Sandboxes for Coding Agents | Docker 基于Docker的Agent 沙盒运行环境 #Docker #Agent #AI
今天无意发现在Ghostty的About 页面点击图标竟然会切换,然后搜索了一下发现Ghostty的图标是可以自定义的。 Ghostty macOS Icons — Gallery & Custom Style Builder #TIL
RFC 2119: Key words for use in RFCs to Indicate Requirement Levels | RFC Editor RFC 2119定义了技术规范中常用关键词的精确含义,例如 MUST、MUST NOT、SHOULD、SHOULD NOT、MAY 等,用来区分强制要求、推荐实践和可选方案。很多工程沟通中的歧义,本质上来自“应该”“建议”“必须”这些词缺少统一语义。RFC 2119 提供了一套简单但非常有效的规范语言,让协议设计、架构文档和团队约定能够表达更清晰的约束等级,值得在编写技术规范和设计文档时借鉴。 #TIL #RFC #软件工程 #规范
竹知了:一转就 " 哇哇 " 叫的传统玩具,Web 模拟版。零依赖单文件,手机优先。 https://zhuzhiliao.imsai.cc https://github.com/imsai-sh/zhuzhiliao
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幽灵汉字 - 维基百科,自由的百科全书 说到日本编码这块,关于汉字也有一些好玩的,比如说幽灵汉字,出名的字有彁和妛。
为什么 Emoji 来自日本? Emoji 的诞生,纯粹是因为日本在90年代末苛刻的移动网络资费,以及日语有各种敬语,需要有节省字数的表达方式。 GPT 总结: Emoji 其实不是硅谷发明的,而是 90 年代末日本移动通信环境下诞生的产物。 在 Emoji 出现之前,日本网络已经流行 kaomoji(颜文字),比如 (^^)、(><)、m(_ _)m。它解决的是文字聊天缺少表情和语气的问题。 到了 1999 年,NTT DoCoMo 推出 i-mode 手机邮件时,发现手机屏幕小、输入麻烦,而且日语交流非常依赖语气和礼貌表达。于是工程师栗田穣崇设计了一套 12×12 像素的小图标,把天气、爱心、情绪、食物等常见表达直接变成字符,这就是 Emoji 的起点。 后来日本三大运营商 DoCoMo、KDDI、SoftBank 都各自做了一套 Emoji,但没有统一编码,导致跨运营商通信经常乱码。这个问题后来随着 iPhone…
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The Productivity Mirage / frantic.im 作者以前沉迷各种生产力工具,比如 Vim 配置、tmux、快捷键、自动化脚本,以为高手的效率来自这些东西。但后来发现一个很厉害的工程师,使用的工具非常普通,甚至没有很多所谓高级工作流,但产出依然很高。 原因是他的优势不在“怎么写代码”,而在“知道该写什么代码”。真正稀缺的是发现重要问题、判断什么值得做、理解用户需求,而不是把工具链折腾得多复杂。 这个观点放到 AI 上更明显。以前工程师的瓶颈可能是实现能力,现在 AI 大幅降低了实现成本,未来差距更多会体现在问题选择和判断能力上。 当然,这不是说工具不重要。工具更像乘数,而不是生产力本身。方向错了,再好的工具也只是让你更快地做错事。 #思考
Making 768 servers look like 1 — PlanetScale 介绍数据库规模增长后,如何通过 Router + Sharding 策略突破单库瓶颈,保持系统稳定运行。(顺便推销自家产品)动画做的很棒(Ben Dicken出品)。 #Database #PlanetScale #Postgres #MySQL #Visualization
How a Beam Engine Works — An Interactive Guide 横梁发动机(早期蒸汽机)原理介绍,3D交互做的好棒 #好玩的 #网站 #原理
今晚看了下GDG开发者社区的 “AI时代,有了Harness,还需要软件工程吗?” 的直播分享,顺便记了几点开发规范(经过AI文本润色)。 1. 可以尝试通过多 Agent 对项目实际使用体验进行反馈,例如采用开发者访谈的方式,从高质量 AI 用户的视角收集反馈,用于优化产品能力并明确产品边界。 2. 当前 AI Agent 在执行任务时仍可能存在一定程度的“偷懒”行为,例如减少代码细节、遗漏必要内容(如格式、括号等),或者尝试调用第三方 AI 工具完成任务。因此,即使 Agent 完成任务,也需要通过 Lint、测试等工程化手段进行验证。 3. 关于 Spec-Driven Development(SDD),小项目通常不需要引入完整 Spec 流程,大量规范文档可能增加维护负担;但企业级、大型项目需要通过 Spec 明确需求、约束和验收标准,降低 Agent 开发过程中的偏差。 4. 当前模型或多或少仍存在不确定行为,…
Showing the 12 most recent of 20 posts we hold for @CyunreiNotebook. 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 — 980,446 of 1,160,990entries 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.
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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“Cyunrei's Notebook” (@CyunreiNotebook), 8 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/CyunreiNotebook.
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.