GitHubDaily engagement report
@GitHub_Daily - 90K followers on X
Measured over 40 original posts from a 30-day window, last computed on October 3, 2026.
Engagement
A typical post picks up 70 interactions against 90K followers, an engagement rate of 0.079%. Measured over 40 original posts, its engagement rate beats 47% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 6.3K times each, and 1.10% of those impressions turn into an interaction. That is about 6.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 00:00 UTC, and Saturday is the busiest day of the week. Of the 40 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 946 interactions, about 14x its own typical post.
Measured over 40 original posts from a 30-day window, last computed on October 3, 2026.
Compared with accounts its own size
GitHubDaily's engagement rate beats 47% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 7 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 43% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.079%, GitHubDaily sits above the 25th percentile of the 212,656 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.697% |
| 90th percentile | 2.61% |
| 99th percentile | 82.9% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 00:00 UTC, and Saturday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 100% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 414 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Sep 30, 202614x their median
自己做的网站上线好几个月，Google 上就是搜不到，找人做 SEO 诊断要花钱，自己对着官方文档一条条查，又不知道先改哪个。 jev-seo 是一个开源的网站 SEO 体检工具，给它一个首页网址，大约一分钟把整个站爬一遍，出一份排好优先级的整改清单。 它对照 Google 官方搜索文档里的 52 条规则逐项检查，死链、重定向、结构化数据、页面加载体验这些都会查到。 每个页面还会交给 TypeSafe 的 Jev 模型判断，这页是干什么的、内容够不够具体、有没有几页在抢同一个搜索词。 GitHub：https://t.co/fYaPWm5VJ1 报告一次出 PDF、Excel 和 Markdown 三份，PDF 给客户看，Excel 里每条整改项都带优先级、改法和官方出处，直接能当进度表用。 费用挺低的，标准模式一个站大约花 1 美分的模型调用费，不用订 SEO 数据服务。作者还拿同样 59 个页面跑了两遍，打分平均只差 0.03。 能在命令行跑，也能装进 Claude Code 当 Skill 用。给客户建站的，交付时附一份这个报告，比口头说一句「SEO 做好了」有说服力。
- Oct 3, 202611x their median
学 C 语言学到指针还行，一碰到进程、线程、锁这些操作系统的东西，课本讲得干巴巴，网上资料又东一块西一块。 伊利诺伊大学的系统编程课 CS 341，把自己用的课本开源了，就叫 Coursebook。 内容从 C 语言入门讲起，一路讲到进程、内存分配、线程、同步与死锁、进程间通信、调度、网络、文件系统、信号和安全，代码全部用 C 写。 前身是一份公开的 wiki 教材，现在补上了引用、脚注和术语表，书后还附了复习题。 GitHub：https://t.co/VoXkeBKAy1 网页版可以直接在线读，也有 PDF 和 EPUB，下载下来放进阅读器里看。 先学过一门编程语言、对汇编有点了解再看会比较顺。正在学操作系统、或者想补补底层功底的朋友，拿它当教材挺合适。
- Aug 25, 202611x their median
自从 DeepSeek Harness 开源之后，我便一直在纠结要不要将其接入到自己的产品当中。 于是想调研一下，被我发现了一款非常好用的 AI 产品：Apodex 1.1。 地址：https://t.co/ZMZvW3utfS 与传统 Deep Research 工具有所不同，它不光整理资料出报告，还会动手执行任务，一路干到交付。 其配套的 Agent 执行框架 FrontierAgent 是开源的，无需安装 Docker，一条命令即可运行。 GitHub：https://t.co/8IeYB3lP6B 他们还开源了 Apodex 1.1 mini（35B）模型，官方评测里，专业、金融、科研几项都能摸到前沿系统同一档的水平。 模型权重：https://t.co/VF7JqkJLhV 正想做 Deep Research 产品的朋友，可以本地部署 mini 模型，套上 FrontierAgent 就能自己搭一个。 这次调研，我是直接在它的 Web 端上实测的，效果出乎意料的好。 在发送需求后，它立马拆分任务，创建不同角色的子 Agent，并分配任务开始执行。（如图 1） 右侧显示的任务看板全程可见，每个任务做到哪一步、哪个 Agent 在干活都清清楚楚。 最实用的是，它在执行任务过程中，我们还能随时给它补充信息或者需求。（如图 2） 我补充要求它与其他工具进行横向对比，并调研一下现在 dsh-plugin 插件生态如何。 还留意到在交付报告前，它还会进行数据、内容、来源等 116 项查证，给人增加不少信任。（如图 3） 最后完整的结论（如图 4），一句话总结：目前 DeepSeek Harness 迭代速度非常快，正式接入建议再等等，个人可以先装着玩玩。
- Sep 1, 20269.3x their median
想给朋友传个大文件，用微信文件传输有大小限制，还得绕道网盘上传下载一大圈，很麻烦。 最近 Tailscale 团队出了个 tailcat，把两台电脑直接连上的看家本事，单独拆分做了文件传输工具。 用起来很简单，一台电脑作为接收端，屏幕上会给出一串短代码，另一台输入这串代码就连上了，文件和代码就能直接发给对方。 GitHub：https://t.co/bd8vlKy6fe 连接先由官方服务器牵线，之后两台电脑就直接对传，不再绕任何第三方，传输全程是加密的。 碰到实在连不通的网络，才退回服务器帮忙中转，保证怎么都能传得动。 不用注册账号，不用管理员权限，也不改系统网络设置，就是个装上就能用的小工具。 还有个网页版，打开浏览器就能和对面互传文件和文字，Linux、Windows 也都有现成安装包。
- Aug 28, 20268.2x their median
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- Oct 2, 20267.8x their median
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- Oct 1, 20267.7x their median
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- Sep 27, 20267.3x their median
csapp-zh-markdown 把《深入理解计算机系统》第三版整理成了中文 Markdown，前言到第 12 章加附录都在，还上线了在线阅读网站。 原书 238 道练习题答案单独整理，每章页首页尾都有入口，做完直接点过去核对。 8 个实验的说明全部译成了中文，从位运算的 Data Lab 到网络编程的 Proxy Lab，每个目录还附了官方自学实验包。 GitHub：https://t.co/BJDhcXfSlM 网站有全文搜索、前后翻页和深色模式，手机上也能看。想从头读就打开整章正文，想查某一节就从小节目录进。 作者在 README 里说，AI 再方便，想把底层知识框架搭起来，还是得系统读完一本扎实的书，这点我同意。 正在学计算机系统、准备面试要补底层的，拿来当主力读本挺合适。
- Jan 1, 20266.8x their median
GitHubDaily 2025 年在 X 所分享的开源项目，已分类整理到 GitHub 上，大家可自行查找学习。 GitHub：https://t.co/TRMpOQpmWy 过去一年，感谢大家的陪伴，希望我们曾经分享过的项目，能对你有所帮助😘 https://t.co/HhM6f4GE7C
- Oct 3, 20266.2x their median
打开 DBeaver 要等 Java 慢慢启动，查个 Redis 又得换一个客户端，电脑里光数据库工具就装了好几个。 dbx 是一个只有 25 MB 的开源数据库客户端，从 MySQL、PostgreSQL、Redis、MongoDB 到达梦这些国产库，100 多种数据库都能连，已斩获 2.3 万多 Star！ 不用装 Java，也不内嵌浏览器内核，下载就能连。 选中一张表，用大白话说要查什么，内置的 AI 助手直接写出 SQL，执行前还会先做一遍安全检查。 Claude Code、Cursor 这些编码工具也能通过它查数据，直接用 dbx 里配好的连接，权限分只读、可改数据、可做高风险操作三档。 GitHub：https://t.co/zcByQY9osy Kafka、RabbitMQ 这类消息队列也有控制台，查完数据库顺手看消息。DBeaver、Navicat 的连接配置能直接导入，迁过来不用重新填。 macOS、Windows、Linux 都有安装包，也能用 Docker 部署给团队一起用。每天跟好几种数据库打交道的，装一个能少开好几个窗口。
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 70 interactions against 90K followers, an engagement rate of 0.079%. Measured over 40 original posts, its engagement rate beats 47% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 6.3K times each, and 1.10% of those impressions turn into an interaction. That is about 6.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.3 post a day over the last 30 days, though only 27% of days saw any activity at all. Most posts go out around 00:00 UTC, and Saturday is the busiest day of the week. Of the 40 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 946 interactions, about 14x its own typical post.
- What is GitHubDaily's engagement rate on X?
- GitHubDaily (@GitHub_Daily) has an engagement rate of 0.079%, based on the median interactions across 40 original posts from the last 30 days against 90,459 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.079%, GitHubDaily sits above the 25th percentile of the 212,656 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @GitHub_Daily have real engagement?
- Its engagement rate beats 47% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @GitHub_Daily post?
- Most posts go out around 00:00 UTC, and Saturday is its busiest day, at roughly 1.33 posts per day across the measured window.