GitHubDaily engagement report
@GitHub_Daily - 84K followers on X
Measured over 60 original posts from a 30-day window, last computed on September 3, 2026.
Engagement
A typical post picks up 66 interactions against 84K followers, an engagement rate of 0.08%. Measured over 60 original posts, its engagement rate beats 45% of 6,874 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 5.5K times each, and 1.21% of those impressions turn into an interaction. That is about 6.56% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 10:00 UTC, and Sunday is the busiest day of the week. Of the 60 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 796 interactions, about 12x its own typical post.
Measured over 60 original posts from a 30-day window, last computed on September 3, 2026.
Compared with accounts its own size
GitHubDaily's engagement rate beats 45% of the tracked X accounts closest to it in follower count (6,874 accounts, accounts of similar size (decile 5 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 49% 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 0.08%, GitHubDaily sits above the 25th percentile of the 66,128 accounts in this comparison. That places it in the below the median band, which runs 0.016% to 0.1%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.002% |
| 25th percentile | 0.016% |
| 50th percentile | 0.1% |
| 75th percentile | 0.499% |
| 90th percentile | 2.09% |
| 99th percentile | 119.9% |
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 10:00 UTC, and Sunday 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 354 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
- Aug 25, 202612x 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, 20269.8x 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, 20268.7x their median
《AI Agents - The Definitive Guide》这本关于 Agent 开发指南的开源书籍,值得一看。 作者把配套代码整个开源了,12 章内容对应 35 个 notebook,按章节分好目录。 从思维链、思维树、ReAct 讲到多 Agent 分工协作,Agent 记忆、评测、成本核算这些生产阶段的主题也各有实操代码。 GitHub:https://t.co/UwYiOWgJ7A 每个 notebook 都带 Colab 链接,浏览器点开就能跑,本地什么环境都不用装。 翻目录时留意到,最后一章专门讲给 Agent 做威胁建模,这个主题在入门教程里少见。 正在做 Agent 开发的朋友可以按主题挑着学习,书没买也不影响把代码过一遍。
- Aug 31, 20266.1x their median
sepia 这个去 AI 味写作 Skill 真有点东西,值得看一下。 它先拿六万多篇小说作为对照实验,发现光看叙事结构就能认出 AI 写的,识别率 93.2%。 然后尝试将表面词句改得干干净净,但这个识别率几乎不降。 于是它就从结构层下手,主题别让叙述者讲出来、因果链松一松,共 30 项诊断。 GitHub:https://t.co/tt5fidkjUK Claude、ChatGPT、Gemini、DeepSeek、Kimi 各家模型的写作指纹,还有单独的修正清单。 写公文也有对应规则,发布公告、复盘报告、工单各配一套,按场合说话不端着。 提供写、诊断、小改、重写 4 种用法,Claude Code、Codex 都能装。前阵子分享过改词句的同类工具,这个更深一层。
- Aug 29, 20264.3x their median
semantica 给 AI 系统底下垫了一层图谱基础设施,号称 Agent 版的开源 Palantir,已斩获 11000+ Star! 它把企业数据抽成知识图谱,每条事实都带来源,每个决策都是能查的对象,为什么做、依据什么、影响了啥都能追。 GitHub:https://t.co/OLqCa8ZDV8 推理走的是规则引擎不靠大模型,结果可复现,遇到互相矛盾的事实会标出来,而不是悄悄用新的盖掉旧的。 Neo4j 在内的 8 种图数据库随便换,LangChain、CrewAI 能直接接,还带 MCP 服务,Claude 这类工具也能连上用。 比较适合金融、医疗这些行业,普通项目拿它当 Agent 的长期记忆也够用,一条命令就装上。
- Aug 29, 20264.3x their median
想跟孩子解释心脏长什么样、肾在哪个位置,翻百科全是平面图,比划半天也讲不明白。 anatomy 把 9 个人体器官做成了浏览器里能转着看的 3D 模型,打开网页就能用,项目介绍里写着整个应用是用 GPT 5.6 Sol 做出来的。 心脏、大脑、肺这些模型上都布了标注点,点一下就能看这个部位叫什么、管什么,还配了器官在身体里的位置图和显微镜下的组织图。 GitHub:https://t.co/WWXHn6XXwo 界面支持中文在内的 12 种语言,翻代码时留意到标注用的是国际标准解剖学术语那套体系,不是随手起的名,挺讲究。 当科普工具给孩子玩、自己涨涨知识都合适。
- Aug 29, 20264.2x their median
现在的剪映不少功能都需要会员才能使用,还动不动就提示升级,剪个短视频也得掂量掂量。 WolfCut 干脆做了个免费开源的替代,开箱即用,装上就能剪,无需注册。 GitHub:https://t.co/y24srURwQC 项目刚开源不久,功能目前还不是很多,但剪短视频常用几个功能都有。 包括多轨时间线、语音滤镜、模板复用,自动字幕还是本地转写的,视频不外传。 剪辑出来的视频不加水印,提供 Windows、macOS、Linux 三个平台安装包。
- Aug 26, 20263.7x their median
把几百页的 PDF 书丢给 AI 做总结,前几十页说得头头是道,越往后越含糊,结尾基本靠编。 AI-reads-books 换了个笨办法,一页一页地读,每页提取知识点存进本地知识库,边读边积累。 每隔一段页数产出一份阶段小结,全书读完再合成最终总结,前后文是连着的,不是各管一段。 GitHub:https://t.co/TndkPr1rNl 目录、索引这类没内容的页会自动跳过,中途断了也没事,下次接着上次的进度继续跑。 就一个 Python 脚本,改个 PDF 文件名就能跑,还能设定先只处理开头几页,试试效果再放全量。 啃大部头技术书,或者要给一堆资料出摘要的,逐页这种笨办法反而稳。
- Aug 23, 20263.1x their median
开线上会议边听边记,散会翻笔记发现关键的几句全漏了,只能回去重听一遍录音。 Pluely 是一个浮在桌面最上层的半透明小窗,分问答和聆听两种模式,开箱即用。 聆听模式实时转写麦克风和系统声音,带说话人标注,还能一边转写一边给出可以接的回答。 GitHub:https://t.co/V0kH53xhkJ 问答模式可以截屏、框选屏幕上任意一块区域,或者直接丢文件进去提问,文档会先过一遍文字识别。 答案是流式出来的,聊天记录全部存在本地,随时能搜、能导出、也能删干净。 用 Tauri 写的,安装包只有 9 到 16 MB,启动不到 100 毫秒,全局快捷键在任何应用里都能唤出来。 macOS、Windows、Linux 三端都有安装包。经常开会、听讲座又懒得做笔记的朋友,可以拿它当个实时助手。
- Aug 30, 20263.1x their median
给编码 Agent 做长期记忆的项目见过不少,但ai-memory 的实现思路,挺有意思的。 它将记忆当成纯 Markdown 文件笔记的 Git 仓库,没有用向量数据库。 笔记能搜索、能拿 Obsidian 打开、能整个备份走,哪天不用它了,文档还是自己的文档。 平时不用刻意维护,它靠钩子自动把提示词、工具调用、会话节点这些记下来,写代码的过程就是记录的过程。 GitHub:https://t.co/GLl0KoxVLn 交接是最能打的部分,Claude Code 干到一半退出,几小时后在同一目录打开 Codex,它开工前会先看到一块「上回做到哪」。 架构讲过什么、哪些路子试过不行,都不用再跟新工具交代一遍。 支持的工具列出来有 15 种往上,Claude Code、Codex、Cursor、Gemini CLI 都在里面,经常在几个工具之间换着用的,装一个能省掉不少重复交代。
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.
Buy or sell X accounts - escrow-protected
PlayerSells is an escrow marketplace for X accounts. Every deal is protected, with no middleman risk.
Reading these numbers
A typical post picks up 66 interactions against 84K followers, an engagement rate of 0.08%. Measured over 60 original posts, its engagement rate beats 45% of 6,874 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 5.5K times each, and 1.21% of those impressions turn into an interaction. That is about 6.56% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 43% of days saw any activity at all. Most posts go out around 10:00 UTC, and Sunday is the busiest day of the week. Of the 60 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 796 interactions, about 12x its own typical post.
- What is GitHubDaily's engagement rate on X?
- GitHubDaily (@GitHub_Daily) has an engagement rate of 0.08%, based on the median interactions across 60 original posts from the last 30 days against 83,858 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.08%, GitHubDaily sits above the 25th percentile of the 66,128 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 45% of the tracked X accounts closest to it in follower count (6,874 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 10:00 UTC, and Sunday is its busiest day, at roughly 2 posts per day across the measured window.