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Source channel @FindBlog · Post #643 · 3月1日

Flare Stack Blog ——基于 Cloudflare Workers 的现代化全栈博客 CMS ## 核心功能 • 文章管理 — 富文本编辑器,支持代码高亮、图片上传、草稿/发布流程 • 标签系统 — 灵活的文章分类 • 评论系统 — 支持嵌套回复、邮件通知、审核机制 • 友情链接 — 用户申请、管理员审核、邮件通知 • 全文搜索 — 基于 Orama 的高性能搜索 • 媒体库 — R2 对象存储,图片管理与优化 • 用户认证 — GitHub OAuth 登录,权限控制 • 数据统计 — Umami 集成,访问分析与热门文章 • AI 辅助 — Cloudflare Workers AI 集成 • 主题系统 — 可扩展的主题模板,支持完整替换所有页面和布局 • 导入导出 — 支持Markdown导入导出,保留图片以及Frontmatter Flare Stack Blog 的所有面向用户的页面与布局均通过 主题契约(Theme Contract) 与业务逻辑解耦。你可以在不修改任何路由或数据逻辑的前提下,完整替换博客的视觉表现层。 项目地址:https://github.com/du2333/flare-stack-blog #Platform#Cloudflare 频道:@FindBlog 群组:@FindBlog_Group

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Libreware

@libreware · Post #1512 · 2025/09/28 11:58

Chasing Your Tail (CYT) https://github.com/ArgeliusLabs/Chasing-Your-Tail-NG A comprehensive #WiFi probe request analyzer that monitors and tracks wireless devices by analyzing their probe requests. The system integrates with #Kismet for packet capture and WiGLE API for #SSID#geolocation analysis, featuring advanced #surveillance#detection capabilities. Features Real-time Wi-Fi monitoring with Kismet integration Advanced surveillance detection with persistence scoring Automatic GPS integration - extracts coordinates from Bluetooth GPS via Kismet GPS correlation and location clustering (100m threshold) Spectacular KML visualization for Google Earth with professional styling and interactive content Multi-format reporting - Markdown, HTML (with pandoc), and KML outputs Time-window tracking (5, 10, 15, 20 minute windows) WiGLE API integration for SSID geolocation Multi-location tracking algorithms for detecting following behavior Enhanced GUI interface with surveillance analysis button Organized file structure with dedicated output directories Comprehensive logging and analysis tools Requirements Python 3.6+ Kismet wireless packet capture Wi-Fi adapter supporting monitor mode Linux-based system WiGLE API key (optional)

djangoproject

@djangoproject · Post #241 · 2017/01/25 13:30

http://www.aparat.com/v/4yGhH #Geolocation apps with #Django. Latitude, longitude, altitude, and even #iBeacons can be leveraged to enable geo-targeted experiences. But how do we build and optimize the server-side components to handle these requirements? Using a combination of libraries and techniques, we will illustrate these concepts. In this discussion everything from #map clustering and caching, to distance calculations and polygonal layering will be demonstrated using Django, #GeoDjango, #Redis, and #PostGIS as our tool belt.