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Source channel @olddriverGDstudy · Post #58 · Mar 27

#风哥避孕套如何选择课堂笔记 都说了多少遍了,别TMD买冈本,冈本TMD容易破 油少,一样的价钱不会买旁边的相模啊,玻尿酸套子也有缺点虽然润但是时间久了干的快,沐浴乳我不挑但是有一个沐浴乳我拒绝 ,力士的薰衣草真的不好闻,冈本最大的问题就是他油放的少拿出来就干,要润就玻尿酸 然后赤尾有小储精囊跟无储精囊套 要感觉我都是用浮点的,浮点套女的感觉来得快,有些人就马眼有感觉的这么办 不过无储精囊适合做多了跟射精量不大的用要不然会破的,超市就买杜蕾斯 杰士邦 相模,淘宝你看中啥买啥,然后小科普 0.01都是聚氨酯套 其他的都是乳交套,名流的玻尿酸套还是不错的,套子我是不追求的薄的,套子主要是为了安全还有就是润,很多套子很润但是油少玻尿酸少了也不行,像玻尿酸套子虽然很润但是也干的快,捷古斯也算日本大牌了,蝴蝶套一个形容 牌子叫捷古斯 因为包装上印着蝴蝶,买啥套子真的是最啥太大追求就用JS的套子 干了就跟JS说换个套子 #知识#避孕套

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Search: #geolocation

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Libreware

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

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 · 01/25/2017, 01:30 PM

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.