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Kanal tas-sors @linuxgram · Post #17841 · Fra 19

📰 AI Helped Uncover A "50-80x Improvement" For Linux's IO_uring Linux block maintainer and IO_uring lead developer Jens Axboe recently was debugging some slowdowns in the AHCI/SCSI code with IO_uring usage. When turning to Claude AI to help in sorting through the issue, patches were devised that can deliver up to a "literally yield a 50-80x improvement on the io_uring side for idle systems." The code is on its way to the Linux kernel... 🔗 Source: https://www.phoronix.com/news/AI-50-80x-IO-uring #linux#kernel

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Tfittxija: #geolocation

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Libreware

@libreware · Post #1512 · 28/09/2025 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 · 25/01/2017 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.