Skip to content

2025

  • 3 min read

Adsb

Reclaiming the Skies: Empowering Local ADS-B Monitoring

In today’s world, many enthusiasts are turning to Automatic Dependent Surveillance–Broadcast (ADS‑B) technology to track aircraft locally. Unlike traditional radar systems that depend on ground stations, ADS‑B is transmitted directly by aircraft using their onboard GPS and transponders—making the data openly available in real time.

Understanding ADS-B

Modern aircraft continuously broadcast their position and velocity via ADS‑B. This self-reporting system offers a transparent view of the skies without the need for centralized control. However, most commercial tracking services consolidate this information into large databases run by governments or corporations. By setting up your own receiver, you can reclaim that data and use it responsibly within your community.

  • 7 min read

Red Hat AI Inference Server (RHAIIS) with Open WebUI

With the recent announcement of Red Hat's new stand-alone AI Inference Server, I wanted to test it out locally in my Blinker19 Lab. I'm particularly interested in the LLM Compressor capabilities and seeing how increased the efficiency is between models. Red Hat AI Inference Server (RHAIIS) is a container image designed to optimize serving and inferencing with Large Language Models (LLMs), with the ultimate goal to make it faster and cheapeer. It leverages the upstream vLLM project, which provides bleeding-edge inferencing features. It also uses paged attention to address memory wastage, similar to virtual memory, which helps lower costs. Here is a blog that goes into more of a technical deep dive called Introducing RHAIIS: High-performance, optimized LLM serving anywhere.

  • 6 min read

Operation 0ri0n - Local AI

Recently, I found time to explore a new area and decided to delve into Data Science, specifically Artificial Intelligence and Large Language Models (LLMs).

Standalone AI Vendors

Using public and free AI services like ChatGPT, DeepSeek, and Claude requires awareness of potential privacy and data risks. These platforms may collect user input for training, leading to unintentional sharing of sensitive information. Additionally, their security measures might not be sufficient to prevent unauthorized access or data breaches.

Users should exercise caution when providing personal or confidential details and consider best practices such as encrypting sensitive data and regularly reviewing privacy policies.