Worth Reading 092026


 


We will be as unknowledgeable about what machines know about us as they are about what we know about them.

 


This post analyzes the technical details of the BGP hijack against Softaculous Ltd, the company behind the Softaculous auto-installer and the Virtualizor VM management platform.

 


In this episode of PING, we speak with Willem Toorop from NLnet Labs and Ilyas Rahimi, who recently completed a Master’s in Security and Network Engineering at the University of Amsterdam (UvA). They discuss their research into the effects of local root serving.

 


One effective form of attack on the Domain Name System (DNS) infrastructure, including the root servers, is the so-called ‘random name attack’.

 


The original purpose of a telephone number was to identify a specific customer. When telephones first came into use, they could only be used to call people in the immediate area who could be connected by the local neighborhood operator.

Hedge 319: Using AI


 
AI is everywhere. While there are many questions about whether AI is a bubble, the ethics of AI, and the cultural impact of AI, in this episode of the Hedge Mike Bushong joins Eyvonne, Tom, and Russ to talk about how network engineers can use AI. Are some use cases better than others? Are there pitfalls network engineers should avoid? Listen in and find out.
 
https://media.blubrry.com/hedge/media.blubrry.com/hedge/content.blubrry.com/hedge/hedge-319.mp3
 
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Worth Reading 091126


 


Recognizing that AI is a bubble requires one to apply the right test to the right companies and that requires one to understand the fundamental nature of the bubble in question.

 


Based on the insights from those articles, we now shift attention to which parts of the Internet Route Registry (IRR) landscape can – or cannot – be replaced safely with information from the Resource Public Key Infrastructure (RPKI) system.

 


While many faults manifest as crashes or exceptions, others are far more subtle to identify. Silent data corruptions (SDCs), also known as silent data errors or silent errors, are violations of data integrity that occur without immediate, observable, and explicit indications.

 


Aligning intended learning outcomes, assessments, and teaching practices with the realities of AI-assisted programming.

 


AI is transforming the role of the junior developer. But as routine tasks become increasingly automated, how will the next generation of software engineers master the craft of maintaining complex systems?

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