When a Device Without an IP Address Wants to Play the IP Game

After I published the Source IP Address in Multicast Packets blog post, Erik Auerswald sent me several examples of network devices sending IP packets with source IP address set to 0.0.0.0:

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Worth Reading: A Primer on Communication Fundamentals

Dip Singh published an excellent primer on communication fundamentals, including:

  • Waves: frequency, amplitude, wavelength, phase
  • Composite signals, frequency domain and Fourier transform
  • Bandwidth, fundamental and harmonic frequency
  • Decibels in a nutshell
  • Transmission impairments: attenuation, distortion, noise
  • Principles of modern communications: Nyquist theorem, Shannon’s law, bit and baud rate
  • Line encoding techniques, quadrature methods (including QPSK and QAM)

Even if you don’t care about layer-1 technologies, you MUST read it to get at least a basic appreciation of why stuff you’re using to read this blog post works.

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Please Respond: MANRS Customer Survey

Andrei Robachevsky asked me to spread the word about the new MANRS+ customer survey:

MANRS is conducting a survey for organizations that contract connectivity providers to learn more about if and how routing security fits into their broader supply chain security strategy. If this is your organization, or if it is your customers, we welcome you to take or share the survey at https://www.surveymonkey.com/r/BDCWKNS

I hope you immediately clicked on the link and completed the survey. If you’re still here wondering what’s going on, here’s some more information from Andrei:

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Classification of BGP Route Leaks (RFC 7908)

While preparing the Internet Routing Security webinar, I stumbled upon RFC 7908, containing an excellent taxonomy of BGP route leaks. I never checked whether it covers every possible scenario1, but I found it a handy resource when organizing my thoughts.

Let’s walk through the various leak types the authors identified using the following sample topology:

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Worth Reading: Building Stuff with Large Language Models Is Hard

Large language models (LLM) – ChatGPT and friends – are one of those technologies with a crazy learning curve. They look simple and friendly (resulting in plenty of useless demoware) but become devilishly hard to work with once you try to squeeze consistent value out of them.

Most people don’t want to talk about the hard stuff (sexy demoware results in more page views), but there’s an occasional exception, for example All the Hard Stuff Nobody Talks About when Building Products with LLMs describing all the gotchas Honeycomb engineers discovered when creating a LLM-based user interface.

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