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Category: Computer Science

Dave Täht, LEDE and CoDel

Dave Täht

This is Dave Täht. Dave is working on LEDE. If you have been working with OpenWRT in the past, you should switch to LEDE. You should also give a lot of money to the Patreon of Dave.

Why should you be doing this?

Dave has been working on Networking Theory and Practice, and has been implementing CoDel. CoDel is one of the few innovations in basic networking, a queueing theory and algorithm that can make thick and fast internet pipes actually fast and efficient by managing buffer memory right.  That means that your Internet is fast, and feels fast, even if it is busy.

Getting CoDel right on Wifi is doubly hard because of interaction between the CoDel queues, Wifi media access control and Wifi encryption. But Dave has done that, and it’s part of LEDE.

So, you should

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On Sandboxing, and Linux distro differences

Dan Walsh, Redhat, SELinux Developer, weeps when you disable SELinux

On one end of the spectrum, LearntEmail points to Stop Disabling SELinux and asks us to instead set up proper sandboxes to contain software: SELinux – A Real-World Guide.

On the other hand, Kristaps Dz explains how differences in Linux Distros, Libraries and other environmental factors make it very hard to define sandboxes in a portable way (seccomp, in this case), so that they can be shipped with an application, such as the Let’s Encrypt ACME client he develops. The LWN Article pointing to this has interesting discussion.

There is a lot to be learnt between these two extremes, for example why we can’t have nice things.

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The History of Moore’s Law

Commodore 64, Evan-Amos @ Wikipedia

As Moore’s Law shown signs of coming to an end, the Guardian has an article about the history of computing from the Intel 4004 to current tech, and how with the Internet of Shit and Chips and Networking in everything all of our computers are becoming just one computer.

Everyone knows that modern computers are better than old ones. But it is hard to convey just how much better, for no other consumer technology has improved at anything approaching a similar pace. The standard analogy is with cars: if the car from 1971 had improved at the same rate as computer chips, then by 2015 new models would have had top speeds of about 420 million miles per hour.

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“There wouldn’t be a Microsoft today without Dave”

Dave Cutler (Image: Majorconfusion @ Wikipedia)

Dave Cutler, now 74, has had his 50th working anniversary, and for that reason we find a laudatio at Microsoft’s News Site.

Cutler not only has been one of the architects of the VAX and their VMS operating system, but also in the designs of the DEC Alpha CPU and its predecessors.

Later at Microsoft, he has been working on the DEC Alpha port of Windows NT, been instrumental in the development of the Windows Server operating system, and been a lead developer in Microsoft’s cloud system, Azure. He’s also been involved in porting the Hyper-V Hypervisor to the XBox.

It is really rare to find people who have been working in IT for so long, who have been involved in so many aspects – from hardware and chip design through operating system design to platform architecture and virtualisation, and even more so, who have been doing this in an ecosystem that is independent and outside of the Unix/Linux universe.

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Netflix vs. IP v6

Netflix detects a tunnel

So in order to view Netflix, your network connection must be direct and not via a proxy or VPN tunnel.

Netflix, being somewhat modern, also advertises IP v6 services and AAAA DNS records so that your computer can find them.

On the other hand, many providers do not offer IP v6 natively, and hence require that customers who want non-legacy internet get it via – right – a network tunnel.

Which triggers the Netflix error message shown above.

Netflix knows that, but offers little support besides “Don’t use a tunnel, then”. Haha. So this article explains how to unfuck Networking for a local Linux or a Chromecast to make Netflix work again. Even if that just means to force it to fall back to l;egacy Internet instead.

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Google vs. your data

Hooray, EU data protection authorities confirm compliance of Google Cloud commitments for international data flows! exclaims Google.

But on the other hand, Google ordered to hand over foreign emails to FBI, unlike Microsoft.

With legal instabilities and conflicting signals like these, are you running your crap in a public cloud owned and operated by a US company?

You probably should, it’s still better infra than you could create yourself. But the legal nonframework around it – it is not helping at all.

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Linux, from before the war…

Seth Kenlon from RedHat has been finding old Linux distros from before the war and installed them. How bad is this old shit, from todays POV?

Slackware 1.01 (1993): It does not even have package management. But:

Over all, Slackware 1.01 feels a lot like a fairly modern UNIX—or more appropriately, it feels like modern UNIX might feel to a Linux user. Most everything is familiar, but there are differences here and there. Not nearly as much a difference as you might expect from an operating system released in 1993!

And it continues like that – much of the old stuff works and feels a lot like stuff today. So besides systemd, there has hardly been any progress at all.

For example, some people’s desktop looks like this, even today:

SUSE 5.1 (1998) – I know people how are using a desktop that looks like this, even today.
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Unlearning Descriptive Statistics

Anscombe’s Quartet by Schutz

Unlearning Descriptive Statistics explains many things you should know about working with Numbers that your Statistics Class in University probably did not explain properly.

If they did, maybe Graphite would not hurt so much, with all the Averaging going on where it shouldn’t, and maybe Gill Tene would not have had to give talks like How NOT to measure latency (which is awesome, by the way and if you haven’t seen this talk, do it right now).

From the Intro of Unlearning:

If you’ve ever used an arithmetic mean, a Pearson correlation or a standard deviation to describe a dataset, I’m writing this for you. Better numbers exist to summarize location, association and spread: numbers that are easier to interpret and that don’t act up with wonky data and outliers.

Statistics professors tend to gloss over basic descriptive statistics because they want to spend as much time as possible on margins of error and t-tests and regression. Fair enough, but the result is that it’s easier to find a machine learning expert than someone who can talk about numbers. Forget what you think you know about descriptives and let me give you a whirlwind tour of the real stuff.

Go, read the rest.

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