drist aims at being simple to understand and pluggable with standard tools. There is no special syntax to learn, no daemon to run, no agent, and it relies on base tools like awk, sed, ssh and rsync.

Exciting times are ahead for us. We expect that our Zion, Kings Canyon, and Mount Shasta designs will address our growing workloads in AI training, AI inference, and video transcoding respectively.

Ladies and gentlemen, I would like you to welcome the new shiny RFC8482, which effectively deprecates the DNS ANY query type. DNS ANY was a "meta-query" - think of it as a similar thing to the common A, AAAA, MX or SRV query types, but unlike these it wasn't a real query type - it was special. Unlike the standard query types, ANY didn't age well. It was hard to implement on modern DNS servers, the semantics were poorly understood by the community and it unnecessarily exposed the DNS protocol to abuse. RFC8482 allows us to clean it up - it's a good thing.

I’m a person who’s only satisfied if I feel I’m being productive. I like figuring things out. I like making things. And I want to do as much of that as I can. And part of being able to do that is to have the best personal infrastructure I can. Over the years I’ve been steadily accumulating and implementing “personal infrastructure hacks” for myself. Some of them are, yes, quite nerdy. But they certainly help me be productive. And maybe in time more and more of them will become mainstream, as a few already have.

For more than a century we’ve counted on calories to tell us what will make us fat. Peter Wilson says it’s time to bury the world’s most misleading measure.

As artificially intelligent systems grow in intelli- gence and capability, some of their available options may allow them to resist intervention by their programmers. We call an AI system “corrigible” if it cooperates with what its creators regard as a corrective intervention, despite de- fault incentives for rational agents to resist at- tempts to shut them down or modify their preferences.

We have grown from a handful of regions to 15 locations around the world. Even as the demands increase, we are bound by hard physical constraints of power and optics supply availability. Because of these dual pressures of increasing demand and physical constraints, we decided to rethink and transform our data center network from top to bottom, from topologies to the fundamental building blocks used within them. In this post, we’ll share the story of this transformation over the last two years.

Easy flamegraphs for Rust projects and everything else, without Perl or pipes.

Reinforcement learning agents interacting with a complex environment like the real world are un- likely to behave optimally all the time. If such an agent is operating in real-time under human supervision, now and then it may be necessary for a human operator to press the big red button to prevent the agent from continuing a harmful sequence of actions — harmful either for the agent or for the environment — and lead the agent into a safer situation. However, if the learning agent expects to receive rewards from this sequence, it may learn in the long run to avoid such interrup- tions, for example by disabling the red button — which is an undesirable outcome.