Start here if you're new to ML. In nineteen problems we'll get from dot products to a working training loop, all in NumPy. Implement the building blocks (sigmoid, softmax, cross-entropy, attention masks), then build a linear layer and work through the backwards passes that make training work.

There are thousands of options in NixOS, but as users, we usually only interact with a select few of them. Despite that, a huge amount of those options does influence the final result in some way. Have you ever wondered which of them were actually relevant for your specific system?

Some reflections on trusting trust, and how deep the rabbit hole goes.

[...] hard-learned patterns to write more defensive Rust code, learned throughout years of shipping Rust code to production. I’m not talking about design patterns here, but rather small idioms, which are rarely documented, but make a big difference in the overall code quality.

Windows 3 is often said to be just an UI on top of DOS. This article presents some of the inner side of Windows 3.x and will show that it is more ambitious and advanced than that.

Pipes are ubiquitous in Unix --- but how fast can they go on Linux? In this post we'll iteratively improve a simple pipe-writing benchmark from 3.5GiB/s to 65GiB/s, guided by Linux perf.

Step-by-step guide to building a NixOS 24 snapshot on Hetzner Cloud using Packer, with complete configuration files and OpenTofu deployment examples.

Last week, I built a tiny Linux system from scratch, and booted it on my laptop!

Fuzz testing is a technique for automatically uncovering bugs in software. The problem is that it’s a pain to set up. Read any fuzz testing tutorial, and the first task is an hour of building tools from source and chasing down dependencies upon dependencies.
I recently found that Nix eliminates a lot of the gruntwork from fuzz testing. I created a Nix configuration that kicks off a fuzz testing workflow with a single command.

The course is a hands-on, research-level introduction to the areas of computer science that have a direct relevance to journalism, and the broader project of producing an informed and engaged public $100 installment loan. We study two big ideas: the application of computation to produce journalism (such as data science for investigative reporting), and journalism about areas that involve computation (such as the analysis of credit scoring algorithms.)

Below is a list of some lessons I’ve learned as a distributed systems engineer that are worth being told to a new engineer. Some are subtle, and some are surprising, but none are controversial. This list is for the new distributed systems engineer to guide their thinking about the field they are taking on. It’s not comprehensive, but it’s a good beginning.

In this post, we are going to take a deep dive into pointer tagging, where metadata is encoded into a word-sized pointer. Doing so allows us to keep a compact representation that can be passed around in machine registers. This is very common in implementing dynamic programming languages, but can really be used anywhere that additional runtime information is needed about a pointer. We will look at a handful of different ways these pointers can be encoded and see how the compiler can optimize them for different hardware.

Explore the In Our Time archive.

JTAG for Reverse Engineers

TL;DR: A world first reverse engineering analysis of AWS Session Tokens. Prior to our research these tokens were a complete black box…

We examine the recent CrowdStrike outage and provide a technical overview of the root cause.

Talk from POSETTE conference