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?

This is a tutorial on how to build a digital implementation of a 2nd-order, continuously-variable filter (i.e. one where you can change the parameters runtime) that has dynamic behaviour that mimics an analogue filter.

This post can be considered an advanced Zig tutorial. I will be covering some of the more unique
aspects of the language, but won't be explaining the easy part. If you haven't read the Zig
Language Reference, you might start there. Additionally,
we will also learn the foundational trick for implementing relational model.

In this deep dive post, I’ll explain how I used the interning design pattern in Rust to compress this data set by a factor of two thousand! We’ll investigate how to best structure the interner itself, how to tune our data schema to work well with it, and likewise how serialization can best leverage interning.

In a previous post, I’ve shown how to use the rayon framework in Rust to automatically parallelize a loop computation across multiple CPU cores.
In this post, I’ll first explain which profiling tools I used to chase optimizations, before diving into how I built a faster replacement of Rayon for my use case. In the next post, I’ll describe the other optimizations that made my code much faster. Spoiler alert: copying some data sped up my code!

Discrepancies in how browsers and libraries handle HTTP cookies, and the problems caused by such things.

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.

I want to try to catalog the bits that I wish someone had just told me before working with a Postgres database. Hopefully, this makes things easier for the next person going on a journey similar to mine.

Commentary on software, robotics, and computer vision.

Web browsers are ubiquitous, but how do they work? This book explains, building a basic but complete web browser, from networking to JavaScript, in a couple thousand lines of Python.

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.)

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.

In a previous article, I described how to set use Nix and Niv to configure an Elixir dev environment. This setup can be simplified by using Nix flakes instead of Niv.

In this article we build off what we’ve already learned about io_uring and extend that to build an async echo server.

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

Talk from POSETTE conference