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?

Last year, I came agross a Linux kernel feature called TCG_TPM2_HMAC. It claims to detect or prevent active and passive interposer attackers.
It all sounds really great. We should care about interposer adversaries. It’s great to use the TPM features that were invented to help us with these problems.

[...] 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.

Achieving high-performance asynchronous Rust projects transcends mere usage of the async/await syntax; it fundamentally relies on a deep understanding of the underlying task scheduling, lock optimization, and architecture design principles.

This blog post describes an alternative, straightforward approach to collaborative text editing, without Conflict-free Replicated Data Types (CRDTs) or Operational Transformation (OT). By making text editing flexible and easy to DIY, I hope that the approach will let you create rich collaborative apps that are challenging to build on top of a black-box CRDT/OT library.

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.

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

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.

This blogpost describes our investigation and proof of concept to enhance the security of Signal Messenger key management on desktop.

This guide explains everything you need to know to build stand-alone photovoltaic systems that can power almost anything you want.

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.)

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.