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.

When you build software for embedded devices (your Wi-Fi router or home automation setup on your Raspberry Pi), there is always the question how to build these images and how to update them.

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

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.

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!

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.

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.

The underlying problem we are facing here with all these examples is the same: having the same set of bits which can mean two different things and needing some way to distinguish those two meanings. Failure to do so leads to ambiguity at best and serious defects at worst. That's why you see so much emphasis in modern systems on type safety and on strict domain separation between different meanings.

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…

The Best Practices for OSS Developers working group is dedicated to raising awareness and education of secure code best practices for open source developers.

We're Sequin, an open source message stream built on Postgres. We think Sequin's cool, but you don't need to adopt the project to get started with streaming in Postgres. In fact, you can turn Postgres into a basic queue/stream pretty easily. Below, we share what we've learned so you

Talk from POSETTE conference