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.

[...] 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.

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.

Rex Wangs blog

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.

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!

Macros, iterators, patterns, error handling and match make Rust almost perfect

Commentary on software, robotics, and computer vision.

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.

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…