Ah, C. Still the language of modern high-performance computing.

C is the lowest-level language most programmers will ever use, but it more than makes up for it with raw speed. Just be aware of its manual memory management and C will take you as far as you need to go.

I contribute to various free software projects and maintain various pieces of free software. Many of the entries on this page are only of archaeological interest.

There was a recent argument about the Python Cryptography library switching to Rust. Who was at fault?

fastmod is a fast partial replacement for codemod. Like codemod, it is a tool to assist you with large-scale codebase refactors, and it supports most of codemod's options. fastmod's major philosophical difference from codemod is that it is focused on improving the use case "I want to use interactive mode to make sure my regex is correct, and then I want to apply the regex everywhere".

How to poison phishing sites with fake data.

Do you actually use algorithms and data structures on your day to day job? I've noticed a growing trend of people assuming algorithms are pointless questions that are asked by tech companies purely as an arbitrary measure. I hear more people complain about how all of this is a purely academic exercise. This notion was definitely popularized after Max Howell, the author of Homebrew, posted his Google interview experience:

Google: 90% of our engineers use the software you wrote (Homebrew), but you can’t invert a binary tree on a whiteboard so fuck off.

A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another. Transcompilers are primarily used for interoperability, and to port codebases written in an obsolete or deprecated language (e.g. COBOL, Python 2) to a modern one. They typically rely on handcrafted rewrite rules, applied to the source code abstract syntax tree. Unfortunately, the resulting translations often lack readability, fail to respect the target language conventions, and require manual modifications in order to work properly. The overall translation process is timeconsuming and requires expertise in both the source and target languages, making code-translation projects expensive.
Although neural models significantly outperform their rule-based counterparts in the context of natural language translation, their applications to transcompilation have been limited due to the scarcity of parallel data in this domain. In this paper, we propose to leverage recent approaches in unsupervised machine translation to train a fully unsupervised neural transcompiler. We train our model on source code from open source GitHub projects, and show that it can translate functions between C++, Java, and Python with high accuracy.
Our method relies exclusively on monolingual source code, requires no expertise in the source or target languages, and can easily be generalized to other
programming languages. We also build and release a test set composed of 852 parallel functions, along with unit tests to check the correctness of translations. We show that our model outperforms rule-based commercial baselines by a significant margin.

Here’s why Rust gets so much love, straight from the Rustaceans themselves.