A powerful technique called SAT solving could work on the notorious Collatz conjecture. But it’s a long shot.

A book for learning the Vim editor. Contribute to iggredible/Learn-Vim development by creating an account on GitHub.

Turn a command line script into a web service. Contribute to beefsack/script-httpd development by creating an account on GitHub.

Moreutilis is a growing collection of more useful Unix utilities. The moreutils can be installed on GNU/Linux, FreeBSD, openBSD and Mac OS.

A huge list of alternatives to Google products. Privacy tips, tricks, and links.

A new kit leaves your cryptographic destiny up to 25 cubes in a plastic box.

The editor behind a new academic journal that only publishes unsurprising research hopes the journal will help fix a major problem in scientific research — a bias towards surprising, unexpected results.

Interactively analyze NLP models for model understanding in an extensible and framework agnostic interface.

Written in Python without dependencies with an optional MRU ordering which could also be used as an application launcher and CtrlP alternative.

Archivy is a self-hosted knowledge repository that allows you to safely preserve useful content that contributes to your knowledge bank.

Open source turn-based survival RPG development project.

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".

No, there hasn’t been any new vulnerability found in SSH, nor am I denying the usefulness of SSH as a building block in the dev toolchain. This article is about why you shouldn’t be (and how you can avoid) using raw SSH sessions for development work.

In summary, how the author discovered screen, tmux, etc.

Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed.