With this demo, you can supply an Input string and see the combinations that are confusable with it, using data collected by the Unicode consortium. You can also try different restrictions, using characters valid in different approaches to international domain names.

TL;DR: OSC52 is an ANSI escape sequence that allows you to copy text into your system clipboard from anywhere, including from remote SSH sessions. Check vim-oscyank, a plugin which integrates OSC52 into Vim.

The basic insight behind Levenshtein automata is that it's possible to construct a Finite state automaton that recognizes exactly the set of strings within a given Levenshtein distance of a target word. We can then feed in any word, and the automaton will accept or reject it based on whether the Levenshtein distance to the target word is at most the distance specified when we constructed the automaton. Further, due to the nature of FSAs, it will do so in O(n) time with the length of the string being tested. Compare this to the standard Dynamic Programming Levenshtein algorithm, which takes O(mn) time, where m and n are the lengths of the two input words! It's thus immediately apparrent that Levenshtein automaton provide, at a minimum, a faster way for us to check many words against a single target word and maximum distance - not a bad improvement to start with!

Of course, if that were the only benefit of Levenshtein automata, this would be a short article. There's much more to come, but first let's see what a Levenshtein automaton looks like, and how we can build one.

This service uses linguistic analysis to detect and interpret emotions, social tendencies, and language style cues found in text.

I’d like to lay out the main arguments that I have against Markdown. Hopefully this will be useful in helping you decide whether it’s a good fit for your organization. If you are considering Markdown, I hope that you also look at Asciidoctor and Sphinx. I find them to be much better toolsets for writing documentation.

K-tree is a tree structured clustering algorithm. It is also refered to as a Tree Structured Vector Quantizer (TSVQ). The goal of cluster analysis is to group objects based on similarity. Each object in a K-tree is represented by an n-dimensional vector. All vectors in the tree must have the same number of dimensions.

This package provides an essential feature to LaTeX that has been missing for too long. It adds a coffee stain to your documents. A lot of time can be saved by printing stains directly on the page rather than adding it manually.

This website is not designed to teach you how to use LaTeX. There is a great deal of information on that elsewhere, most of it for free. Rather, this website is designed to make LaTeX easy for the beginner as well as for the expert by providing heavily commented, easy to understand, templates for a diversity of document types. It is my hope that this website will decrease frustration, increase the use of LaTeX and provide a generally useful service to all who are interested.

WriteLaTeX is a free online collaborative LaTeX editor that first went live on September 30, 2011, and has been improving ever since.

Draw something in the left box! And let shapecatcher help you to find the most similar unicode characters! Currently, there are 11817 unicode character glyphs in the database. Japanese, Korean and Chinese characters are currently not supported.