Regular Expressions made Strangely Simple. Contribute to StartAutomating/Irregular development by creating an account on GitHub.
regex101: build, test, and debug regex
Regular expression tester with syntax highlighting, explanation, cheat sheet for PHP/PCRE, Python, GO, JavaScript, Java, C#/.NET, Rust.
A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code https://t.co/pMhg9AxYbJ
A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code… https://t.co/XgyFxNmImW pic.twitter.com/pMhg9AxYbJ— slash1s (@slash1sol) June 5, 2026
RegExr: Learn, Build, & Test RegEx
RegExr is an online tool to learn, build, & test Regular Expressions (RegEx / RegExp).
Fast regex search: indexing text for agent tools · Cursor
How we're building indexes for regular expression search so agents can find text in large monorepos without the 15-second ripgrep waits.
Warnings for pattern matching
We examine the ML pattern-matching anomalies of useless clauses and non-exhaustive matches. We state the definition of these anomalies, building upon pattern matching semantics, and propose a simple algorithm to detect them. We have integrated the algorithm in the Objective Caml compiler, but we show that the same algorithm is also usable in a non-strict language such as Haskell. Or-patterns are considered for both strict and non-strict languages.
Vim anti-patterns | Arabesque
The benefits of getting to grips with Vim are immense in terms of editing speed and maintaining your “flow” when you’re on a roll, whether writing code, poetry, or prose, but because the learning curve is so steep for a text editor, it’s very easy to retain habits from your time learning the editor that stick with you well into mastery. Because Vim makes you so fast and fluent, it’s especially hard to root these out because you might not even notice them, but it’s worth it. Here I’ll list some of the more common ones.
Packrat parsing: | Proceedings of the seventh ACM SIGPLAN international conference on Functional programming
For decades we have been using Chomsky's generative system of grammars, particularly context-free grammars (CFGs) and regular expressions (REs), to express the syntax of programming languages and protocols. The power of generative grammars to express ...
le0pard/re2js
RE2JS is the JavaScript port of RE2, a regular expression engine that provides linear time matching
BurntSushi/ripgrep
ripgrep recursively searches directories for a regex pattern while respecting your gitignore
An 8 Year Algorithm Nemesis: Analyzing long string trends in a body of text with suffix trees
Efficient and flexible incremental parsing | ACM Transactions on Programming Languages and Systems
Previously published algorithms for LR (k) incremental parsing are inefficient, unnecessarily restrictive, and in some cases incorrect. We present a simple algorithm based on parsing LR(k) sentential forms that can incrementally parse an arbitrary number ...
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages, specifically those using non-Roman scripts. We propose an approach that utilizes the romanized form of text as an interface for LLMs, hypothesizing that its frequent informal use and shared tokens with English enhance cross-lingual alignment. Our approach involve the continual pretraining of a English LLM like Llama 2 on romanized text of non-English, non-Roman script languages, followed by instruction tuning on romanized data. The results indicate that romanized text not only reduces token fertility by 2x-4x but also matches if not outperforms native script representation across various NLU, NLG and MT tasks. Moreover, the embeddings computed on romanized text exhibit closer alignment with their English translations than those from the native script. Our approach presents a promising direction for leveraging the power of English LLMs in languages traditionally underrepresented in NLP research.
