AWK, the text-processing scripting language has been with us since the 1970s. It remains widely used...
Semble: Fast and Accurate Code Search for Agents
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
Speed Comparison - Programming Languages
Benchmarks run on GitHub Actions. Results may vary based on runner hardware.
zk : a plain text note-taking assistant
zk is a plain text note-taking assistant that leverages the power of the command line.
Comparing gRPC performance across different technologies
gRPC is an open-source Remote Procedure Call system focusing on high performance. There exist several gRPC benchmarks including an official one, yet we still wanted to create our own.
AI Coding Agent Benchmarks & Leaderboard | Artificial Analysis
We measure real-world performance of coding agents on software engineering tasks, including cost, token usage, and execution time. We compare how performance changes across agents, models, and execution settings.
iA Writer: The Benchmark of Markdown Writing Apps
Plain text. Total focus. The Industry standard Markdown text editor. Available for Mac, iPhone, iPad, Android, and Windows. Download it now, try it for free
Snakemake
With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Steps are defined by "rules", which denote how to generate a set of output files from a set of input files (e.g. using a shell command). Wildcards (in curly braces) provide generalization. Dependencies between rules are determined automatically.
Natural Language Processing With Python's NLTK Package – Real Python
In this beginner-friendly tutorial, you'll take your first steps with Natural Language Processing (NLP) and Python's Natural Language Toolkit (NLTK). You'll learn how to process unstructured data in order to be able to analyze it and draw conclusions from it.
Engineering High-Performance Parsers with Data-Oriented Design
Notes from building Yuku: the AST is flat arrays of u32 indices instead of a pointer tree, and memory layout, allocation, strings, unicode, and serialization all follow from that one decision.
NLTK Book
This version of the NLTK book is updated for Python 3 and NLTK 3. The first edition of the book, published by O'Reilly, is available at http://nltk.org/book_1ed/. (There are currently no plans for a second edition of the book.)
MirrorCode: A benchmark for real-world software projects
Artificial Writing and Automated Detection
Artificial intelligence (AI) tools are increasingly used for written deliverables. This has created demand for distinguishing human-generated text from AI-generated text at scale, e.g., ensuring assignments were completed by students, product reviews written by actual customers, etc. A decision-maker aiming to implement a detector in practice must consider two key statistics: the False Negative Rate (FNR), which corresponds to the proportion of AI-generated text that is falsely classified as human, and the False Positive Rate (FPR), which corresponds to the proportion of human-written text that is falsely classified as AI-generated. We evaluate three leading commercial detectors—Pangram, OriginalityAI, GPTZero—and an open-source one —RoBERTa—on their performance in minimizing these statistics using a large corpus spanning genres, lengths, and models. Commercial detectors outperform open-source, with Pangram achieving near-zero FNR and FPR rates that remain robust across models, threshold rules, ultra-short passages, "stubs" (≤ 50 words) and ’humanizer’ tools. A decision-maker may weight one type of error (Type I vs. Type II) as more important than the other. To account for such a preference, we introduce a framework where the decision-maker sets a policy cap—a detector-independent metric reflecting tolerance for false positives or negatives. We show that Pangram is the only tool to satisfy a strict cap (FPR ≤ 0.005) without sacrificing accuracy. This framework is especially relevant given the uncertainty surrounding how AI may be used at different stages of writing, where certain uses may be encouraged (e.g., grammar correction) but may be difficult to separate from other uses.
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.
Compiling Match Statements to Bytecode
Full pipeline deep dive for purple garden: AST, BB SSA IR, Bytecode, Optimisations