User-friendly glob matching
Extends `minimatch.match()` with support for multiple patterns
It's a very fast and efficient glob library for Node.js
An easy-to-use wildcard globbing library.
Lexes CommonJS modules, returning their named exports metadata
Bash-like brace expansion, implemented in JavaScript. Safer than other brace expansion libs, with complete support for the Bash 4.3 braces specification, without sacrificing speed.
Extended glob support for JavaScript. Adds (almost) the expressive power of regular expressions to glob patterns.
No description provided.
No description provided.
n-D spatial indexing data structures with a shared ES6 Map/Set-like API
awesome ngx mask
Utilities for working with match patterns.
CLI tool for intra-mart Accel Platform development
Markdown dictionary for cspell.
CSS dictionary for cspell.
Lazy-evaluating list of files, based on globs or regex patterns
ByteStream is a library making possible to manipulates single bytes and bits on pure JavaScript
Transform GLOB patterns to JavaScript regular expressions for fast file path matching.
HTML dictionary for cspell.
Use the Supabase JavaScript library in popular server-side rendering (SSR) frameworks.
a glob matcher in javascript
An AST-based pattern checker for JavaScript.
Core logic for the tour widget implemented as a state machine
Parse a .gitignore or .npmignore file into an array of patterns.
Universal GPU actor blueprints for rakka-accel-cuda: DynamicBatchingServer, InferenceCascade, ModelReplicaPool, FairShareScheduler, ModelHotSwapServer, SpeculativeDecoder, MoeRouter.
Backend-agnostic compute-acceleration core. Defines the AccelBackend trait, AccelRef<T> typed pointers, AccelError enum, and CompletionStrategy — the abstraction layer that lets rakka-accel-cuda (NVIDIA), and future ROCm / Metal / oneAPI / Vulkan backends plug into the same actor surface.
GPU acceleration via the actor model. Wraps NVIDIA CUDA libraries (cuBLAS, cuDNN, cuFFT, cuRAND, cuSOLVER, cuSPARSE, cuTENSOR, cuBLASLt, NVRTC, NCCL) as supervised rakka actors with generation-validated buffers and a uniform async surface.
Distributed training blueprints on rakka-accel-cuda: DataParallelTrainer, PipelineParallelTrainer, TensorParallelTrainer, AsyncParameterServer, optimizer + loss enums.