terminal tasks for neat-log
Fast CSV parser
Streaming CSV parser that aims for maximum speed as well as compatibility with the csv-spectrum test suite
Offload tasks to a pool of workers on node.js and in the browser
A lightweight, semantic grid framework
Run an array of functions in parallel
a neat logger for the command line
MediaPipe Vision Tasks
Cloud Tasks API client for Node.js
Run an array of functions in parallel, but limit the number of tasks executing at the same time
List of ML tasks for huggingface.co/tasks
High-priority task queue for Node.js and browsers
Lint files staged by git
Runs a list of async tasks, passing the results of each into the next one
Run promise-returning & async functions in series, each passing its result to the next
Run promise-returning & async functions concurrently with optional limited concurrency
Beautiful 3D gradients for your website
High-priority task queue for Node.js and browsers
Manage contextual information needed by (a)synchronous tasks without explicitly passing objects around
A shim for the setImmediate efficient script yielding API
PostCSS plugin that provides a semantic and fluid grid framework.
A diff friendly cli input module. Made for usage with neat-log and ansi-diff-stream
node-sass wrapper for thoughtbot's bourbon neat library
Linter for consistent directory and file naming.
One Rake task to give you rcov code coverage for your rails app. rake test:coverage
RubyNEAT -- Neural Evolution of Augmenting Topologies for Ruby. By way of an enhanced form of Genetic Algorithms -- the NEAT algorithm, populations of neural nets are evolved to handle pre-defined goals. RubyNEAT is the first implementation of the NEAT algorithm for Ruby, and it leverages Ruby's power to implement the NEAT algorithm in a way that would be difficult to do in other languages. The 'activation function' is largely standalone. Basically, activation is achieved by functional programming. Meaning, once your network is evolved, you can extract it as source code you can then utilize without the RubyNEAT engine. RubyNEAT can be used for nearly any Machine Learning task you can dream of, because it's also extensible and modular. See http://rubyneat.com for the details.