Backing data processing types for compcon - total rewrite of existing code from the official project
A library to convert URLs to a clickable HTML anchor elements
Unique machine (desktop) id (no admin privileges required).
Easy as cake e-mail sending from your Node.js applications
State management made super simple
Mind elixir is a free open source mind map core.
Generate trusted local SSL/TLS certificates for local SSL development
State machine utilities for the Reach UI library.
Core logic for the checkbox widget implemented as a state machine
Best-effort discovery of the machine's default gateway and local network IP exclusively with UDP sockets.
Build functions in standardized containers.
AI-powered issue solver and hive mind for collaborative problem solving
Tests whether one path is inside another path
Explode async and generator functions into a state machine.
Text recoding in JavaScript for fun and profit!
XState for finite state machines
A conversational AI-driven telecom multi-agent system for managing call balances, push notifications, marketing, targeting, and sales.
A finite state machine library
Creates a consistent, implementation-agnostic hash from a given raw machine ID resolution function. Designed to be used by MongoDB Tools.
A visual builder for your Slice Models with all the tools you need to generate data models and mock CMS content locally.
Native retrieval of a unique desktop machine ID without admin privileges or child processes. Faster and more reliable alternative to node-machine-id.
Install this package as a pallette by using node red dashboard
JSON for Humans
A set of helpers to develop and run Slice Machine plugins
A barebone implementation of the finite state machine keeping simplicity in mind.
Cabalist is conceived as a simple way of adding some smarts (machine learning capabilities) to your Ruby on Rails models without having to dig deep into mind-boggling AI algorithms. Using it is meant to be as straightforward as adding a few lines to your existing code and running a Rails generator or two.
AI4R is a lightweight, educational Ruby library featuring clean implementations of core machine learning and AI algorithms—such as decision trees, neural networks, k-means, genetic algorithms, and even a bit size Transformers architecture covering encoder, decoder, and seq2seq variations. Designed with simplicity and clarity in mind, this library is ideal for students, educators, and developers who want to understand these algorithms line by line. With no external dependencies, no GPU support, and no production overhead, AI4R serves as a practical and transparent way to explore the foundations of AI in Ruby. It is a long-maintained open-source effort to bring accessible, hands-on machine learning to the Ruby community.
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