Open Source Unix Shell Artificial Intelligence Software for Mac

Rhasspy (ˈɹæspi) is an open-source, fully offline set of voice assistant services for many human languages that works well with Hermes protocol-compatible services (Snips.AI), Home Assistant and Hass.io, Node-RED, Jeedom, OpenHAB. Rhasspy will produce JSON events that can trigger action in home automation software, such as a Node-RED flow. Rhasspy comes with a snazzy web interface that lets you configure, program, and test your voice assistant remotely from your web browser. All of the web UI's functionality is exposed in a comprehensive HTTP API. You can easily extend or replace functionality in Rhasspy by using the appropriate messages. Many of these messages can be also sent and received over the HTTP API and the WebSocket API. Rhasspy is intended for savvy amateurs or advanced users that want to have a private voice interface to their chosen home automation software.

Kalavai is a self-hosted platform that turns everyday devices into your very own AI cluster. Do you have an old desktop or a gaming laptop gathering dust? Aggregate resources from multiple machines and say goodbye to CUDA out-of-memory errors. Deploy your favorite open-source LLM, fine-tune it with your own data, or simply run your distributed work, zero-DevOps. Simple. Private. Yours.

SSH user management with add/delete users, online users, and limit users.

Olares is an AI-powered chatbot framework designed to support real-time natural language understanding and response generation.

MARF is a general cross-platform framework with a collection of algorithms for audio (voice, speech, and sound) and natural language text analysis and recognition along with sample applications (identification, NLP, etc.) of its use, implemented in Java.

Sakura is a Knowledge Navigator and User Interface for UNIX, which implements HyperMedia and its own windowing and packing system, both in the main program and in an extensive API for Tcl and other languages.

Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. Modularity and being designed for both scale and mobile deployments are the high-level answers to the first question. In many ways Caffe2 is an un-framework because it is so flexible and modular. The original Caffe framework was useful for large-scale product use cases, especially with its unparalleled performance and well tested C++ codebase. Caffe has some design choices that are inherited from its original use case: conventional CNN applications.

Easily Usable Lisp Utilized Interface and Editor aims to make GNU/Linux more enjoyable, and productive in the console environment.

This is a suite of mathematical software solvers interface on DLV and other command-line solvers. It helps researchers and students to better comprehend their declarative definitions published on papers. The user can have links to the related papers and to the software of the corresponding definitions.

MASyV (Multi-Agent System Visualization) enables one to write agent-based models/cellular automata, eg. in C, visualize them in real time & capture to movie file with MASyVs GUI & message passing lib. Includes examples: Hello World, ants, viral infection

The aim of MIEX (Metadata and Information Extractor from small XML documents) is to create a wrapper for the Stanford Parser, to extract and store metadata (syntactic structures, relationships among words...) from simple XML documents.

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection - https://github.com/Tessellate-Imaging/Monk_Object_Detection. Monk object detection is our take on assembling state of the art object detection, image segmentation, pose estimation algorithms at one place, making them low code and easily configurable on any machine. - Monk GUI - https://github.com/Tessellate-Imaging/Monk_Gui. An interface over these low code tools for non coders.

superseded by SgpDec http://sgpdec.sf.net Java implementation of the holonomy algorithm for the algebraic hierarchical decomposition of finite state automata.

GitHub Action for sending a Telegram notification message.

THIS PROJECT MIGRATED TO https://gitlab.com/mwetoolkit/mwetoolkit3/ The Multiword Expressions toolkit aids in the automatic identification and extraction of multiword units in running text. These include idioms (kick the bucket), noun compounds (cable car), phrasal verbs (take off, give up), etc. Even though it focuses on multiword expresisons, the framework is quite complete and can also be useful in any corpus-based study in computational linguistics. The mwetoolkit can be applied to virtually any text collection, language, and MWE type. It is a command-line tool written mostly in Python. Its development started in 2010 as a PhD thesis but the project keeps active (see the SVN logs). Up-to-date documentation and details about the tool can be found on the mwetoolkit website: http://mwetoolkit.sourceforge.net/