Open Source Mac System Software - Page 6

"Netlands Project" is a <b>Graphical MUD engine</b>, it comes with a distributed world server, an advanced IA server, a Macromedia Flash MX client and a predefined world named WOARPS, full of ready to go adventures.

tlearn is a backpropagation neural network simulator, written by Jeff Elman. xtlearn is a version of tlearn for the X Window System. OSXtlearn is xtlearn wrapped in a MacOSX application bundle that runs ons MacOSX 10.5 or higher and that requires XQuartz. OSXtlearn is created by Harm Brouwer.

Self Organization of an adaptive learning framework for pervasive graph structures from grids, utilities to semantic governance. Business, performance and provisioning assurance are converged with EGA, ITIL, eTOM,SOX, HIPPA, CobiT and AC

OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability.

OpenFlow is a web based, publishing workflow management system aimed at magazine production, controlling editorial planning thru imposition. It uses web standards (XHTML, CSS, javascript, XML, XSL, RDF) making it easy to configure and customize.

The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly.

Papier-Mâché is a toolkit for building tangible user interfaces that employ computer vision, RFID tags, and/or bar-codes.

Paul Graham GPT is a specialized AI-powered search and chat app built on a corpus of essays from Paul Graham, giving users the ability to query and discuss his writings in a conversational way. The repo stores the full text of his essays (chunked), uses embeddings (e.g. via OpenAI embeddings) to allow semantic search over that corpus, and hosts a chat interface that combines retrieval results with LLM-based answering — enabling RAG (retrieval-augmented generation) over a fixed dataset. The app uses a Postgres database (with pgvector) hosted on Supabase for its embedding store, making the backend relatively simple and accessible, and the frontend is again built with Next.js/TypeScript for a modern responsive UI. By pulling together search and chat, it creates a useful tool both for readers who want to revisit or explore Paul Graham’s ideas thematically, and for learners or researchers who want to query specific essays or concepts quickly.

Porcupine2 is a small wearable sensory unit for logging motion data and doing low-level activity recognition. The project includes electronic schematics for the hardware, software for the microcontroller and client-side software for hardware-interaction.

Project Daedalus is an aim to bring Artificial Intelligence to the Machintosh Operating System (Mac OS X) to aid the user

Prolog+CG is a Java implementation of Prolog with extensions implementing a subset of the Conceptual Graph (CG) theory of John Sowa. CGs are first-class datatypes on a par with terms. Object oriented extensions are also included.

The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of Particle Swarm Optimization is the emulation of the social behaviour of, e.g., a flock of birds, as a stochastic optimisation method. Specifically, a particle is an entity representing a solution in the search space. Several particles cooperate inside an algorithmic flow to occupy positions close to the best solution. Development takes place at https://github.com/zfoxer/PsoPath

Semantic Peer to Peer Recommender Systems -A simulation framework for semantic peer to peer recommender systems -P2P prototype

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).

A modern and usable interface for the well known neuronal network simulator SNNS. The GUI acts as a client to serveral servers embedding SNNS and supports team work aspects.

Robot Warfare is a real-time strategy game that gives the user an opportunity to manage and program a team of battling robots that autonomously face opposing teams in a network-enabled environment.

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.

A Distributed Web Search System bassed on current high level nerual network ai for the rating algorithum and search direction.

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.

The Secure Mobile Agents (SeMoA) platform constitutes a secure runtime environment for Java-based mobile software agents.

SecureJDMS is the attempt to develop a secure DMS, based on an RCP and (even untrusted) DB for data storage. For now, its all about managing scanned documents and searching them by content (using OCR). All data sent and stored will be strongly encrypted.

The purpose of SNX is to have a modular series of components that report back to a central repository and 'AI' for pattern recognition and anomoly detection. This is to help with IDS systems, and to move to the next generation of security.

A packet dissector driven by machine learning algorithms. You train it to recognize specific types of packets by showing it examples and counterexamples of some packet type, and it will figure out which bits in the packet define it as the type you seek.

SimIS presents an agent-based simulation environment from multiagent research to simulate Internet-of-Services systems. The simulation environment bases on Repast Simphony 1.2 (http://repast.sourceforge.net/).