anarchygraph


Nameanarchygraph JSON
Version 0.1.8 PyPI version JSON
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home_pagehttps://pypi.org/project/anarchygraph/
SummaryA decentralized graph system to simulate agents in an artificial reality.
upload_time2024-07-05 05:39:19
maintainerNone
docs_urlNone
authorChris Mangum
requires_python>=3.10
licenseNone
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ![AnarchyGraph Logo](docs/img/anarchy_graph_logo.png)
![Project Status](https://img.shields.io/badge/status-in%20development-orange)
# Rules of AnarchyGraph

1. Be **independent**
2. Be **simple**
3. Be **optimized**

## Be Independent

A node is independent when it can operate and interact autonomously of other nodes.

In AnarchyGraph, each node is self-contained, having all it needs to interact with other nodes.

This independence promotes the self-organizing and self-sustaining nature of the graph, without centralized control.

## Be Simple

A node is simple when it performs its purpose efficiently and effectively.

This means the code and design should be optimized for speed and simplicity, making it easy to understand and manage any operational complexity within the graph.

While the node itself should remain straightforward, the data it handles can be complex.

## Be Optimized

A node is optimized when it maximizes performance and minimizes resource usage.

In AnarchyGraph, optimization goals:

- Efficient algorithms to handle node interactions and data processing.
- Minimizing memory footprint to allow for scalable implementations.
- Reducing computational overhead to ensure swift operations.

The goal is to ensure each node operates at its best, enabling the entire graph to function seamlessly and efficiently under various load conditions and implementations.

            

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