Internal Python Packages

mxm-scaffold

Scaffold is a library that makes building AI solutions on the Merantix Momentum AI Platform a walk in the park.

pharindoko-cdk-internal-gateway

CDK construct to create internal serverless applications and websites

onespacemedia-cms

A collection of Django extensions that add content-management facilities to Django projects.

nanodns

A lightweight, zero-dependency DNS server for internal networks — configured with a single JSON file.

weverse

Creates internal cache for the communities a user follows on weverse.io

cogito

Cogito is a Python module designed to streamline the creation and deployment of inference services. It provides tools and abstractions to wrap machine learning models and expose them as robust, production-ready APIs with minimal effort.

pydit-jceresearch

Library of data wrangling functions that an internal auditor typically needs (for my own use and learning, if you wish to collaborate pls get in touch, or use at your own peril).

onespacemedia-server-management

A set of server management tools used by Onespacemedia.

foxtail-contact

A simple, reusable Django contact form application, for internal use.

bunkersync

BunkerSync simplifies Git repository syncing. Sync internal and external repositories with branch filtering and automatic syncing. Ideal for developers who need to keep repositories in sync.

pysyncgateway

Library for communication with Couchbase Sync Gateway

clusters-features

The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.

dasst

A Python package to read system usage also known as Disk And System Stats Tool

inferia

Inferia is a Python module designed to streamline the creation and deployment of inference services. It provides tools and abstractions to wrap machine learning models and expose them as robust, production-ready APIs with minimal effort.