Infrastructure

Abstract This document outlines a foundational perspective for a possible future discipline of runtime epistemology — the study of how infrastructure systems can quantify their own state of divergence from intended behavior. It proposes that Shannon entropy offers a mathematically principled basis for measuring runtime drift in live systems, forming the core of a design pattern suitable for both operational reliability and machine-driven reasoning. Introduction Contemporary infrastructure systems are increasingly dynamic, distributed, and subject to change. While observability tools have improved, systems still rely on humans to reconcile what is happening with what was supposed to happen. This epistemic gap — the difference between actual and intended behavior — remains largely qualitative, ad hoc, and unmeasured. ...

Karma is an experimental system that treats infrastructure not just as state — but as consequence. What Is Karma? Karma is a new open source project that builds on the ideas behind Adage, extending them into a fully introspectable, graph-based runtime. At its core, Karma models your infrastructure as a graph of components, each defined by versioned configuration (in Git and AWS Parameter Store) and connected by runtime dependencies. Every component is: ...

This article outlines the core design principles behind a configuration-driven approach to AWS infrastructure. These principles are designed to support modularity, auditability, scalability, and low-friction deployment across environments. Together, they form a consistent model that separates infrastructure code, configuration, and runtime behavior—while keeping everything connected through predictable patterns. If you’re looking to implement infrastructure that supports dynamic environments, feature isolation, and clean service boundaries, these principles provide a framework for building systems that are both flexible and easy to manage. ...