Show HN: Contextual Engineering – Patterns for Building Offline-First AI Agents (github.com)

A new project called "Contextual Engineering" has emerged, focusing on developing offline-first AI agents tailored for environments often disregarded by conventional AI design, particularly in the Global South. This initiative highlights the limitations of standard AI systems, which are built for ideal conditions—consistent internet access and robust infrastructure. The project outlines three essential adaptation layers: the Infrastructure Adapter for managing offline states and limited computing resources, the Cultural Adapter for navigating semantic challenges in high-context communication, and the Safety Adapter to implement constitutional safeguards and a Human-in-the-Loop (HITL) approach. This repository serves as a significant resource for the AI/ML community, supplying Python reference implementations that address real-world challenges faced in diverse settings. By aligning the code’s structure with the adaptation layers discussed in the accompanying manuscript, developers can easily access and integrate these solutions into their AI projects. This addresses the widening "Agentic Gap," allowing for AI agents that better recognize and adapt to the unique needs and conditions of their environments, ultimately leading to more effective and meaningful AI applications in varied social contexts.