Information architecture for AI reasoning. PromptOS structures rigorous thinking (7 or 8-step pipelines). HITL Context Engine manages cross-domain problems with human guidance. Both work with any model—Claude, GPT-4, Gemini. Copy, paste, use immediately. MIT licensed, freeware logic. GitHub: m727ichael/context-engineering

Hi everyone. I'm m727ichael. Context Engineering is something I've been building for a while—two operating systems for thinking with AI. PromptOS structures how models reason. Two pipelines: X for everyday tasks, Ω++ for high-stakes problems. Both scale automatically from simple to complex. HITL Context Engine manages what context the model needs and when human judgment is essential. Built for cross-domain problems where you need both rigor and human direction. The core insight: information architecture determines behavior. Change what information flows to the model, when it flows, and how it's structured—you change what the system can do. Both are copy-paste ready. Work with any model. MIT licensed. I'm excited to see what people build with these. Questions, feedback, and use cases welcome.

Launching context-engineering: Information Architecture for AI Reasoning

PromptOS structures thinking (7-8 step pipelines). HITL Context Engine manages context and human control.

Both visible, transparent, copy-paste ready.

Information architecture determines behavior. Change the information flow, change what's possible.

GitHub: m727ichael/context-engineering