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Advanced Ontology Design: From a Reasonable Model to a Production-Grade Semantic Layer for Agents
When an ontology has to go to production: pick the right OWL profile, turn constraints into executable checks, handle identity and time, and let agents use it through a tool contract. This is about making it hold up, not making it look pretty.
Ontology Design from Scratch: Building a Reasoning-Ready Business Semantics Layer
Same model, same tools, same data — yet the agent is sometimes right and sometimes wrong. The difference is rarely the model; it is whether there is a shared, computable, constrainable layer of business semantics. This guide starts from zero and ends with a working minimal accounting ontology.
The Double-Graph Architecture: Separating "What the Target Is" from "How Far We Have Tested"
Most automation systems track progress in a single state table — and end up with facts and reasoning contaminating each other: unverified guesses get treated as confirmed assets, and work proceeds on false premises. This article unpacks a rare design that splits the truth store from the per-task reasoning chain into two graphs joined by anchors — and why that pattern deserves attention from any long-running AI agent system.
GitHub Trending May W4: A Deep Dissection of 14 Trending Projects
A complete analysis of the GitHub Trending list for May 18-24, 2026: from personal AI superintelligence to WiFi spatial sensing, from code knowledge graphs to turning everything into a CLI, covering the technical architecture, business value, and field assessment of 14 trending projects.
CodeGraph Deep Technical Breakdown: How to Save AI Coding Agents 35% in Costs and Cut Tool Calls by 70%
Complete technical analysis of the GitHub Trending #1 project CodeGraph: the architecture design of pre-indexed code knowledge graphs, the tree-sitter AST parsing pipeline, the MCP tool layer, performance comparison data against native Agents, and the real impact on Claude Code/Hermes Agent.