Focused on deep research and development of large-model applications. Authors of "RAG Application Development and Optimization Based on Large Models" and "MCP Principles Unveiled and Development Guide". Primarily B2B, with B2C as a supplement.
This article explores how to engineer OpenClaw from a personal desktop assistant into a controllable, enterprise‑grade AI productivity platform by addressing multi‑tenant architecture, security safeguards, application integration, skill asset management, cost governance, and operational monitoring.
This article explains the design of OpenClaw’s Gateway – the control‑plane that unifies channel access, message routing, agent provisioning, distributed node execution, session isolation and hot‑reload configuration – and shows how each piece enables a scalable, governed AI‑agent platform.
This article walks you through why enterprise AI agents need a semantic ontology, explains TBox and ABox concepts, outlines a general modeling workflow, introduces RDF/OWL standards and tools like Protégé and reasoners, and provides a hands‑on example—including Python code with Owlready2—to create and test a business ontology for order‑expedition rules.
This article outlines six concrete criteria—relationship‑centric data, reproducible reasoning, evolving schemas, multi‑hop queries, explainable decisions, and cross‑system data integration—to help engineers decide whether a knowledge graph is the right solution or if a relational database will suffice.
This article dissects OpenClaw’s engineering techniques for building robust, production‑level AI agents, covering zero‑trust tool policies for security, markdown‑based memory management, cost‑aware reasoning levels, and controlled sub‑agent collaboration to ensure safety, efficiency, and reliability.
This article dissects OpenClaw’s core Agent engine, explaining its workspace layout, overall architecture, scheduling and concurrency mechanisms, high‑availability safeguards, and context‑guard strategies that together enable robust, production‑grade AI agents.
This article explains why AI‑assisted coding needs a disciplined, spec‑driven workflow, introduces the SDD methodology, outlines its core principles, tool ecosystem, step‑by‑step process, a hands‑on SpecKit + Copilot example, and the organizational changes required for successful adoption.
This article examines why most enterprise AI agents stumble—due to hallucinations, semantic mismatches, and lack of explainability—then introduces ontology as a semantic layer that structures business concepts, rules, and constraints to enable reliable reasoning, centralized rule management, and transparent AI behavior.
This guide walks through the MemOS open‑source memory framework, showing how to create a graph‑based memory ChatBot, add and retrieve memories incrementally, enable asynchronous memory reorganization, and seamlessly integrate the system into LangChain 1.x agents using middleware.
This article explains the order‑blindness of Transformer self‑attention, why naïvely adding raw position indices harms semantics, and walks through sinusoidal, learnable, and rotary positional encodings together with PI and YaRN techniques for extending sequence length.
