AI changes everything
This article analyzes the trade‑offs between Model Context Protocol (MCP) and Skills for Claude‑based AI workflows, offering a three‑question test, real‑world examples, and architectural diagrams to help engineers choose the most efficient approach for their tasks.
This guide explains how Claude Code Skills work, how to define them in Markdown with frontmatter, where to store personal and project skills, best practices for metadata, description crafting, priority rules, complex skill organization, and how they differ from CLAUDE.md, Hooks, and Subagents.
The article examines the hidden governance gap of powerful enterprise AI agents, shares real‑world failures from the OpenClaw platform, and proposes a practical AAAA (Access, Authority, Audit, Abort) framework to safely deploy autonomous assistants in production environments.
This article explains why engineering discipline is essential for modern AI agents, introduces the KubeClaw platform and its Kubernetes‑native architecture, provides step‑by‑step installation and Helm deployment instructions, and outlines proven operational patterns for secure, observable, and reliable agent systems.
The article analyzes the 2026 GitHub star‑ranking of the top 20 open‑source AI projects, highlighting a shift from model‑centric hype to practical agent execution, workflow orchestration, and data‑centric solutions, and examines the core capabilities of representative tools such as OpenClaw, AutoGPT, n8n, Dify, RAGFlow and Firecrawl.
OpenClaw is an open‑source, OS‑level autonomous agent runtime that combines dynamic system prompts, powerful tool access, file‑based memory, and sub‑agent generation, offering a secure, extensible architecture that runs on a single Node.js process and integrates with any LLM provider.
This article examines NemoClaw’s three‑layer architecture that adds kernel‑level sandboxing, policy‑driven deployment, and flexible inference routing to OpenClaw, outlines installation steps, compares it with the native OpenClaw runtime, and discusses current limitations for production use.
The article explains how an artifact layer—comprising specs, guidance files, skills, tests, and logs—preserves intent across AI agent sessions, enabling reliable, secure, and maintainable agent‑driven software development through spec‑first practices, bounded loops, and robust verification stacks.
This article explains how to transform separable nonlinear programming models into piecewise‑linear (PWL) approximations, use SOS‑type 2 constraints, and implement the whole workflow in Python with Gurobi, illustrating the method with a portfolio‑selection example and showing how breakpoint refinement improves solution accuracy.
This article examines a three‑layer AI‑powered code review system built around Claude, comparing GitHub Action, a custom sub‑agent pipeline, and Anthropic's native review in terms of cost, detection depth, false‑positive rates, and practical deployment recommendations for mid‑size development teams.
