Expert perspectives on enterprise AI, governance, platform engineering, and digital transformation.
221 articles
Introducing Claude Mythos, Anthropic’s restricted frontier model for advanced cybersecurity, coding, and scientific research. Learn how Claude Mythos works, why access is limited through Project Glasswing, what Mythos 5 costs, and what enterprise security, privacy, and governance risks organizations should evaluate before adoption.
Jenkins MCP Server connects Jenkins CI/CD pipelines with AI agents through the Model Context Protocol, giving MCP clients structured access to Jenkins jobs, builds, build logs, artifacts, test results, queue items, and pipeline status. This guide explains how Jenkins MCP Server works with the Jenkins API, how AI coding agents and DevOps teams can use it for build failure investigation, log search, test-result review, artifact retrieval, and pipeline troubleshooting, and what security controls are needed, including API tokens, least-privilege permissions, read-only access, monitoring, and human approval for production workflows.
![Langflow Tutorial: Build an AI Agent With RAG [Step-by-Step Guide]](/_next/image/?url=%2Fuploads%2Fbuild-an-ai-agent-with-langflow-codeconductor-145b9f67-800.webp&w=3840&q=75)
Learn how to build an AI agent in Langflow with tools, RAG document search, API access, and web app integration. This tutorial covers setup, LLM configuration, Chroma DB retrieval, testing, debugging, production deployment, and how CodeConductor helps turn agent workflows into complete AI applications.
Learn what Stirling PDF is, how it works, and how teams use it for self-hosted PDF processing, OCR, API automation, pipelines, secure deployment, and repeatable document workflows.
MCP semantic search helps AI applications retrieve relevant context without loading an entire codebase, knowledge base, or message history into every prompt. Learn how MCP, session management, semantic search, RAG, and persistent memory work together in production AI systems.
Graphify helps developers improve AI-assisted coding by mapping repository relationships into a queryable graph. This guide explains how Graphify works, where it helps, its limits, and when source inspection is still required.
Compare the best MCP servers for Claude Code by real development use case, including repository intelligence, GitHub workflows, current documentation, browser testing, database analysis, production debugging, web research, and team knowledge. Learn where Harmony MCP, GitHub MCP, Context7, Playwright, PostgreSQL, Sentry, Firecrawl, Slack, and Notion fit, and how to avoid overlapping tools, excessive context usage, and unnecessary permissions.
Installing Graphify involves two steps: install the official graphifyy package, then register the platform-specific skill with Claude Code, Cursor, Aider, Codex, or another supported coding assistant. Once configured, Graphify gives the assistant a structured knowledge graph of your repository, helping it trace dependencies and reduce repeated file searches.
![What Is Claude Code? Features, Setup, & Limits [Guide]](/_next/image/?url=%2Fuploads%2Fclaude-code-codeconductor-2f4c289e-800.webp&w=3840&q=75)
Claude Code is an AI coding agent that helps developers inspect files, edit code, debug issues, review changes, and document projects from inside a local codebase. Learn its key features, setup steps, use cases, limits, and how Harmony MCP helps preserve codebase context across sessions.
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