The July 28 MCP specification eliminates sessions, removes the initialize handshake, and makes every request self-contained. A technical breakdown of what changed, why it matters for load balancers and multi-tenant deployments, and how to migrate existing servers.
How the central question of building with AI has shifted three times in four years — from 'what should I say?' to 'what system should I build?' A deep dive into prompt engineering, context engineering, and harness engineering with architecture diagrams, decision frameworks, and production lessons for engineering leaders.
Complete hands-on guide to building a production-ready agentic AI system. From project setup to deployment — every layer implemented with working code, tests, and Docker compose.
A complete technical guide to building a profitable agentic AI system using only open-source tools — with retrieval, orchestration, tool use, and observability. Includes architecture diagrams and real cost analysis.
Anthropic's Model Context Protocol hit 97M installs and joined the Linux Foundation. As a Technical Lead, here's what this means for how we build AI systems today and tomorrow.
Anthropic's Model Context Protocol crossed 97M installs in March 2026. As a Technical Lead who's been building with MCP since early days, here's what that milestone actually means — and why every developer building AI systems needs to understand it now.
The Model Context Protocol crossed 10,000 published servers under the Linux Foundation's Agentic AI Foundation. As someone who's integrated dozens of AI systems, here's why this number matters more than any benchmark.
March 2026 marks a turning point — AI coding tools evolved from assistants to autonomous agents. As a Technical Lead, here's what you actually need to know to navigate JetBrains Central, GitHub Agent HQ, Dapr Agents, MCP, and the multi-agent revolution.
A practical, hands-on guide to automating Developer, QC, and Tech Lead workflows across three maturity levels — Manual, Agentic (Claude Code), and Fully Autonomous (NanoClaw). With mermaid diagrams, real configs, BDD examples, and step-by-step instructions you can implement today.
Most people are stuck in Phase 1 of AI — asking questions and doing work themselves. Phase 2 is Agents: you set a goal, the agent delivers results. Here's the complete breakdown of what changed, how it works, and why it matters.
How to build a coordinated Claude agent team: defining agent skills, assigning responsibilities, orchestrating multi-agent workflows, and collaborating across agents to deliver a real software product from idea to production.
What MCP is, how to configure servers in .mcp.json, the top servers by category (GitHub, Playwright, databases, search, design, cloud), performance pitfalls, and why you should limit yourself to 2-3 per project.
A complete guide to connecting Jira, Linear, GitHub, and Playwright MCP servers with Claude Code to auto-generate BDD tests from ticket acceptance criteria, execute them against a live app, and report results back.
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