I am a Director of Engineering at Decathlon Digital, where I lead teams in building global omnichannel experiences. My leadership philosophy is rooted in Happiness-Driven Development, autonomy, and radical technical excellence.

• 🌍 Scaling Platforms: Orchestrating high-traffic systems across the global Decathlon ecosystem.
• 🧘 Culture First: Believer in psychological safety as the primary driver for high-performing teams.
• 🧪 Vibe Coding: Experimenting with AI-orchestrated development and "Zero-to-One" prototyping.
• 🧰 AI Toolkit: Claude Code · Cursor · GitHub Copilot · MCP Toolbox — actively shipping with them.

Inspired by the Sivers Now page concept.

• 🛡️ Cyber Code Academy: Building a secure Python sandbox for educational purposes.
• 🏠 Smart Home: Enhancing my Hitachi CSNET Home integration for the Home Assistant community.
• ✍️ Writing: Sharing architectural deep-dives on blog.mornati.net.

Backend & Architecture	DevOps & Cloud	Automation & Fun

• The AI Orchestrator: Why Intelligent Delegation is the Missing Piece in Your AI Toolchain — 1. Introduction: The Age of Model Abundance The AI assistant landscape in mid-2026 is one of abundance. According to McKinsey's State of AI report, 78% of organizations now use AI regularly, and the n
• Lifting the Lid on Copilot's Black Box: Observability for LLM Code Generation — Introduction: The Black Box of AI Code Generation When you ask GitHub Copilot to write a function, refactor a module, or explain a complex piece of code, the response you get is the output of a probab
• Your AI Agent Deserves a Tool Harness, Not a Wild West — We started the same way everyone does: give the LLM access to everything and hope it figures it out. Connect the GitHub MCP, the Jira MCP, the internal product API MCP, throw in a database schema or t
• The Hidden Tax on Every AI Request: How MCP Servers Are Draining Your Token Budget — Last month, I published a comparison: MCP Servers vs. CLI. Single server (GitHub), controlled test, clear conclusion: Native MCP wastes 99.7% on schema tax in typical sessions. But that's a lab test.
• The Future of Agentic Tooling: MCP Servers vs. CLI A Data-Driven Comparison — As Large Language Models (LLMs) evolve into autonomous coding agents, one of the most consequential architectural decisions is deceptively simple: how should an AI agent talk to external services? Tra

💬 🚀 The LLM Zoo in 2026: How not to go crazy? Today, opening your AI toolbox means facing a dizzying abundance: GPT-4.1, Claude 4 Sonnet, DeepSeek-V3, Gemini 2.5... Choosing the right model for the right task has become a software engineering problem in itself. ❌ The problem: Manual selection is unmanageable. Worse, burning a super-premium, expensive model to format a simple docstring...

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