RAG is the aspirin of generative AI: everyone prescribes it, almost no one truly understands how it works, and when it fails, the patient hallucinates. After implementing RAG in production with the Ops Copilot and discarding it in favor of Tool Calling for the obsolescence radar, I’ve catalogued the 7 errors that destroy precision — and the solutions that actually work.
On August 2, 2026, most obligations of the EU AI Act come into force. Maximum fine: 35 million euros or 7% of your global turnover. And most articles explaining this regulation are written by lawyers, for lawyers. Not this one. This one is written by an engineer who already operates AI agents in production, translating every article of the regulation into the language we actually understand: architectures, pipelines, and code.
Today, every LLM has its own way of connecting to your databases and tools. It’s a chaos of proprietary integrations. MCP (Model Context Protocol) promises to end this by becoming the ‘universal plug’ of AI. We analyze if it’s truly the future or just another standard that will die trying.
Everyone talks about what AI can do. Almost no one talks about what AI costs to do. After more than 6 months operating autonomous agents in production with Gemini, CrewAI and GitHub Actions, I open the books and break down every euro invested — and every surprise I found along the way.
Engineers build complex systems but hate marketing. I built an autonomous agent army with Gemini to distribute my content. Here is the technical breakdown of what worked, the friction I encountered, and how it shapes my roadmap.
How do you explain a complex engineering process to a non-technical audience? I used Nanobanana’s AI and a Formula 1 analogy to turn the abstract V-Model into a visual roadmap that everyone can understand. Here’s the process.