Blog

Artificial intelligence
Alessandro Fiori

The Future of AI: Professions Redefining Work (2026-2035)

As we close 2025 and enter 2026, artificial intelligence has already surpassed the experimental phase. 96% of large Italian organizations are implementing AI solutions. By 2030, 1.7 million new jobs will emerge in Europe in the AI sector alone. But what will these professions be? And what does it really mean to work with—not against—AI in the next decade?

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Artificial intelligence
Alessandro Fiori

Teachable Machine: Learn Machine Learning in 10 Minutes (2026 Practical Guide)

While 72% of US enterprises now use machine learning as standard IT operations, most people still see it as “magic” for PhDs only. Google’s Teachable Machine demolishes this barrier, letting anyone build functional AI models in under 10 minutes—no code, no math degree, no expensive infrastructure. Just a webcam and curiosity. Here’s how this tool democratizes the $192 billion ML market.

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Artificial intelligence
Alessandro Fiori

Vibe Coding: The End of Traditional Programming?

Imagine creating a complete app by simply describing what you want. No syntax, no mysterious bugs, just ideas becoming code. Welcome to vibe coding—where AI generates 40% of global code and teams of 10 build $100M startups. Andrej Karpathy defined it as “programming by abandoning to vibrations.” Silicon Valley calls it the next revolution. Is it really the end of traditional coding?

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Artificial intelligence
Alessandro Fiori

Generative AI 2025: The Revolution Creating Content from Nothing

Over 200 million people already use generative AI. But do you really understand how it works and why it’s changing everything? It’s not just ChatGPT answering questions: generative AI creates text, images, music, and code from scratch. Discover how these models transform ideas into concrete content and why your generation will use AI like your parents use Google.

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DBMS
Alessandro Fiori

What is a vector database?

Vector databases are designed to store and search high-dimensional data, such as embeddings of text, images, or audio. These tools are critical in artificial intelligence and machine learning applications, as they enable semantic searches, recommendation engines, and RAG systems. With techniques such as Approximate Nearest Neighbor (ANN) and similarity metrics such as cosine and Euclidean distance, they ensure high performance even with large volumes of data. Although they are very powerful, they present challenges, such as complexity in management and integration with legacy systems.

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