It was an ordinary Friday evening. The parcel had arrived with the courier that morning, but I only opened it after dinner, with that silent ceremony I perform every time new hardware shows up – as if opening a box too quickly were a form of disrespect toward the object. Inside was a HUNSN 4K. Small, almost ridiculously small. A mini PC in a form factor that fit in the palm of a hand. I put it on the table, looked at it. Looked at it again. And then an uncomfortable thought occurred to me. I had ordered it from a Chinese reseller, paid with a credit card, through a completely traceable payment infrastructure, from one of the most centralised and surveilled commercial ecosystems in existence. To build a homelab that would let me escape centralised and surveilled ecosystems.

A hackmeeting, many years ago. A conference on various open-source projects. They were talking about Kiwix. The audience seemed interested, nodding, asking questions. I sat in the back of the room with a doubt that seemed legitimate but that I didn't dare express out loud: “what's the point of offline Wikipedia?” I mean: the internet is everywhere. If you need to look something up on Wikipedia, you open your browser, search, read. Done. Why would anyone download gigabytes of data to consult an encyclopedia offline? It seemed like a solution in search of a problem. Something for nerds nostalgic for CD-ROM encyclopedias.

It took me years to understand how naive I'd been.

When the world woke up astonished in November 2022 to this “magical” chatbot, few realized that this magic was the result of decades of research. The history of artificial intelligence begins in 1943, when Warren McCulloch and Walter Pitts proposed the first mathematical model of an artificial neuron. In 1956, at the Dartmouth Conference, John McCarthy coined the term “Artificial Intelligence” and the discipline was officially born.

The '60s and '70s were characterized by excessive optimism: people thought strong AI was just around the corner. Two “AI winters” followed – periods when funding disappeared and research slowed – because promises weren't materializing. But some continued working in the shadows. Geoffrey Hinton, Yann LeCun, Yoshua Bengio – those we now call the “godfathers of deep learning” – continued their studies on neural networks when no one believed in them anymore.