3:00 AM. Another one of those nights where my brain decided sleep was overrated. After my usual nocturnal walk through the streets of a remote Scottish town—where even a fox observed me with that “humans are weird” look—I sat back down at my server. Just a quick scan of my RSS feeds, I told myself, then I can start work. When...

We backed up Spotify (metadata and music files). It's distributed in bulk torrents (~300TB), grouped by popularity. This release includes the largest publicly available music metadata database with 256 million tracks and 186 million unique ISRCs. It's the world's first “preservation archive” for music which is fully open (meaning it can easily be mirrored by anyone with enough disk space), with 86 million music files, representing around 99.6% of listens.

The news came from Anna's Archive—the world's largest pirate library—which had just scraped Spotify's entire catalog. Not just metadata, but also the audio files. 86 million tracks, 300 terabytes. I stopped to reread those numbers, then thought: holy shit, how big is this thing?

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.

There's a moment in the history of technology when everything changes. We don't always recognise it. Sometimes it takes years to understand that a small spark, an apparently insignificant detail, ignited a revolution that would forever change the way we live, communicate, and consume culture. In 1987, an American singer-songwriter named Suzanne Vega released a minimalist track called “Tom's Diner”. Two minutes and nine seconds of a cappella vocals, no instrumental accompaniment, no special effects. Just a voice telling the story of an ordinary morning in a New York diner. A song so essential, so pure in its simplicity, that someone on the other side of the world – a German engineer obsessed with #audio compression – would use it as a benchmark to create a technology that would shake the global music industry to its core. That technology was called #MP3. And that voice, that “warm a cappella voice” as Karlheinz Brandenburg would later describe it, would become the ultimate test to determine whether a compression algorithm actually worked or not.

From time to time, to completely disconnect from everything and everyone, I turn back into a kid and immerse myself in video games. I'm slow, I admit it: a game that would normally take 4-5 hours, I finish in at least quadruple the time. But every now and then, among the depths of Steam, I encounter genuine gems. And last night I finally completed Planet of Lana, a 2023 indie game that had been sitting in my library for months. The plot is straightforward but effective: Lana and Elo, presumably brother and sister, live in a peaceful fishing village built on stilts, where life flows serenely in harmony with nature. But this peace is shattered when a group of robots assault the village, kidnapping some inhabitants including Elo himself. From here begins Lana's odyssey: a journey to the edges of the known world to find and save her brother.

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.