What does competition in the world of music look like without copyright? We may be on the cusp of finding out.

Trying to decide which open hardware license to use? Not super interested in actually reading the text of licenses? You have come to the right place. There are a bunch of caveats at the bottom of this post, but let’s get to the heart of things.

Last week the GLAM-E Lab released a new report Are AI Bots Knocking Cultural Heritage Offline?. The short answer seems to be “yes”. The longer answer fills a report.

A team just announced the release of the Common Pile, a large dataset for training large language models (LLMs). Unlike other datasets, Common Pile is built exclusively on “openly licensed text.” On one hand, this is an interesting effort to build a new type of training dataset that illustrates how even the “easy” parts of this process are actually hard. On the other hand, I worry that some people read “openly licensed training dataset” as the equivalent of (or very close to) “LLM free of copyright issues.”

Moving from NYC to Berlin gave me an excuse to update my old Pi-Powered MTA Subway Alerts project for the BVG. Now, as then, the goal of the project is to answer the question “if I leave my house now, how long will I have to wait for my subway train?”. Although, in this case, instead of just answering that question about the subway train, it also answers it for trams.