This was #BYOTA, thanks for following this long thread! What you see here are some of the next steps I planned: in addition to natural ML extensions, I’d like to see it grow as a tool for people to experiment with different algorithms and easily share them, and for less tech-savvy people to use as easily as possible. For this to be true, I will invest some time in understanding how to bring this to fruition at protocol level, rather than a single application. Stay tuned!

So, what can you do with #BYOTA? The first thing is embeddings visualization. In these pictures you can see a 2D plot of embeddings calculated on four different timelines: home (blue, only people I follow), local (orange, all posts from my instance, which is fosstodon.org), public (red, federated posts from people followed by users of my instance), and the timeline that I got searching for the #gopher hashtag (light blue).

What does having a WASM-powered notebook mean? Consider #BYOTA: you can download it from its repo, pip-install its deps, and run it locally as any python notebook. But you can also deploy it as HTML+Javascript files, host it somewhere super cheap (coz the server won’t run any of your code), and people will be able to run it in their browser with no need to install anything else! Plus, this will work both with “my” algorithm and whatever alternative you might develop starting from BYOTA’s code.

In #BYOTA, we use #llamafile to calculate *sentence embeddings*.
If you don’t know what embeddings are, just think about them as numerical descriptors of your Mastodon statuses, which are closer (as in two cities’ coordinates being close on a map) the more semantically similar their respective statuses are. We’ll get back later to this with a more visual description. If you are interested in embeddings and wanna delve deeper, see https://vickiboykis.com/what_are_embeddings/ by @vicki.