elk VS Weaviate

This has lead to a delightful variety of custom clients like https://phanpy.social/ - https://elk.zone/ is another example that I really like.

It's the complete opposite of the Twitter API situation, where they locked their API down and killed the entire ecosystem of third-party clients.

And there are tons of third party clients. I think Tusky is the best one I’ve seen for Android, and there’s an interesting web-based one called Elk that’s very nice. You load up https://elk.zone and then use it as a front-end to sign in to your server.

The answer to that is https://elk.zone atm, a fun and chef's kiss interface (built with nuxt). You can insert elk.zone/ before any Mastodon url. https://phanpy.social is also great, with multi columns even for lists.

A browser plugin (like "Mastodon – Simplified Federation") to follow, favorite, etc. directly on any server has also improved my experience a lot.

Quick! Tell Obama and Taylor Swift about https://elk.zone! They're going to be so excited!

Also try logging in through https://elk.zone/ if you want to stick with the familiar twitter interface

For me, the boost/reply-numbers using https://elk.zone have been much more reliable than the original app. (It's also the (web)app that convinced me. Mastodon is the real deal.)

Try elk.zone. You can log into the UI with an account from any instance. I use it as my main UI and it looks pretty close to twitter, at least close enough for me.

In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.

This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424

Link to GitHub -->

Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:

1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.

2. Parameter tuning. lowering `efConstruction` can speed up imports.

3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.

In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.