cleora VS dog

Our team at Synerise AI has open sourced Cleora - an ultra fast vertex embedding tool for graphs & hypergraphs. If you've ever used node2vec, DeepWalk, LINE or similar methods - it might be worth to check it out.

A few weeks ago, our team at Synerise AI has open sourced Cleora - an ultra fast vertex embedding tool for graphs & hypergraphs. It is a tool, which can ingest any categorical, relational data and turn it into vector embeddings of entities. It is extremely fast, while offering very competitive quality of results. In fact, it may be the fastest hypergraph embedding tool possible in practice, without intentionally discarding/reducing input data.

Thanks for raising so many interesting points about model performance and complexity. In this context, I think our newly released graph embedding library - Cleora - might be of interest: https://github.com/Synerise/cleora Cleora has some nice performance-wise properties:

Synerise/cleora (Rust): Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.

Dog is cross platform and has some nice features, like json output.

https://dns.lookup.dog/

> As a user of the "--color" flag for the `ip` command, I'd love to see tools like dig produce more modern output

https://github.com/ogham/dog is pretty good in that regard

Alternatively, use dog (https://github.com/ogham/dog)

> dog www.readlang.com

A www.readlang.com. 1h59m16s 139.144.234.197

There's this, which is a more modern dig, with color output, among other things: https://github.com/ogham/dog

There's also stuff like this, which will postprocess & color output from any command: https://github.com/garabik/grc, or https://github.com/armandino/TxtStyle

Here is link number 1 - Previous text "dog"

There's also doggo, which you can say is a bit of a dig at dog.

See also "dog" which I've been using for a while, works well. https://github.com/ogham/dog