crystal VS orange

I didn't see a v5 tag in order to know, and I have no idea what "utils/graphile" does for the project, but one will want to ensure they are aware of its licensing scheme https://github.com/graphile/crystal/blob/db8894c74eb0ec3fe96...

I was about to say “but this one is!” and realized I had confused PostgREST with PostGraphile. If you’re interested in GraphQL, you can check out PostGraphile here: https://github.com/graphile/postgraphile

If you point is to abstract all the CRUD/GraphQL application, Go isn’t needed. You can go with PostgREST or Postgraphile.

What do you mean locally? Hasura is OSS, and you can run it locally (you have autogenerated SQL statements) Here you can just use Nhost and its CLI;

Alternatives are https://github.com/graphile/postgraphile or dgraph as you mentioned. Hasura is working on support for sqlite, so you may have some blockers there, you can also look into the Prisma engine which has GQL as an intermediate (for resolvers, for example)

I've personally found Postgraphile to be fantastic. Nicer to use than Hasura and fully OSS: https://github.com/graphile/postgraphile/

Hi all, this sounds very cool. How does pg_graphql compare to Postgraphile? https://github.com/graphile/postgraphile (besides I guess running in the DB with PLpgSQL instead of as a NodeJS server)

Did you think about integrating Postgraphile with the Supabase ecosystem or have specific limitations with it?

Thanks!

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.