pandas has a .pipe operator which works exactly like this

I'm building https://www.ergodic.ai - and we are using a graphs as the primary objects in which the intelligence operates.

I don't think every graph needs a graph database. For 99% of use-cases a relational database is the preferred solution to store a graph: provided that we have objects and ways to link objects, we're good to go. The advantages of graph dbs are in running more complex graph algorithms whenever that is required (transversal, etc) which is more efficient than "hacking it" with recursive queries in a relational db.

For us, I've yet to find the need for a dedicated graph db with few exceptions, and in those exceptions https://kuzudb.com/ was the perfect solution.

> which is more efficient than "hacking it" with recursive queries in a relational db

It seems to me that the way recursive CTEs were originally defined is the biggest reason that relational databases haven't been more successful with users who need to run serious graph workloads - in Frank McSherry's words:

> As it turns out, WTIH RECURSIVE has a bevy of limitations and mysterious semantics (four pages of limitations in the version of the standard I have, and I still haven't found the semantics yet). I certainly cannot enumerate, or even understand the full list [...] There are so many things I don't understand here.

https://github.com/frankmcsherry/blog/blob/master/posts/2022...

> For 99% of use-cases a relational database is the preferred solution…

After enough years you realize this is the case for every single problem

"After trillions spent in GPUs and data centers, the AI gold rush was finally over when a developer in Lithuania built the pg_thinking plugin - turns out postgres was all you needed all along."

would you consider needing to do community detection as a reason for using graph over relational?

Lol, considering that the entire pricing and risk system of the company runs on a proprietary programming language, I'm pretty sure this is just publicity

I'm pretty sure this is a PR piece, too. In complete fairness, I've heard that they're doing a lot more Python lately.

Not only that. I have an agent product and I’m currently blocked from using their reasoning models on Azure for having asked for a chain of thought, which apparently is against the ToS.

The customer service itself was surreal enough that it was easier just to migrate to Anthropic

Yes, (ergodic.ai) working on causal inference applied to process mining and event logs. Basically: something happened, why did it happen and how can I avoid it/get more of it?

Why? Replace the context and not having that property is now called a hallucination.

Overall the model is tra

It's a question of timelines. While I agree with Amazon, we know pretty well the periods in which Rome and London have been inhabited, but the question is more about understanding pre ice-age human settlements, of which we know nothing about because these are more likely submerged now.

Not sure about squids in particular, but the easiest way isn't necessarily to find remains, but to see negative prints on sediments.

Tons of fossils are "negative prints on sediments". GP's assertion that "If humans didn't exist while they do, we would have never known about them" is clearly wrong.

Also a type of collider bias in causal inference, which generates all sorts of Simpsons paradoxes

Are we still using real terminology?

Simpson’s paradox is a real thing in medical sampling. As far as the rest of it, who’s to say?

Berkson’s paradox is also real.

Can I ask you to beta test my product? I'm building something like this and I want to focus on medical data (from omics to RCTs)