I happen to have a very similar idea recently and created this GPT-logic package for node. It basically transforms GPT generated results into JS data types. Check it out if you are interested. https://github.com/wayneshn/gpt-logic
I think relational databases are capable of doing everything that graph DBs are capable of. But under some certain scenarios, graph DBs are just more efficient. For example, in fraud detection and community discovery.
In China, where most people are more familiar with Chinese characters, or hanzi, Wordle fans have invented a localized version of Wordle with a very clever name: Handle.
In this article, my colleague Wey Gu explored a new way of solving Wordle-like games. He indexed all possible answers of the Chinese Wordle in a knowledge graph and found the shortest path to the final answer.
Hi HN, we are launching today the beta version of Nebula Graph Cloud, an out-of-the-box graph database solution in the cloud. While Nebula Graph is an open-source graph database, maintaining a Nebula Graph cluster in production can be a pain. That's why we introduced Nebula Graph Cloud, which allows you to launch a Nebula Graph cluster within in just a few clicks and maintain it in an intuitive users interface.
A graph database is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. Graph databases are especially good at discovering hidden connections in unstructured data.
Nebula Graph Cloud is currently in beta and is offering a generous 70% lifetime discount for beta users. If you are interested in giving graph technology a try, please sign up for the beta here: https://nebula-graph.io/nebula-graph-cloud/
