Perhaps what separates the good companies from the Great companies is that the latter always tries to make it works, doesn't rest on it's laurels, and takes any bet which is +ev.
Appreciate the feedback. I shall certainly revamp the README; it is rather stale.
> "how Modolap differs from just asking AI to use any other OLAP engine"
There presently exist two components, the OLAP query engine and the remote infrastructure service. The service enables systems like Codex (or developers as well) to manage datasets, maintain version control over queries, and offload the computational burden to dedicated machines. This is especially beneficial given the current trend of running agents inside micro-VMs.
In addition, it is designed with AI usage in mind. There is significant value in co-design. One could argue that models can use Polars or DuckDB just as well, and that there is no room for improvement, but I do not think this is true.
Pandas is terrific, yet even its original author has noted inherent shortcomings [1], and there exist alternatives.
Polars seems to be the most prominent competitor in the Python DataFrame space, and DuckDB appears to pursue an approach similar to SQLite, but columnar.
I am personally working on a solution to a broader problem, which can also be viewed as an alternative to Pandas [2].