Hi HN, i have ben building a CLI first framework for exploring sematic structures in data. instead of just applying statistical or ML models, it tries to uncover hidden connections, entities and pathways across datasets.

What TensorPack does today: Discovers hidden semantic connections and pathways between datasets Supports entity search across datasets (not just within them) Extends dynamically with domain-specific transforms, letting users add their own semantic knowledge at runtime GitHub repo: https://github.com/fikayoAy/tensorpack

How this differs from existing tools: It overlaps with knowledge graphs, semantic web, and graph databases, but is:

CLI-first and designed to work directly with tensors, matrices, tabular data, and text

Built to integrate domain-specific transforms at runtime

Intended as a lightweight bridge between everyday data formats and a graph-style view of semantic relationships

I’d love feedback on:

Is this genuinely new, or just reframing existing approaches?

Where could this break (scalability, false positives, UX)?

Any use cases or datasets you’d want to see this applied to?

Screenshots and examples are in the repo. Thanks for taking a look!