Public data reservoirs are powering AI-ready search. The Global Database of Society (GDelt) maps global signals at scale, giving AI systems a public backbone for retrieval [1].
That breadth helps AI fetch context across governance, media, and society—precisely the kind of signals that fuel AI-powered search workflows [1]. The public nature of GDelt's data makes it a natural substrate for testing, benchmarking, and cross-domain retrieval use cases [1].
On speed, Hugging Face lays out a path with a 'Lightning-fast vector search for legal documents' guide. It benchmarks embedding APIs, shows sub-millisecond retrieval on 143k chunks using only CPU, and digs into embedding API terms of service as a real-world constraint when pairing with USearch [2]. It even contrasts local vs hosted models to map trade-offs for production setups [2].
On language, dbDialog lets you query your database in plain English, no SQL needed. It generates and runs SQL behind the scenes, returns results instantly, and stays privacy-first so no data leaves your DB or is exposed to external AI; masking layers guard data from LLMs [3].
Together, these threads show how public datasets, embedding-powered search, and natural-language querying can fuel AI-ready retrieval across domains—while governance questions like terms of service and privacy safeguards stay front and center [1][2][3].
References
A Global Database of Society
Link to GDelt's global society database; describes a large, multi-source socio-economic dataset for events, language, tone analysis.
View sourceBuilding Fast Vector Search for Legal Documents
Benchmarks embedding APIs speed; local vs hosted models; tuned USearch for sub-millisecond CPU retrieval on 143k chunks; TOS considerations insights
View sourceShow HN: dbDialog – Query your database in plain English (no SQL required)
dbDialog lets users query databases in plain English, generating SQL automatically, with privacy safeguards and no external processing.
View source