Koinju.io is exploring SQL access to crypto market data as an alternative to REST APIs, motivated by the observation tha
Iām Nazim, founders of Koinju.io and I wanted to share here an exploratory option we opened very recently: providing access to our database, which contains all cryptocurrency market data, via SQL. REST give access for direct retrieval but we're thinking more and more that SQL access for analytical work over a unified crypto market data layer could be of something because of llms.
This was partly triggered by Didier Lopes, ceo of OpenBB recent essay on financial firms owning the infrastructure where financial work happens (https://www.linkedin.com/pulse/how-did-we-end-up-here-didier-rodrigues-lopes-hgeqe/ ), especially the runtime where workflows execute and AI inference happens.
Most data APIs were designed for software that already knows what it wants. Call an endpoint, get JSON, parse it, compute somewhere else. That model worked great and still works great. But Iām not sure it maps well to llm-driven workflows, especially with big data.
A language model can call APIs /read JSON or write python to do so (claude code can force json output). But that does not mean the model is efficient in ingesting, reshaping, joining, aggregating, validating, or reasoning over large structured datasets through tokenized rows. At small scale, it fit within context limit. At large scale, it becomes complexe and small details may disappear silently, as if they were outliers...
So the thesis we are testing is: For big datasets, the AI-facing p