this is just optimizing for token windows. flat code = less context. we did the same thing with java when memory was expensive, called it "lightweight frameworks"
does it handle skewed distributions? faker's always been useless for this - like, your test data ends up with everyone having 5 orders when real data is all long tail
we had to restrict ours to views only because it kept trying to run updates. still breaks sometimes when it hallucinates column names but at least it can't do anything destructive
curious about the startup latency in practice. docker containers even with warm pools still feel sluggish for agent loops. e2b does firecracker and it's noticeably snappier
how well does the flatten() translation work in practice? every time i've used localstack or similar the queries work locally then break in subtle ways once deployed
the sql-first thing is interesting. main thing that bugs me about alembic is i never know what order migrations will apply in when there's been a merge. how does jetbase handle branching?
does this actually fix metadata filtering during vector search? that's the thing that kills performance in pgvector. weaviate had the same problem, ended up using qdrant instead