SEARCHSearch & retrieval
Metaview JEV Integration
Shahriar Tajbakhsh@s16h_
2:00This project integrates JEV into Metaview agents, enabling faster and more cost-effective candidate searches by treating intelligence as software, allowing agents to be broken down into semantic units for querying and bug fixing.
at @MetaviewAI, over the weekend we shipped @typesafeai's jev into every agent on metaview.
candidate searches in our sourcing product that took minutes now take seconds. same accuracy, ~10x faster, meaningfully cheaper per search.
but the speedup isn't the interesting part. jev let us treat intelligence like software: break each agent into its smallest semantic units, query each one, set our own thresholds, and fix bugs by adding a question — not by begging in a system prompt.
that's a different way to build, and it's now our baseline. credit to the typesafe team.
optimizing for intelligence per dollar has unintuitive consequences. intuitive to @CompleteSkeptic, of course.
Related builds
Vectorless RAG with Jev
This project introduces a novel approach to Retrieval Augmented Generation (RAG) that replaces traditional embedding methods with a structured, table-of-contents-based navigation system, significantly improving speed and reducing costs by offloading non-generative tasks to Jev.
