Wikipedia link race: choosing among thousands of options
The model chooses the next step from hundreds or thousands of links, demonstrating fast decisions with constrained outputs. The post includes a 4:04 video.
Ranking NYC Places with Jev Based on Google Maps History
The author built a Karpo experiment with Jev that learns from Google Maps history to rank 1,253 NYC places by personal taste.
I gave Jev 1,253 NYC places and one job: figure out which ones you’d actually love 👀 Made a little Karpo experiment that learns from your Google Maps history, then ranks the entire Google Maps list based on your taste. Jev from @typesafeai is kind of perfect for this:
· 5 likesOpen on X
More like this
Semantic Querying Use Case with Jev and TypeSafe AI
Nathan LeClaire shares a semantic querying use case for Jev and TypeSafe AI, noting people have only scratched the surface.
Building a live BS meter for the Trump vs Kamala debate with Jev
The author used Jev to run a live BS meter on every sentence of the Trump vs Kamala debate, with 5 yes/no questions per candidate, totaling 1,191 Jev calls, 1.18M tokens, 415 ms median latency, and $0.0497 total cost.
