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.
Testing Jev for Search Reranking
The author tested TypeSafe's Jev for search reranking on 33,047 entries from Agent Skills Hub, noting it alone doesn't beat vector retrieval.
Using Jev for search reranking, let me first pour a bucket of cold water: on its own, it doesn't beat vector retrieval TypeSafe's Jev has been super popular lately, with a bunch of projects using it for reranking. We did a serious test on the 33,047 entries in Agent Skills Hub,
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Semantic Querying Use Case with Jev and TypeSafe AI
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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.
