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.
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.
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.
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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.
Jev text classification: 6000x cheaper than Pangram
A user demonstrates TypeSafe's Jev for AI text classification, claiming it is close to Pangram but 6000x cheaper and 5-10x faster, tested on a Paul Graham essay. Link
