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.
High Buzz Around the Jev Model
The author observes that discussion around the Jev model has been extremely intense in recent days, marking the highest density of original content in English Twitter feeds over the past six months.
These past couple of days, the buzz around this Jev model has been incredibly high—it's safe to say it's the highest density of original content I've seen since I started closely following English Twitter feeds over the past six months. As it happens, I've been running a small
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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.
