xpost·Content and growth
1,315 posts across eight dimensions
Jev classified 1,315 X posts for about $0.086 in estimated model cost 😂 seeing everyone's Jev demos made me want to build something for my own content research. i'd collected a lot of posts, but figuring out what they had in common still meant opening them one by one and taking notes. so i built a dashboard around Jev. it labels each post across 8 dimensions, including topic, hook and writing style. now i can filter by topic and hook, compare engagement, and open the original posts to see the examples behind each pattern. my archive is a lot easier to learn from now.
Open X (@yuhasbeentaken) ⚡ ~$0.086
Yum⋆₊˚
@yuhasbeentaken
Jev classified 1,315 X posts for about $0.086 in estimated model cost 😂 seeing everyone's Jev demos made me want to build something for my own content research. i'd collected a lot of posts, but figuring out what they had in common still meant opening them one by one and taking notes. so i built a dashboard around Jev. it labels each post across 8 dimensions, including topic, hook and writing style. now i can filter by topic and hook, compare engagement, and open the original posts to see the examples behind each pattern. my archive is a lot easier to learn from now.
Evaluate input state with calibrated confidence (latency: <100ms, cost: ~$0.086)
Jev sits between the incoming context and the next system action. Rather than generating lengthy, slow natural language that requires brittle regex parsing, Jev returns non-autoregressive, calibrated probabilities that downstream code can immediately execute.
01actionPrimary action
02confidenceConfidence score
03fallbackFallback route
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