Paper maps early Jev ecosystem usage against attention
Someone scraped the entire early Jev ecosystem to compare how each domain actually uses it against GitHub star distribution, finding routing projects take 63% of stars but only 19.6% of projects.
if you're picking where to build with Jev, this paper is basically a treasure map. someone scraped the entire early Jev ecosystem and plotted what people actually do with it vs where the attention is. two things jump out > first - Jev isn't one tool, its job changes by domain. in interface agents and simulation, "action selection" is the main use (47% and 52% of decisions). in routing, a quarter of all decisions are the model picking which model or tool to call next (27%, vs just 6% in software engineering). > but across six of eight categories, the top job is plain attribute judgment - "is this thing what it claims to be." same model, completely different role depending on where you drop it. > second - and this is the opportunity - attention is wildly lopsided. Routing & Automation and Interface Agents are only 19.6% of all Jev projects but pull 63% of the GitHub stars. > the biggest categories by volume, Content & Expert Tasks and Search & Memory, have ~387 and ~381 projects but average just 31 and 37 stars each. > the sharpest line in the whole thing: Routing and Simulation have almost identical project counts (250 vs 252) and average 364 vs 8 stars. same amount of work, 45x the attention. takeaway for a builder: the crowd is piling into routing. the big, under-starred categories - search, memory, expert tasks - are where the room actually is
This post quotes another post: “Long read: how to build a Jev-powered agent stack”