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20 个 Jev 技能仓库盘点:单次路由决策约 $0.00003

作者按五层结构梳理 20 个 Jev 相关仓库(共 28.4k star),并分享自己为客户做决策系统时单次路由成本约 0.00003 美元、比前沿模型便宜约六成。

BLAZT
@BLAZT_Ai2026.09.26收录原推发布于 2026.09.26
BLAZT @BLAZT_Ai原推发布于 2026.09.26
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I forked one of these twenty repos for a client's decision system, and the routing cost came in at $0.00003 per turn, cheap enough that nobody even asked about scaling it. 20 f*cking useful JEV skills, 20 repos, 5 layers, 28.4k stars combined, snapshot September 2026. One judgment model small enough to sit inside a live loop, returning a typed verdict instead of a paragraph nobody wants to parse at inference speed. Five layers laid out top to bottom: Primitive, 21 repos, typed verdicts not prose, jev-mcp, jev-codex-router, typesafe-mcp, semdecide. Context Gate, 3.4k stars, gates context and done, fast-jev-compaction leading at 3.4k, jev-review, Canny, and winnow sitting at the bottom with zero stars, clearly the newest addition. Surface, 6.9k, browser, desktop, code, graph, jev-ultrafast pulling 5.9k on its own. Tight Loop, 39, decisions inside milliseconds, typesafe-mario, jev-trader, jev-drone, prism-liquidity-agent. Verdict, 18.0k, the answer is the product, json-render carrying nearly all of it at 18.0k stars, sitting next to killmyidea, OneVOneJev, jev-curate. Every node traces back through connecting lines to one center block labeled JEV, system, one model, and the line underneath explains exactly why that matters: the big model still writes the code, this one only answers yes, no, or which, fast enough to ask every single turn. 2.5Hz cited as a live example, a quadrotor picking its next move mid-flight, camera only. That's the part that actually closed the deal on my end. Not the star count. $0.00003 per routing decision, about 60% off what a frontier model call would have cost for the same yes-or-no question, run thousands of times a session instead of once.

x.com/blazt_ai/status/2103577140365029407采集于 2026.09.26 10:13