@SUOHA_AI compares Jev and DeepSeek on matching 15 brands to PR hooks in a live news stream and tagging intent. They say Jev finished all 428 items in 28 seconds while DeepSeek V4.1-flash had done 6 of 428. They attribute the gap to Jev returning a decision in one pass instead of writing text, and point to an Elvis Sun GitHub demo.

梭哈.AI (@SUOHA_AI) published this on 2026-09-18. @SUOHA_AI compares Jev and DeepSeek on matching 15 brands to PR hooks in a live news stream and tagging intent. They say Jev finished all 428 items in 28 seconds while DeepSeek V4.1-flash had done 6 of 428. They attribute the gap to Jev returning a decision in one pass instead of writing text, and point to an Elvis Sun GitHub demo. 完全睡不着了....这个视频让你完全看懂JEV的恐怖能力 我实际演示了一下，用 JEV 和 DeepSeek 做同一个任务：从当天的海量实时新闻流里，为 15 个品牌快速匹配有没有适合借势的公关热点，并打上结构化意图分类标签 结果是：28 秒内，Jev 狂刷完了整整 428 条数据，并完成了全部的目标分解与分类；而同一时间跑在同样任务上的 DeepSeek V4.1-flash，才刚刚完成 6/428 条 速度被拉开了几十倍，答案甚至在你读完第一行字之前就已经返回了.... How Jev or TypeSafe is used, in the post's own words: “完全睡不着了....这个视频让你完全看懂JEV的恐怖能力 我实际演示了一下，用 JEV 和 DeepSeek 做同一个任务：从当天的海量实时新闻流里，为 15 个品牌快速匹配有没有适合借势的公关热点，并打上结构化意图分类标签 结果是：28 秒内，Jev 狂刷完了整整 428 条数据，并完成了全部的目标分解与分类；而同一时间跑在同样任务上的 DeepSeek V4.1-flash，才刚刚完成 6/428 条 速度被拉开了几十倍，答案甚至在你读完第一行字之前就已经返回了....” The post does not name a separate audience. It is for people reading what @SUOHA_AI shows here (it links to github.com; it quotes @SUOHA_AI; it includes media).