全网爆火的JEV 上下文即时压缩策略，被狠狠开喷了...
This is a AI post classified by Jev as AI coding agents (an opinion), kept by the AI Radar because it carries real work, not commentary.
全网爆火的JEV 上下文即时压缩策略，被狠狠开喷了... 起因是前苹果工程师 Tamara @tamarajtran 开源了 fast-jev-compaction，主张彻底抛弃大模型写全文总结的做法，改用毫秒级小模型 Jev 给每条工具调用打分，没用的直接删，只留关键原文 然而前 Twitch 工程师Theo @theo 不认可这种做法..... • Tamara 的逻辑：大模型做总结经常胡说八道、严重漏细节，动不动就把具体报错、文件路径甚至“绝对不能改某代码”的硬规则给概括没了。用小模型瞬间过滤掉无用记录，不仅快，而且能完整保留关键代码 • Theo 的反驳：中途乱删历史等于自己坑自己 第一，现在的顶尖模型在后台有一整套加密的推理逻辑，你把中间调用删了，模型智商直接断崖式下跌； 第二，更致命的是账单！大模型缓存是按“前缀一字不差”来打折的（命中了便宜 90%）。你自作聪明把中间某一条记录删了，后面的缓存全部失效，全得按最贵的原价重新写入一遍，省了小钱却赔了大钱！ 不过我亲自实测了 JEV 的这个策略，发现确实有个致命的硬伤： Agent 跑了条命令报错失败，Jev 判定‘这行报错没用’，随手给删了。结果几轮之后，Agent 彻底失忆，根本不知道自己刚才试错踩过坑，转头又傻乎乎去跑同一行的错误命令…… 但换个角度看，Jev 的价值根本不仅限于压缩上下文....JEV
Posted by 梭哈.AI (32.7k followers) 1 days ago · 76 likes · 45.7k views · view the original post on X. Kept by the AI Radar as AI coding agents. Tools mentioned: fast-jev-compaction.
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