AYi on X: 你是不是也在推特收藏了上千个书签,最后只存不看,
@AYi_AInotes@AYi_AInoteslisted 48m agoreviewed by Jev你是不是也在推特收藏了上千个书签,最后只存不看, 开发者 Alex 用jev解决了这个痛点, 他刚上线的这个工具 Margin, 算是真正把你的书签收藏救活了, 以往尝试用传统大模型做个人书签检索, 体验几乎是灾难级的: 把几百条推文全文丢给大模型或者向量库, 每次搜一句话要在云端等上半分钟, 还要硬烧几块钱的调用费, 根本没人愿意天天用。 而接入专做强类型决策的 Jev 之后, 整个检索逻辑彻底被掀翻了。 你只要用扩展把这几年积攒的书签一键导出导入工具, 接下来直接用最口语化的人话去搜, 比如问它有哪些关于冷启动获客的实操技巧。 系统完全不需要自回归逐字生成废话, 而是由底层的决策小脑以单条几十毫秒的速度在后台做并行打分。 短短几秒钟之内, 上百条沉睡了几年的推文被挨个计算出匹配置信度, 界面带着进度条瞬间弹出一份按相关性精准排序的推文清单, 甚至把每条内容到底契合在哪用数字标得清清楚楚。 百万 Token 只要四分钱且输出免单的机制, 让这种以往极度奢侈的遍历打分, 变成了一次只需要花几分钱的日常微小操作。 真正好用的知识库从来不是逼着人类自己去建复杂的分类文件夹, 而是把检索成本压低到可以肆无忌惮地用自然语言反复提问。 以前是把好内容随手扔进黑洞当个数字囤积狂, 从今天起是用毫秒级的决策神经随时把冷数据捞出来变成生产力。 https://x.com/alexchristou_/status/2101674202361221376/video/1 ❤️ 546 likes on X
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- @AYi_AInotes
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- 2026-09-25
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Jev guides for this use case
- How Jev sorts a build into one of 21 use casesThe 21 criteria Jev classifies against, published in full, plus what the reviewer sees and how ambiguity is handled.
- What people actually build with Jev, by use caseAll 21 use cases with live counts from real submissions, plus what each one is actually for.
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