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Fusion。",1789084800000,false,"Blockbeats",0,[13,20,26,32,38,44],{"id":14,"title":15,"content":16,"publishTime":17,"important":9,"sourceName":10,"language":18,"dateString":19},198946,"Jevgrep让Coding Agent少自己找代码：SWE-bench主模型成本降29%","动察 Beating AI 快讯，开发者 David 基于 Jev 开源了一个专门给 Coding Agent 找代码的研究工具 Jevgrep。\n用户只要问一句「登录校验写在哪里」，它就会用 TypeSafe 的决策模型 Jev 逐层搜索代码库，找出相关文件和源码片段，再交给 Claude Code、Codex 等 Agent 继续修改和测试。\n它主要解决 Coding Agent 很费 token 的「找代码」环节。Jevgrep 不会先把整个仓库塞给模型，也不是只做一次语义搜索。它会先判断哪些目录值得继续找，再检查相关文件和代码声明，最后返回源码片段、行号和后续阅读线索。仓库还提供了 Skill，让 Agent 知道什么时候调用 `jg` 收集上下文。\n最新 SWE-bench 实验用了 10 个 Python 任务。使用 Jevgrep 和不用 Jevgrep 都完成了 8 个，但 GPT-5.6 Sol 的总成本从 7.62 美元降到 5.44 美元，减少 28.63%。作者最初在 X 上写的是 40%，仓库随后更新实验结果，目前已经改成「约 30%」。\n不过，这个数字只统计 Sol 的费用，没有把 Jev 算进去。实验日志中能够确认的 Jev 调用费用已经至少有 1.57 美元，部分调用缺少完整计费记录，实际总额未知。此外，这组实验只有 10 个任务，而且每题只跑了一次。",1790592180000,null,"Mon Sep 28 2026 10:43:00 GMT+0000 (Coordinated Universal Time)",{"id":21,"title":22,"content":23,"publishTime":24,"important":9,"sourceName":10,"language":18,"dateString":25},206639,"AI订阅大摸底：Claude补贴比国产Coding Plan还狠","动察 Beating AI 快讯，SemiAnalysis 把 OpenAI、Anthropic、Meta、SpaceXAI、MiniMax、月之暗面、智谱、Cursor 和 Cognition 的 AI 订阅套餐逐个测了一遍。它分别测试输入、缓存写入、缓存读取和输出会消耗多少订阅额度，再按自己 9 月的 Agent 使用结构计算，其中约 96.6% 是缓存读取。最后再套用各家的 API 标价，看同样一批 token 如果直接走 API，需要花多少钱。SemiAnalysis 将这个数字称为「API 等价价值」。\nClaude 的 Sonnet 和 Opus 补贴最狠。同样每月 200 美元的 Max 20x，使用 Sonnet 5.5 最多可跑出约 12,529 美元的 API 用量，相当于月费的 62.6 倍；Opus 5.5 约为 11,726 美元，相当于 58.6 倍。换成更贵的旗舰模型 Fable 5.1，却只剩 2,485 美元，也就是 12.4 倍。同一个套餐能差近 5 倍，因为不同模型和不同类型的 token，扣掉后台额度的速度并不一样。Anthropic 给 Sonnet 和 Opus 留了大量额度，对 Fable 则明显收紧。\n国产 Coding Plan 同样大量补贴重度用户，但力度仍追不上 Claude。SemiAnalysis 测得，132 美元的 MiniMax Token Ultra 可折算约 3,088 美元 MiniMax-M3 API 用量，相当于月费的 23.4 倍；168 美元的智谱 GLM Coding Max 约为 11.6 倍；199 美元的 Kimi Code Ultra 约为 6.7 倍。即使是其中最高的 MiniMax，每美元折算价值也只有 Claude Sonnet、Opus 的四成左右。\nOpenAI 最近反而开始主动收紧补贴。新购 Pro 200 的各档模型 token 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Time)",{"id":33,"title":34,"content":35,"publishTime":36,"important":9,"sourceName":10,"language":18,"dateString":37},196585,"Jev团队公开Coding Agent设计草案：每轮重新决定模型该看什么","动察 Beating AI 快讯，TypeSafe 创始人 Diogo Almeida 公开了一份 Coding Agent 设计草案，希望社区直接拿去实验。他直说团队大概率没时间自己把这些想法都做出来。\n这份草案最核心的改动是上下文。现在的 Coding Agent 通常会一直带着之前的聊天、工具结果和代码状态，太长了再压缩或重启。TypeSafe 想让 Agent 每轮重新判断：继续沿用现有缓存，还是重新挑一份更合适的上下文；哪些保留全文，哪些只留摘要，哪些直接丢掉。\nAlmeida 认为，现有 Agent 不少设计都被 KV Cache 反过来绑住了。比如任务先切给便宜模型，再切回强模型，强模型可能还得重新读一遍长上下文，最后反而更贵；工具定义长期塞在系统提示词里，也会一直占空间。\n上下文如果能动态重组，模型路由、Sub-agent、MCP 和 AGENTS.md 也能跟着按需加载。简单任务交给便宜模型，需要哪个工具再加载哪个，子 Agent 只拿和自己任务相关的状态。Almeida 暂时把这套思路叫「Meta-attention」。",1789980180000,"Mon Sep 21 2026 08:43:00 GMT+0000 (Coordinated Universal Time)",{"id":39,"title":40,"content":41,"publishTime":42,"important":9,"sourceName":10,"language":18,"dateString":43},195915,"Kimi Code桌面端上线：浏览器、终端、多Agent塞进一个窗口","动察 Beating AI 快讯，月之暗面正式上线 Kimi Code Desktop，把原本偏终端的 Kimi Code 做成了独立桌面应用。用户可以直接打开本地项目，让 Agent 读写代码、跑命令、查看 Git 改动，目前支持 macOS 和 Windows。\n桌面版直接内置了浏览器和终端。做网页时，Agent 可以读取右侧网页的页面信息，用户也能直接点选元素、框出区域或者截图批注，告诉它具体哪里要改。改代码、看网页效果、跑测试都不用再切窗口。\n它还支持计划、目标和 Swarm 三种任务模式。Swarm 可以把任务拆给多个子 Agent 并行处理，长任务也能放到后台继续跑。除了 Kimi 自家的模型，用户还可以自己填 API Key 和 Base URL，接入其他模型供应商。",1789729260000,"Fri Sep 18 2026 11:01:00 GMT+0000 (Coordinated Universal Time)",{"id":45,"title":46,"content":47,"publishTime":48,"important":9,"sourceName":10,"language":18,"dateString":49},195337,"Kimi推出金融行业方案，Agent直接跑投研、建模和报告","动察 Beating AI 快讯，月之暗面推出 Kimi 金融行业解决方案，把金融数据、Skill 和 Agent 能力打包到一起。方案接入 Wind、东方财富、标普全球、财联社、财新数据等 10+ 数据源，并提供财务建模、机构研报、财报点评、组合复盘、持仓早报等 9 项金融 Skill。\nKimi 称，合作案例中，财务建模的人力投入从 5–7 人天降至 0.5–1 人天，深度研究从 10–20 天缩短至约 2 天。\nKimi 还与中信建投共建「风险评估网关」，处理数据分级、个人信息保护、工具授权、内容核验和审计追溯。试点中，单份临时受托报告的人工制作时间从约 30 分钟降至 10 分钟。\n最近一周，OpenAI、Anthropic 也相继推出金融行业产品。现在 Kimi 也跟进，把金融数据、专业 Skill 和 Agent 工作流一起推向机构客户。",1789630140000,"Thu Sep 17 2026 07:29:00 GMT+0000 (Coordinated Universal 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