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大模型想在笔记本上跑，最愁的是内存不够。新工具 Janas 把模型大部分数据放在固态硬盘上，用到哪块读哪块，结果 32GB 内存的机器跑起了 800 亿参数的模型，速度还是常用方案的八倍多。看完挺想在自己电脑上试试。https://github.com/prabanta-dev/janas
Anthropic 晒出一组数字：5 月他们每天合并约 2500 个代码变更，9 月已超过 3 万，多出来的基本是 AI 写的。按人类规模设计的 git 撑不住这种提交量，他们干脆花五个月自研了一套版本控制系统顶上。看完挺有感触：AI 时代的开发工具，得按机器的规模重新想一遍。https://www.anthropic.com/engineering/redesigning-source-control
Liquid AI 新发布了一款专门跑在自己设备上的模型，思路挺讨巧：总参数 240 亿，每次生成只动用其中 20 亿，苹果 M5 Max 芯片上每秒能吐出 184 个词，自动办事的能力还不输比它大一半的模型。免费开放下载，做本地 AI 应用的朋友可以上手。https://www.liquid.ai/blog/lfm2-5-24b-a2b
机房级别的模型搬进家里跑，这回看着是真的。Muse Spark Open 今天免费开放下载，8160 亿参数，两台顶配 Mac Studio 就能带动。演示里它自己操作剪辑软件，逐帧看完素材，把最精彩的片段裁成竖屏短片。查了下价格，冷静了。https://research.meta.ai/blog/muse-spark-open
网站图片格式多了两个新选择：AVIF 和 JPEG XL，浏览器都已支持，到底用哪个？有人拿照片、截图、带透明背景的图逐项实测：绝大多数情况 AVIF 文件更小，压缩和打开也更快。一个容易踩的坑：JPEG XL 有款压缩工具遇到透明背景一律无损保存，文件能大出两三倍。https://jakearchibald.com/2026/jpeg-xl-vs-avif/
模型为什么能记住那么多冷知识？有人写了篇长文回答：把神经网络的构件看成记忆仓库，存得越多回忆时干扰越厉害，同样规模下，最基础的结构只能存几十条，注意力机制却能存上百万条，这正是大模型最核心的部件。一路推下来，Transformer 像是你自己也能想出来的。https://alexwang.ai/posts/associative-memory/
AI 代理干活，一大半成本烧在它写给自己看的推理文字上。有篇新论文换了个做法：思考时不落文字，让模型在"脑子里"默默想，只在调用工具、对外行动时才开口。结果 11 个基准上性能反涨一成，成本降到原来的三分之一，思路挺巧。https://arxiv.org/abs/2610.18861
有人拿几家的价目表算了笔账：用 AI 写代码时，模型每一轮都得把之前的对话重新读一遍，这笔重读费用能占总开销九成以上。所以有厂商把重读的单价砍掉七成五，整张账单也就跟着降了七成。https://minimaxir.com/2026/10/cheap-llms/
Cloudflare 把自家一次线上事故写成了公开报告：不同客户本该互不可见，结果有 155 次回答混进了别人提问的内容，触发条件都是超长输入。旧缓存没清干净，加上报错处理有漏洞，两处凑到一起才出事。用托管模型服务的人可以对照看看。https://blog.cloudflare.com/kv-cache-exposure-remediation/
谷歌负责 Chrome 安全的团队拿半年真实数据比了两种找漏洞的办法：让大模型读代码，和让程序自动乱造代码去跑。大模型报出 45 个，另一套 37 个，重合的只有四分之一。结论挺实在，两套一起用才划算。https://projectzero.google/2026/10/fuzzing-v-AI.html
Mac 自带的 git 至今还信任一家已经被攻破的证书机构，拉代码、推代码的连接都可能被人中途截走。作者留了个测试仓库，跑一条命令就知道自己有没有中招。用 Mac 写代码的不妨查一下。https://emudhra.we-have-a-problem.com/apple.html
通义千问 4B 的权重里现在装着一颗能跑 C 和 MicroPython 的处理器，不训练、不接外部工具，全在模型自己的计算里完成。做法是把模型内部那块固定大小的记忆调成永不淡忘、可按位置精确改写，它于是成了一块内存。https://divagr.com/blog/qwenputer
安全研究者在 Cursor 的插件市场里发现，AI 编程助手 Claude Code 的编辑器插件被塞了后门，装上后别人能长期远程登录这台电脑。恶意代码放在文件最前面，编辑器一加载就执行，不用用户点任何东西。受影响的版本是 2.1.312 和 2.1.313，装过的话现在就去查一下。https://phorion.io/blog/offical-claude-code-vsx-compromise/
有人在 ChatGPT 里点开一条别人分享的对话链接，账号就可能被别人接管。问题出在两个看着无害的小功能：网页预览和画图组件，两者一配合，攻击者的代码就在你已登录的页面上跑了起来，连你没公开过的私聊都能读走。OpenAI 已经修好。https://www.parameter.ai/labs/we-found-a-gap-at-openai
不同模态训练的模型会不会形成同一套世界表示，一直只是猜想。有篇论文把它证成了定理，条件是训练数据得是同一场景的两个观测。照这个思路做的小文本模型，跟图像模型的对齐程度超过参数量大几百倍的语言模型。https://arxiv.org/abs/2610.39302
Postgres 19 推迟到十一月了，比往年晚大约一个月。作者数了提交记录，原因是大模型把堆了多年的老 bug 一一翻出来，修完还得搬回旧版本，这类提交一年多了七成，人手被占住。AI 帮忙找 bug，也顺手给维护者加了活。http://rhaas.blogspot.com/2026/10/postgres-19-is-late-and-its-ais-fault.html
经常调接口的人大概都装着 Postman。有开发者查了本地日志，发现它打开后会自己往电脑上的 AI 编程助手装插件、写启动指令，让助手把接口的活都交给它。一个客户端顺手替别的工具做主，挺让人介意。两样都在用的人可以照着查一遍。https://github.com/postmanlabs/postman-app-support/issues/14370
Go 1.27 把标准库里解析 JSON（接口数据最常用的格式）的代码换成了新版：同一段数据里出现两个同名字段时，旧版随便挑一个，这个含糊之处曾被人利用出过漏洞，新版默认报错。跑 Go 服务的人早晚会撞上这类边界。https://go.dev/blog/jsonv2
有人翻出 OpenSSH 一个藏了九年的问题：在 Alpine 这类精简 Linux 上，本该只认密钥、不收密码的账户，连 root 也算在内，输一段超过 256 字符的任意密码就能登进去。根源是底层密码函数算不出结果时返回一个星号，而星号在密码文件里恰好表示禁止密码登录。https://sshhole.com
GitHub 把自家底层存代码的系统重做了一遍。内部测试里，5000 个 AI 代理同时往一个仓库推代码，每秒 7200 次，读取也照常。关键在于代码内容本身不可变，可以同时写，真正要排先后顺序的只剩分支位置那一小块。做大规模系统的可以想想这个思路。https://github.blog/engineering/architecture-optimization/inside-githubs-new-git-storage-architecture/
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