以前得租服务器才能跑的大模型,现在一张普通游戏显卡在家就能带得动,回复比人阅读还快,数据不离开自己电脑。作者顺手做成了免费开源的工具 Strata,附上好几张显卡的实测速度,想自己折腾本地部署的可以参考。https://github.com/Niko1221/Strata
GitHub
GitHub - Niko1221/Strata: Qwen3.8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference…
Qwen3.8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference engine, OpenAI/Anthropic API on localhost, optional image input. - Niko1221/Strata
👎2
Reflection 公布了第一个开源模型 Beam:总参数 5010 亿,但每次真正参与计算的只有 230 亿,所以用起来便宜不少,跑分却能咬住比它大好几倍的模型。权重本月内公开。少花钱办大事,这个方向我挺喜欢。https://reflection.ai/blog/introducing-beam
Reflection
Introducing Beam: Reflection’s 501B open-weight model — Reflection
Make intelligence open and accessible to all
让 AI 自己设计跑 AI 的芯片,这事真有人做成了,还全部开源。设计烧进一块可编程芯片后,实机跑通了十个主流模型,和软件模拟的结果分毫不差。从一行矩阵乘法到电路的每个环节都在同一个仓库里,想搞懂 AI 加速器原理的人,可以顺着一路读完。https://github.com/FeSens/openTPU
GitHub
GitHub - FeSens/openTPU: An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo.…
An open-source AI accelerator, developed by AI: RTL, ISA, simulator, compiler and profiler in one repo. Runs Qwen3, LFM2.5 and Qwen3.5 on a Kintex-7 PCIe card. - FeSens/openTPU
现在 AI 很热衷给开源项目提用完就把敏感数据清零的补丁,看着像在加固安全。有人把补丁前后的代码编译成机器码逐行对比,发现有的清零被编译器直接删掉了,有的把零写进一堆从没存过密码的内存,秘密却一直待在 CPU 里。看完有点后怕。https://00f.net/2026/10/06/zeroization-1/
Frank DENIS random thoughts.
Zeroization, part 1: Wiping can make things worse
A good hygiene when processing secrets is to wipe them after use.
有人把国产模型 DeepSeek 4.1 Flash 当主力用了一个月:写代码、查资料、跑杂活,不看名字常分不清它和贵很多的旗舰差别。关键是它记长对话很省内存,比第一代小了四百多倍,跑一整天常常不到一块钱。我看完的第一反应是:以后先算账单,再谈模型。https://www.dgt.is/blog/2026-10-07-deepseek-freek-out/
www.dgt.is
Why Isn't The Industry Freaking Out About DeepSeek 4.1 Flash?
thoughts, talks, docs and unpopular opinions
一个 AI 编程工具把自己重写了一遍:它指挥两千多个 AI 分身,花两周把代码从 TypeScript 全部重写成 Rust,每改一处都拿新旧版本跑同样的任务对比,行为一致才算过。思路不复杂,但规模吓人,看完挺开眼。https://www.primeintellect.ai/blog/prime-agent-rust
www.primeintellect.ai
Rewriting Prime Agent in Rust
Today, we're excited to ship a faster, cleaner and more reliable Prime Agent, rewritten from the ground up in Rust.
想给自己搭个 AI 助手,又懒得买服务器、配数据库,可以看看 Talorys:一行命令就装进你自己的 Cloudflare 账户,聊天、记忆、待办、提醒都有,全跑在免费额度上。就算 AI 额度用光,任务和提醒也照常工作,这点挺周到。https://github.com/rociiu/talorys
GitHub
GitHub - rociiu/talorys: Your personal AI agent in your own Cloudflare account. Chat, memory, tasks, notes and scheduled reminders…
Your personal AI agent in your own Cloudflare account. Chat, memory, tasks, notes and scheduled reminders, deployed with one command: npx create-talorys@latest. Free-tier friendly, single-user, no ...
反编译是把编译好的程序还原成源码,安全分析常靠它查漏洞。一篇论文发现,如今这活多交给 AI,评分却只看能不能编译通过:最会刷分的模型把通过率从 75% 刷到 90%,代码行为与原程序一致的比例反而掉了十几个点,已知漏洞也悄悄消失了。https://arxiv.org/abs/2609.05370
arXiv.org
When LLM Decompilers Recompile More and Preserve Less
Decompilation recovers high-level source from compiled machine code and serves as a foundation for security tasks such as vulnerability detection and malware analysis. Traditional decompilers like...
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
GitHub
GitHub - prabanta-dev/janas: 37 tok/s from Qwen3-Next-80B (48 GB) on a 32 GB laptop, 24 without drafts vs 2.8 for llama.cpp; reading…
37 tok/s from Qwen3-Next-80B (48 GB) on a 32 GB laptop, 24 without drafts vs 2.8 for llama.cpp; reading 62 vs 16 tok/s. Faster than llama.cpp on all 13 models compared in Q4_K_M. LLMs bigger than y...
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
Anthropic
Redesigning source control at Anthropic
As our merges per day grew more than tenfold, source control became our biggest development bottleneck. Here, we share how we migrated to an in-house version control system in five months.
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
Liquid AI
LFM2.5-24B-A2B: Built for Fast On-Device Agents | Blog
LFM2.5-24B-A2B: an open-weight 24B reasoning model with 2B active parameters, built for fast, private on-device agents. Beats Qwen3.6-35B-A3B on LiveClaw.
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
Meta AI Research
Introducing Muse Spark Open, The Most Capable US Open‑Weight Model to Date
Muse Spark Open is the most capable US open-weight model to date, and brings leading agentic capabilities to your desktop on Apple M5 Ultra Mac Studios.
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/
Jakearchibald
JPEG XL vs AVIF for web use-cases
JPEG XL has landed, but how does it compare?
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/
Alex Wang
You could have invented transformers from memory
Every layer is a memory store
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
arXiv.org
Hybrid Reasoning Agents
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while latent reasoning...
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/
Max Woolf's Blog
OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?
What is “the true cost of a token”?
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/
Cloudflare Blog
Cross-request KV cache exposure in the GLM-5.3 Workers AI model, and our remediation with SGLang
A configuration mismatch in GLM 5.3 on Workers AI triggered an SGLang bug that exposed KV cache pages across requests. Here is how we addressed the issue.
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
projectzero.google
Fuzzers vs. AI: Comparing Fuzzilli and BigSleep on JavaScript Engines
SummaryProject Zero compared two automated tools used by Google to find vulnerabilities in Chrome...
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
we-have-a-problem.com
Apple's git still trusts the compromised eMudhra certificates
Eleven days after Apple was notified of the eMudhra CA compromise, the git, curl and python3 that Apple ships with macOS still trust the compromised certificates.
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
Divyansh Agrawal
Qwenputer: a fully functioning, deterministic RISC-V CPU inside Qwen3.5
I compiled a RISC-V CPU into Qwen 3.5 4B's weights, using its Gated DeltaNet layers as RAM. It runs C and MicroPython inside the forward passes, and Qwen uses it for exact arithmetic.