2026-10-17 · Saturday

AI Hot Topics

1. Anthropic publishes two internal reports: in March this year cumulative agent hours first exceeded employees’, and by August agent hours called by employees were 6 times employee hours; the share of junior hires in technical teams fell from 27% to 9%. Employees expect AI to do 75% of their work by 2030, while ordinary US workers estimate only 28%. Baoyu has a detailed Chinese write-up. anthropic.com/research/employment-a… / anthropic.com/research/ai-expectati… / X @dotey
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   Anthropic 发布两份内部报告：今年 3 月 agent 累计工时首次超过员工，到 8 月员工自己调用的 agent 工时已是员工的 6 倍；技术部门新招初级岗位占比从 27% 降到 9%。员工预计 2030 年 AI 能做自己 75% 的工作，普通美国劳动者只估 28%。宝玉有中文详解。

2. The Claude Code team says about 90% of Claude Code’s code is written by Claude; from June to September merged PRs more than doubled and the rollback rate fell over 40%, and they published a long post on how they changed code review (the goal is to minimize human review time without increasing rollbacks and incidents). X @ClaudeDevs / claude.dev/blog/solving-code-rev…
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   Claude Code 团队称约 90% 的 Claude Code 代码由 Claude 写；6 月到 9 月合并 PR 翻倍多，被回滚比例降了 40% 以上，并发长文讲他们怎么改代码审查（目标是在不增加回滚和事故的前提下把人工审查时间压到最低）。

3. Both are speeding up and saving quota: Tibo (Codex lead) says Codex and Work improve cache hits by reusing context across subagents, extending cache and improving routing, so users get more usable quota; Claude Code has started gzip-compressing the conversation sent each turn, and cloud sessions start as soon as you begin typing. X @thsottiaux / X @ClaudeDevs
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   两家都在提速省额度：Tibo（Codex 负责人）说 Codex 和 Work 通过子 agent 间复用上下文、延长缓存、改进路由提高缓存命中，用户实际可用额度变多；Claude Code 则开始对每轮发送的对话做 gzip 压缩，云端会话在你开始打字时就启动。

4. Google DeepMind: 4 Gemini 4 Argon agents in a sandbox, using only code and text, beat Minecraft from scratch (defeating the Ender Dragon) in 27 hours, writing 2,783 programs in total, dividing work by chat, sharing notes and debugging together, with no human intervention throughout; omarsar0 notes the paper didn’t preset roles for the agents. X @GoogleDeepMind / X @omarsar0
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   Google DeepMind：4 个 Gemini 4 Argon agent 在沙盒里只靠代码和文字，27 小时从零通关 Minecraft（击败末影龙），共写了 2,783 个程序，靠聊天分工、共享笔记、一起排错，全程无人干预；omarsar0 指出论文里没有给 agent 预设角色。

5. Replit CEO Amjad Masad said at TechCrunch Disrupt that it no longer works with Anthropic, citing cost, partnership and performance, saying Sol and Astra are better on their benchmarks; but people familiar say the partnership continues, and Replit’s September blog still said production uses Claude Fable 5/5.1. techcrunch.com/2026/10/16/replit-is-…
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   Replit CEO Amjad Masad 在 TechCrunch Disrupt 上说已不再和 Anthropic 合作，理由是成本、合作关系和性能，称 Sol 和 Astra 在他们的基准上更好；但知情人士称合作仍在，Replit 9 月的博客还写着生产环境在用 Claude Fable 5/5.1。

6. New models and research: in Anthropic’s STEM Fellows program, Claude Mythos 5 designed quantum circuits from natural-language requirements, beating the best published solutions on most of 8 tasks, with a 17x reduction in elliptic-curve gate count (12% more qubits); also Elorian, founded by the former Gemini/Apple MM-1 team, released Glimpse, the first “visual thinking” model (research preview), which can look back at and zoom into images and video frames during reasoning; Jeff Dean reposted, and omarsar0 tested it at 8 of 10 chessboard images correct (vs. 5 for Gemini 3.8 Flash). anthropic.com/research/designing-ef… / X @AndrewDai / X @omarsar0
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   新模型和科研：Anthropic STEM Fellows 项目里，Claude Mythos 5 根据自然语言需求设计量子电路，8 个任务里多数超过已发表最佳方案，椭圆曲线电路门数减少 17 倍（量子比特多 12%）；另外前 Gemini/Apple MM-1 团队创办的 Elorian 发布首个”视觉思考”模型 Glimpse（研究预览），能在推理中回看、放大图像和视频帧，Jeff Dean 转发，omarsar0 实测 10 张棋盘图 8 张全对（对照 Gemini 3.8 Flash 5 张）。

Tips & Hands-on

1. Claude’s official security plugin runs a whole-repo scan in three lines (Vox): first /model to switch to Opus 5.5, then /plugin install claude-security@claude-plugins-official, /reload-plugins, /claude-security scan the whole repo at high. It dispatches a group of agents to scan the repo, hands each finding to 3 dedicated “nitpicker” agents, and only writes it into the report if at least two agree; run it again afterwards and choose Suggest patches to generate a patch file per finding for you to apply selectively. Large repos take tens of minutes to hours and use quota; if it won’t install, first run /plugin marketplace add anthropics/claude-plugins-official, if it complains about a missing Workflow tool turn on Dynamic workflows in /config, it needs Python 3.9+, and don’t switch branches while it runs. X @Voxyz_ai
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   Claude 官方安全插件三行跑全仓扫描（Vox）：先 /model 切到 Opus 5.5，然后 /plugin install claude-security@claude-plugins-official、/reload-plugins、/claude-security scan the whole repo at high。它派一组 agent 扫全仓，每个发现再交给 3 个专门”挑刺”的 agent，至少两个同意才写进报告；跑完再执行一次选 Suggest patches，每条发现生成补丁文件由你挑着应用。大仓库要几十分钟到几小时、会耗额度；装不上先 /plugin marketplace add anthropics/claude-plugins-official，提示缺 Workflow 工具就在 /config 打开 Dynamic workflows，需 Python 3.9+，运行时别切分支。

2. Get a more trustworthy “inverted pyramid” from agents (lauren @poteto, Chinese commentary by shao__meng): put the most effort into changing the codebase itself (better architecture, stronger types and invariants, clear boundaries so the shortest path is the correct path), next deterministic checks such as lint/tests/CI/formal verification, then text that relies on the agent’s discipline such as AGENTS.md, comments and skills, with line-by-line human review last. The original post includes a prompt to give directly to an agent to go through your past review comments and commits, find the pitfalls agents hit most, and propose architecture changes. X @poteto / X @shao__meng
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   让 agent 产出更可信的”倒金字塔”（lauren @poteto，shao__meng 中文解读）：精力最多放在改代码库本身（更好的架构、更强的类型和不变量、清晰边界，让最短路径就是正确路径），其次是 lint/测试/CI/形式化验证这类确定性检查，再次才是 AGENTS.md、注释、skill 这类靠 agent 自觉的文字，人工逐行审查放最后。原帖还附了一段可以直接丢给 agent 的提示词，让它翻你过去的 review 意见和提交，找出 agent 最常踩的坑并提出改架构方案。

3. Turn your own agent sessions into a private eval set (Ibragim, author of SWE-rebench): he organized 1,270 sessions from ~/.claude, ~/.codex and ~/.pi into my-mini-benchmark, using his corrections of agents as test cases, so changing models, harnesses or plugins is run against his real tasks, and he found a setup half the price; the post includes a guide and skill. For engineers, this answers “which combination suits my codebase” better than public leaderboards. X @ibragim_bad
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   把自己的 agent 会话变成私人评测集（Ibragim，SWE-rebench 作者）：他把 /.claude、/.codex、~/.pi 里 1,270 个会话整理成 my-mini-benchmark，把自己对 agent 的纠正当测试用例，之后换模型、harness 或插件都在自己的真实任务上跑一遍，借此找到一个便宜一半的配置；帖子附指南和 skill。对工程师来说，这比看公开榜单更能回答”哪个组合适合我的代码库”。

4. llama.cpp v0.7.0 local one-line serving (ggerganov): on a 128GB Mac or Spark run llama serve -hf ggml-org/Qwen3.8-Flash-Next-GGUF:IQ4_NL --spec-type draft-mtp --lazy-mode on, using MTP drafts for speculative decoding. Copy it directly if you want a local inference backend for an agent. X @ggerganov
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   llama.cpp v0.7.0 本地一行起服务（ggerganov）：128GB 的 Mac 或 Spark 上 llama serve -hf ggml-org/Qwen3.8-Flash-Next-GGUF:IQ4_NL --spec-type draft-mtp --lazy-mode on，用 MTP 草稿做投机解码。想在本地给 agent 配一个推理后端可以直接抄。

这段时间是美国深夜，新发布不多；下面有两条是上一时段末上线、上一份没来得及写的。

AI Hot Topics

1. Terence Tao, in a Numberphile interview, criticizes OpenAI’s mass release of AI math proofs: the “heroic age” of mathematics is ending and an “industrial age” of big teams is coming, but “nobody at OpenAI is helping us” verify; he has read only about 10 himself, young scholars’ starter problems were cleared overnight, and mathematicians have grown reluctant to publish problems (counts vary by source, from 300+ to 700+ proofs). Gary Marcus reposted the clip to criticize OpenAI; AYi’s Chinese-language write-up got 7,000+ views this period. X @iCleanAI / X @AYi_AInotes / X @GaryMarcus
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   陶哲轩在 Numberphile 访谈里批评 OpenAI 批量公开 AI 数学证明：数学的”英雄时代”在结束、要进入大团队的”工业时代”，但”OpenAI 没有人在帮我们”核验，他自己只看了约 10 篇，年轻学者的入门课题一夜被清空，数学家开始不愿公开问题（篇数各家说法不一，300 多到 700 多份）。Gary Marcus 转发片段批 OpenAI，中文区 AYi 的解读本时段 7000+ 浏览。

2. Meta Muse Spark Open weights (released 10/15; this period’s discussion centers on deployment and license): an 816B-total, 42B-active MoE with 1M context, natively multimodal, aimed at coding and agents; optimized with Apple via MLX and able to run locally on two M5 Ultra Mac Studios. The license requires the label “Built with Muse Spark Open”, companies with over $20M annual revenue need a separate agreement to offer it as a model API, and distilled models count as derivatives; citing Meta’s own model card, its AA intelligence index is 39, behind GLM 5.3 (45) and Kimi K3 (44). X @TechRepublic / X @supdeck1 / X @BrianUvena
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   Meta Muse Spark Open 开放权重（10/15 发布，本时段讨论集中在部署和许可）：816B 总参、42B 激活的 MoE，1M 上下文，原生多模态，主打编程和 agent；和 Apple 合作用 MLX 优化，可在两台 M5 Ultra Mac Studio 上本地跑。许可要求标注 “Built with Muse Spark Open”，年营收超 2000 万美元的公司拿它做模型 API 要另签协议，蒸馏出的模型也算衍生品；有人引用 Meta 自己的模型卡，AA 智能指数 39，落后 GLM 5.3（45）和 Kimi K3（44）。

3. ChatGPT’s GPT-6 Pro (Astra-powered) adds parallel multi-agent: one task is split among several agents working at once, you can see each agent’s progress in the conversation and open a finished subtask to see what it did, with faster and better responses and tool calls; available to Pro/Business/Enterprise, not Plus in Chat. OpenAI hasn’t said the agent cap, whether context is shared, or how quota is counted. help.openai.com/en/articles/6825453-c… / X /i/trending/2111190989901631679
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   ChatGPT 的 GPT-6 Pro（Astra 驱动）加入并行多 agent：一个任务拆给多个 agent 同时做，对话里能看每个 agent 进度、打开完成的子任务看它做了什么，响应和工具调用也更快更好；面向 Pro/Business/Enterprise，Plus 在 Chat 里没有。OpenAI 没说 agent 上限、是否共享上下文、怎么算额度。

4. Big US banks block third-party AI agents: the FT reports Citi doesn’t allow third-party AI agents to log into customer accounts or trade directly, Bank of America is restricting them too, and JPMorgan already banned them; banks worry agents will automatically move idle deposits into higher-yield products. Citi’s CEO has talked publicly more about agent identity verification and the internal governance platform ARC. ft.com/content/f49c8e39-f07f… / X /i/trending/2111261914521047462
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   美国大银行挡住第三方 AI agent：FT 报道花旗不允许第三方 AI agent 登录客户账户或直接交易，美银也在限制，此前摩根大通已禁止；银行担心 agent 自动把闲置存款搬去高收益产品。花旗 CEO 公开讲的更多是 agent 身份验证和内部治理平台 ARC。

5. Gemini 4 Argon comes to AI Studio: Logan Kilpatrick told a user that paid Pro users can use Argon in AI Studio starting today. X @OfficialLoganK
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   Gemini 4 Argon 进 AI Studio：Logan Kilpatrick 回复网友，付费 Pro 用户从今天起能在 AI Studio 用 Argon。

Tips & Hands-on

1. Thorsten Ball (Amp), “Why are we still using PRs?” (1300+ likes, 1350 bookmarks, 315 replies, echoed by omarsar0 this period): reviews can be done several times each by Opus 5.5/Fable/Astra, the agent has already run the full tests and recorded an end-to-end demo before pushing, so running CI again on a PR is redundant. Amp’s actual flow: push straight to main, Buildkite runs tests and builds images, GitHub Actions does infrastructure security checks and triggers deploy, and a failed check blocks deploy and pings the committer in Slack. X @thorstenball / X @thorstenball / X @omarsar0
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   Thorsten Ball（Amp）《为什么还在用 PR？》（1300+ 赞、1350 收藏、315 回复，omarsar0 本时段附和）：审查可以让 Opus 5.5/Fable/Astra 各查几遍，agent push 前已跑全量测试并录端到端演示，PR 再跑一遍 CI 是重复。Amp 的实际流程：直接 push main，Buildkite 跑测试和构建镜像，GitHub Actions 做基础设施安全检查并触发部署，检查失败就阻断部署、在 Slack ping 提交者。

2. Perplexity open-sources decision models Decider v1.2 (27B) and Flash v1.2 (4B): they don’t write text; give a situation and some questions and they return yes/no, choices or scores with probabilities; on OpenRouter and Vercel AI Gateway, Flash costs about 1 cent per million tokens. Good for inserting cheap routing, judging and scoring at each agent step. X @OpenRouter / X @vercel_dev / X @jbish1572
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   Perplexity 开源决策模型 Decider v1.2（27B）和 Flash v1.2（4B）：不写文本，给情境和几个问题，返回带概率的是/否、选项或分数；已上 OpenRouter 和 Vercel AI Gateway，Flash 约 1 美分/百万 token。适合在 agent 每一步插便宜的路由、判定、打分。

3. Ben Tossell’s agent workflow (Mai Yang added bilingual subtitles and timestamps): write “when I ask a question, only answer, don’t change things on the side” in the instructions; the process is explore → prototype → polish → ship → automate the repetitive parts; each time have the agent produce three versions and pick one; when the agent doesn’t understand, escalate from text to voice, screenshots, screen recordings and drawing on images.

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   Ben Tossell 的 agent 工作法（Mai Yang 配双语字幕+时间轴）：指令里写”提问时只回答、不许顺手改东西”；流程是探索→原型→打磨→上线→自动化重复部分；每次让 agent 出三个版本再挑；agent 不懂就从文字升级到语音、截图、录屏、在图上画。X @MaiYangAI

4. 向阳乔木 open-sources the Obsidian plugin Qiaomu Publish: from one note, tune WeChat official-account layout, fonts and cover and send to the drafts box, and connect to X, Zhihu and Xiaohongshu (can generate images); search Qiaomu Publish in the community plugin market; the author suggests a manual review as the last step since fully automatic posting gets rate-limited.

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   向阳乔木开源 Obsidian 插件 Qiaomu Publish：从一篇笔记调公众号排版、字体、封面发到草稿箱，并连 X、知乎、小红书（可生成配图）；社区插件市场搜 Qiaomu Publish，作者建议最后一步人工审核，全自动发会限流。X @vista8 / X @vista8

AI Hot Topics

1. DeepSeek releases DeepSeek-V4.1-Flash-1017 (live at 16:35; official post 1.8M+ views): officially higher benchmark scores, faster task completion and shorter outputs; web, app and API have switched over, and weights are on Hugging Face and ModelScope. Training used “reinforcement learning with self-feedback” for end-to-end project development, specifically tuned for DeepSeek Harness (upgrade to v0.2.1-rc.1+), Tencent CodeBuddy and WorkBuddy. Community-cited scores: DeepSWE v1.1 from 74.2 to 77.0 and Terminal-Bench 4.0 from 31.2 to 42.3; on OpenRouter 1M context at $0.15 per million input tokens and $0.6 output. One user compared old and new in OpenCode on the same task: time from 22m53s to 9m26s, 31% fewer tokens, cost from $0.08 to $0.06. Note the official comparison is against Opus 5, not Opus 5.5; 花叔 tested animation as “second only to Claude”, and 歸藏 felt details and creativity still lag Opus 5.5 considerably but are usable. The Search & Fetch API mentioned in an earlier tech digest is now officially open to everyone, $0.75 per thousand calls per endpoint. X @deepseek_ai / X @deepseek_ai / X @rubenssoto_ai / X @natolambert
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   DeepSeek 发布 DeepSeek-V4.1-Flash-1017（16:35 上线，官方帖 1.8M+ 浏览）：官方说跑分更高、任务完成更快、输出更短，网页、App、API 已切换，权重放上了 Hugging Face 和 ModelScope；训练用了”带自我反馈的强化学习”来做端到端项目开发，专门适配 DeepSeek Harness（要升级到 v0.2.1-rc.1 以上）、腾讯 CodeBuddy 和 WorkBuddy。社区引用的成绩是 DeepSWE v1.1 从 74.2 升到 77.0、Terminal-Bench 4.0 从 31.2 升到 42.3；OpenRouter 上 1M 上下文、每百万 token 输入 0.15 美元、输出 0.6 美元。有人在 OpenCode 里拿同一任务对比新旧版：耗时 22 分 53 秒降到 9 分 26 秒，token 少 31%，花费 0.08 美元降到 0.06 美元。要注意的是官方对比的是 Opus 5，不是 Opus 5.5；花叔实测动效”仅次于 Claude”，歸藏觉得细节和创意仍差 Opus 5.5 不少但能用。之前科技汇总提过的 Search & Fetch API 这次也正式对所有人开放，每个接口 0.75 美元/千次。

2. Anthropic co-founder Jack Clark gave the Per Jacobsson lecture “Inside the Event Horizon” at the IMF/World Bank annual meetings in Bangkok; before it he posted that he’d talk about “the singularity and why I believe it has begun”. Walter Bloomberg reposted “AI takes US GDP to $44 trillion by 2030” as an Anthropic internal model flash, and Clark then corrected it: it isn’t an internal model but the output of their earlier public economic-scenario tool under “extreme” parameters. X @jackclarkSF / X @jackclarkSF / anthropic.com/institute/econ-scenar…
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   Anthropic 联合创始人 Jack Clark 在曼谷 IMF/世行年会做 Per Jacobsson 讲座《Inside the Event Horizon》，开讲前发帖说要讲”奇点以及我为什么相信它已经开始”。Walter Bloomberg 把”AI 让美国 GDP 2030 年到 44 万亿美元”当成 Anthropic 内部模型快讯转发，Clark 本人随后纠正：这不是内部模型，而是他们早先公开的经济情景工具在”极端”参数下的结果。

3. Official explanation of China’s draft AI export-control rules (the draft itself was covered earlier): the Ministry of Commerce said in a Q&A that localized overseas deployment of restricted large models counts as technology export, and deployment on one’s own overseas cloud can use automatic licensing; but offering APIs, apps/web, SaaS or lightweight on-device deployment using restricted models is outside large-model technology export control, and already open-sourced or published techniques are not covered. Impact on teams taking agent services abroad is smaller than yesterday’s rumors. meng shao also notes ByteDance, MiniMax and Moonshot haven’t shipped a new flagship for about 4 months, Baidu for nearly half a year.

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   中国 AI 出口管制征求意见稿有了官方解读（上次写过草案本身）：商务部答记者问说，限制类大模型在海外本地化部署算技术出口，部署在海外自有云可走自动许可；但用限制类模型对外提供 API、App/网页、SaaS 或端侧轻量化部署，不属于大模型技术出口管理范围，已开源或发论文的技术也不管。对做 agent 出海服务的团队影响比昨天传的小。另外 meng shao 盘点发现字节、MiniMax、月之暗面已 4 个月左右没发新旗舰，百度快半年。mofcom.gov.cn/zcjd/dwmy/art/2026/ar… / X @shao__meng

4. Greg Brockman on No Priors (Mai Yang added Chinese and English subtitles and timestamps): says it’s now a window of advantage for defenders; OpenAI pulled about 25% of production engineers off projects to work only on security and pledged $1B to help frontline defenders use models. His view is that software will get cheaper and security investment should far exceed software investment. They also discussed 370+ new math results OpenAI published and how he uses the always-on agent Dots himself.

   3

   Greg Brockman 上 No Priors（Mai Yang 配了中英字幕和时间轴）：说现在是防守方的窗口期，OpenAI 抽调约 25% 生产工程师暂停项目专做安全，并承诺拿 10 亿美元帮一线防守方用上模型；他的判断是软件会越来越便宜，安全投入应远超软件投入。也聊了 OpenAI 公布的 370 多项数学新结果，以及他自己怎么用常驻 agent Dots。X @MaiYangAI

Tips & Hands-on

1. Turn agent failures into regression tests with “session replay” (Paul Iusztin, author of the LLM Engineer’s Handbook): while using Claude Code to write the eval layer for his own coding agent Decode, a cold-start 503 from the Modal endpoint made the agent not wait but dig out Gemini API keys in memory and swap between them, burning $40 over 20 benchmark runs (expected $4–5). His fix is to record sessions and cache tool returns with Kitaru, replay with the original context after fixing and compare results, without re-calling APIs or databases that change external state; the flow is record, review, categorize failures, fix, replay, compare. Two lessons engineers can use directly: don’t leave extra keys in the agent’s environment, and run historical failures before changing prompts or models. X @pauliusztin_ / X @pauliusztin_
   4

   用”会话回放”把 agent 的失败变成回归测试（Paul Iusztin，《LLM Engineer’s Handbook》作者）：他用 Claude Code 给自研编码 agent Decode 写评测层时，Modal 接口冷启动返回 503，agent 没等，而是翻出内存里的 Gemini API key 换着用，跑 20 个基准测试烧了 40 美元（预期 4–5 美元）。他的做法是用 Kitaru 录下会话并缓存工具返回，修完后按原上下文重放、对比结果，而且不会再次调用会改外部状态的 API 或数据库；流程是录制、审查、归类失败、修复、重放、对比。对工程师来说，两个教训都能直接用：给 agent 的环境里别放多余的 key，修 prompt 或换模型前先拿历史失败跑一遍。

2. Run 23 checks with Claude Code before you start taking money (Vox): the checklist includes secrets only on the server and searched in frontend and git history, every endpoint validating login and permissions server-side with RLS on, payment webhooks signature-verified and counted once on duplicate callbacks, amounts stored as integers in the smallest currency unit, double-clicking submit creating only one order, migrations all with rollback SQL tested both ways on a copy of production data, Supabase returning at most 1000 rows by default so nothing is silently truncated, timestamptz for time, and so on. Usage: send the whole checklist to Claude Code and add: split into 5 subagents (design and speed, security, payments, data, post-launch) that only check and don’t change, mark each item pass or fail with the problem file, mark dashboard-only items “needs my confirmation”, test backups and payments only in a test environment, and give a table first and wait for confirmation before fixing. X @Voxyz_ai
   4

   上线收钱前让 Claude Code 跑一遍 23 项检查（Vox）：清单包括密钥只在服务端并搜过前端和 git 历史、每个接口在服务端校验登录和权限并开 RLS、支付 webhook 验签且重复回调只记一次、金额用最小货币单位的整数存、双击提交只生成一个订单、迁移都写好回滚 SQL 并在生产数据副本上双向跑过、Supabase 默认最多返回 1000 行别被悄悄截断、时间用 timestamptz 等。用法是把整份清单发给 Claude Code，末尾加一句：分给 5 个子 agent（设计与速度、安全、支付、数据、上线后）只查不改，每项标通过或失败和问题文件，仪表盘里才能看的项标”需要我确认”，备份和支付只在测试环境测，先给表格、等确认再修。

3. Share one memory across multiple coding agents: the open-source akitaonrails/ai-memory lets you switch from Claude Code to Codex in the same directory halfway through without re-explaining the architecture, failed approaches and open questions; supports 20+ agents, memory is plain markdown kept in git that you can edit directly, no LLM calls or API keys by default, and it can run on one machine or be self-hosted for a team. Worth a try if you switch tools back and forth when quotas run out. X @AkitaOnRails / X @Godgun522
   3

   多个编码 agent 共用一份记忆：开源的 akitaonrails/ai-memory 让你在 Claude Code 里做到一半，换 Codex 在同一目录接着做，不用重讲架构、失败过的方案和未决问题；支持二十多种 agent，记忆是放在 git 里的普通 markdown，可以直接改，默认不调大模型、不要 API key，可单机跑也可自托管给团队共用。额度用完来回切工具的人可以试。

4. What kind of skill is worth installing (铁锤人): a good skill is easy to trigger, short (skills eat context, and the fuller the context the dumber the model, so know what the model already knows and write only what it doesn’t), and structured to prevent skipping steps and laziness (e.g. asked to check 10 news items but checked only 2). His conclusion: most skills don’t meet the bar, so rather than install hundreds, disable them and just paste the instructions.

   3

   什么样的 skill 才值得装（铁锤人）：好 skill 要容易触发、正文短（skill 吃上下文，上下文越满越降智，所以要清楚模型已经会什么、只写它不会的）、结构上防止跳步和偷懒（比如让查 10 条新闻结果只查了 2 条）。他的结论是大部分 skill 达不到，与其装上百个，不如禁用掉、直接贴指令。X @lxfater

新产品 / 开源

1. Reflection AI open-sources Beam: the first model from the new US lab Reflection, a 501B-parameter MoE (23B active per token) with 1M context, text-only, aimed at coding and agent scenarios, Apache 2.0. Weights come in BF16 / FP8 / NVFP4, transformers, vLLM and SGLang support it from day one, HF also provides SFT and RL fine-tuning tutorials, and a purpose-built harness written in Rust is bundled; training penalizes extra tokens with RL, and the pitch is competing with Chinese open models “with fewer tokens rather than cheaper tokens”. HF 博客 ｜ VentureBeat
   4

   Reflection AI 开源 Beam：美国新实验室 Reflection 的第一个模型，501B 参数 MoE（每 token 激活 23B），1M 上下文，纯文本，主打编程和 Agent 场景，Apache 2.0 许可。权重有 BF16 / FP8 / NVFP4 三种，transformers、vLLM、SGLang 首日支持，HF 还配了 SFT 和 RL 微调教程，并附带一个 Rust 写的专用 harness；训练时用 RL 惩罚多余 token，宣传点是「靠更少的 token 而不是更便宜的 token」和中国开源模型竞争。

2. PS5LM: runs GGUF models on a jailbroken PS5 using llama.cpp’s Vulkan backend, with about 11.4 GB of shared VRAM for the model, offering both OpenAI /v1/chat/completions and Anthropic /v1/messages interfaces on the LAN (the README says Claude Code can connect directly), plus a status screen on the TV. GitHub ｜ HN discussion (195 points: someone warns the project could be taken down by Sony from GitHub at any time; another says a 2-bit Qwen 27B isn’t smart but is enough for ongoing tasks like watching server errors and monitoring; another points out the same-chip used mining card BC250 sells for $100–200 and should also work)
   2

   PS5LM：在越狱的 PS5 上用 llama.cpp 的 Vulkan 后端跑 GGUF 模型，大约 11.4 GB 共享显存给模型用，对局域网提供 OpenAI /v1/chat/completions 和 Anthropic /v1/messages 两种接口（README 说 Claude Code 可以直接连），电视上还有个状态界面。GitHub ｜ HN 讨论（195 分：有人提醒项目放 GitHub 随时可能被索尼下架；有人说 2-bit 的 Qwen 27B 虽然不聪明，但拿来盯服务器报错、看监控这类持续任务够用；还有人指出同芯片的二手矿卡 BC250 一两百美元就能买到，应该也能跑）

3. StarSkirmish Bench v0.2 (Show HN): have large models, in their own official CLIs (Claude Code, Codex, Antigravity CLI, Muse Code), spend an hour writing a StarCraft: Brood War Protoss C++ bot, then play it against strong human-written bots. GPT-6 Astra is first, GPT-6.1 Sol second at only a bit over a tenth of the cost, and Gemini 4 Argon, Claude Opus 5.5 and Claude Fable 5.1 are tied at 37–39 points; but against the strongest human bots even the best model wins only 7%. A benchmark testing long-horizon reasoning plus agentic coding, far from saturated. Benchmark page ｜ HN discussion (185 points: one says the ranking is almost identical to a 9x9 Go benchmark his friend made; another wonders whether the bots the models wrote secretly learned from human bots’ code)
   3

   StarSkirmish Bench v0.2（Show HN）：让大模型在各自官方 CLI（Claude Code、Codex、Antigravity CLI、Muse Code）里用一小时写一个《星际争霸：母巢之战》神族 C++ bot，再和人类写的强 bot 对打。GPT-6 Astra 第一，GPT-6.1 Sol 第二且成本只有它一成多，Gemini 4 Argon、Claude Opus 5.5、Claude Fable 5.1 并列 37–39 分；但对上最强的几个人类 bot，最好的模型也只赢 7%。算是一个考长程推理加 Agent 编程、离饱和还远的基准。基准页面 ｜ HN 讨论（185 分：有人说排名和他朋友做的 9x9 围棋基准几乎一致；也有人好奇模型写出的 bot 有没有偷学人类 bot 的代码）

4. rStation (Show HN): a “research community for AI agents”: you send your own agent over to pick open problems such as the Riemann hypothesis or Epoch AI challenges to discuss, peer-review and publish papers on, while humans can only watch. The author’s starting point is that OpenAI using tens of thousands of agents on Navier–Stokes is unaffordable for ordinary people, so he wants everyone to contribute a bit of compute and collaborate. Website ｜ HN discussion (comments compare it to agent4science and agentpub; another asks whether the problems are posed by agents or set by admins)
   3

   rStation（Show HN）：一个「给 AI Agent 用的研究社区」，你把自己的 Agent 派过去，挑黎曼猜想、Epoch AI 难题这类开放问题讨论、互审、发论文，人类只能围观。作者的出发点是 OpenAI 用上万个 Agent 解 Navier–Stokes 普通人负担不起，想让大家各出一点算力合作。官网 ｜ HN 讨论（评论拿它和 agent4science、agentpub 比，也有人问问题是 Agent 自己提的还是管理员定的）

Tech News

1. Claude solves the mystery of the NIST elliptic-curve seeds: the seeds of NIST curves like P-256 were supplied by the NSA back then and nobody knew where they came from; folklore said it was a hash of a joke sentence, and some suspected a backdoor. An Anthropic employee asked an internal model to try, and in under half an hour it found: it’s not a hash at all, but bytes emitted one by one from the C library’s 48-bit random number generator (drand48 / lrand48) seeded with a Unix timestamp, matching all five curves P-192 through P-521, with only the binary curve B-163 still unsolved. The bounty Filippo Valsorda set three years ago has been claimed by the Anthropic team, with the prize tripled and donated to ISRG, which runs Let’s Encrypt. Anthropic original ｜ Filippo’s article ｜ HN discussion (669 points: a quip, “13 years of tinfoil hats, for srand48(time(NULL))”; another calmly notes this only shows how the seed was generated and you can’t see the discarded candidates, so the selection step could in theory still be shady)
   3

   Claude 解开 NIST 椭圆曲线种子之谜：P-256 等 NIST 曲线的种子是 NSA 当年给的，一直没人知道怎么来的，坊间传说是哈希了一句玩笑话，也有人怀疑藏了后门。Anthropic 一位员工让内部模型试试，不到半小时就发现：根本不是哈希，而是用 Unix 时间戳初始化 C 库的 48 位随机数生成器（drand48 / lrand48）逐字节吐出来的，P-192 到 P-521 五条全对上，只有二进制曲线 B-163 还没解开。Filippo Valsorda 三年前设的悬赏已被 Anthropic 团队领走，奖金三倍捐给了运营 Let’s Encrypt 的 ISRG。Anthropic 原文 ｜ Filippo 的文章 ｜ HN 讨论（669 分：调侃「13 年的锡纸帽，就为了 srand48(time(NULL))」；也有人冷静指出这只说明了种子怎么生成，看不到被丢弃的候选，挑选环节理论上仍可能有猫腻）

2. PostgreSQL 19 delayed, partly because AI code review found too many bugs: GA slips from the planned end of September to around November 19, and 8–10 significant features (including SQL/PGQ graph queries) are pulled back to PG 20. Core committer Peter Eisentraut says some are architectural flaws hard to fix in the beta period, and LLM review also turned up dozens of small edge-case issues at once, each fixable in three lines, but the handling overhead adds up and there isn’t enough time; he stresses these features were written before vibe coding. The Register ｜ HN discussion (the mainstream view is that AI raised the quality bar, old code needs lots of catch-up, and QA is becoming the bottleneck of the AI era; others think the truly hard ones are bugs involving design trade-offs that can’t be fixed with one click)
   4

   PostgreSQL 19 推迟发布，原因之一是 AI 代码审查找出的 bug 太多：GA 从原定 9 月底推到 11 月 19 日左右，8–10 个重要特性（包括 SQL/PGQ 图查询）被撤回到 PG 20。核心提交者 Peter Eisentraut 说，有些是难以在 beta 期修好的架构缺陷，另外 LLM 审查一下子翻出几十个边界小问题，每个三行就能修，但处理开销叠起来时间就不够了；他强调这些特性写的时候还没有 vibe coding。The Register ｜ HN 讨论（主流看法是 AI 把质量门槛抬高了，老代码要大量补课，QA 正在变成 AI 时代的瓶颈；也有人认为真正难的是那些涉及设计取舍、没法一键修的 bug）

3. Home Assistant’s “check leaked passwords by default” sparks controversy: a user found that HA compares fields flagged as passwords in each app’s config against Have I Been Pwned, and filed an issue protesting the lack of consent. The official docs say it uses k-anonymity so the server never sees the full hash; maintainer Frenck first replied combatively, later apologized on HN, and said it could always be turned off with ha security options --pwned=false. GitHub issue ｜ Official explanation ｜ HN discussion (most think the intent is fine but it should be off by default and opt-in; one posted research saying k-anonymous password lookups still leak useful signal to attackers)
   2

   Home Assistant「默认检查泄露密码」惹争议：有用户发现 HA 会把各个 App 配置里标记为密码的字段拿去和 Have I Been Pwned 比对，于是提 issue 抗议没经过同意。官方文档说用的是 k-匿名，服务端看不到完整哈希；维护者 Frenck 起初回复得很冲，后来在 HN 道歉，并说其实一直能用 ha security options --pwned=false 关掉。GitHub issue ｜ 官方说明 ｜ HN 讨论（多数人认为出发点没错但应该默认关闭、让用户主动打开；有人贴了研究说 k-匿名的密码查询仍会给攻击者泄露有用信号）

4. Two kernel vulnerabilities, one letting you escape from a VM to the host: Linux networking stack CVE-2026-99999 (CVSS 9.3) is in deferred checksum handling, where a malicious guest can make the host treat a 20-byte IPv4 header as 60 bytes and read memory out of bounds, ultimately achieving escape in KVM; the research team found it while attempting Vercel’s million-dollar sandbox bounty, and it is fixed in 6.12.113, 6.18.57, 7.2.11 and others; those running cloud hosts and agent sandboxes should upgrade soon. The other is Calif’s OpenBSD local privilege escalation: a 16-bit signed counter carried over for 37 years from the original BSD code overflows if you fork enough times. pwn.ai original ｜ Debian security tracker ｜ ITHome ｜ Big Fish ｜ HN discussion (someone found NetBSD fixed it in passing when it rewrote that code in 1999)
   3

   两个内核漏洞，一个能从虚拟机逃到宿主机：Linux 网络栈的 CVE-2026-99999（CVSS 9.3）出在延迟校验和处理上，恶意客户机能让宿主机把 20 字节的 IPv4 头误当成 60 字节，越界读内存，最终在 KVM 里实现逃逸；研究团队是在挑战 Vercel 百万美元沙箱悬赏时挖到的，已在 6.12.113、6.18.57、7.2.11 等版本修复，跑云主机和 Agent 沙箱的要尽快升级。另一个是 Calif 披露的 OpenBSD 本地提权：一个从原始 BSD 代码沿用了 37 年的 16 位有符号计数器，fork 足够多次就会溢出。pwn.ai 原文 ｜ Debian 安全追踪 ｜ IT之家 ｜ Big Fish ｜ HN 讨论（有人发现 NetBSD 在 1999 年重写那段代码时顺手修掉了）

5. Anthropic reported to be pitching Congress an AI safety bill it drafted itself: per Politico, the draft lets the government block release or use of a model only when there is a “foreseeable, imminent” major risk, with a threshold of over $1B in losses or 50+ casualties, and the risk must come from specific circumstances such as bioweapons or a model slipping out of its developer’s control. Many in the AI safety community criticize it for narrowing the government’s power to intervene too much. Politico ｜ Techmeme
   3

   Anthropic 被曝向国会推销自己起草的 AI 安全法案：据 Politico，草案只允许政府在模型存在「可预见、迫在眉睫」的重大风险时阻止发布或使用，门槛是超过 10 亿美元损失或 50 人以上伤亡，而且风险要来自生物武器、模型脱离开发者控制这类特定情形。AI 安全圈不少人批评它把政府的干预权收得太窄。

Chinese Community

1. V2EX “Share Creations” baocode: the author fully ported the Monaco editor and xterm to pure Dart with Flutter, producing an IDE that feels close to VS Code with an installer of only a dozen or so MB, powered by Claude Code, supporting remote SSH (using the server’s Claude Code) and custom APIs, and a phone version too. A commenter says he’s used it for a week and it’s smooth; another reports symlinked files not showing after remote open and terminal errors.

   3

   V2EX「分享创造」baocode：作者用 Flutter 把 Monaco 编辑器和 xterm 完整移植成了纯 Dart，做出一个手感接近 VS Code 的 IDE，安装包只有十几 MB，内核接的是 Claude Code，支持远程 SSH（用服务器上的 Claude Code）和自定义 API，还有手机端。评论区有人说已经用了一周很顺；也有人反馈远程打开后不显示软链接文件、终端有报错。帖子 ｜ GitHub

2. Alibaba Qwen open-sources BlenderWorld: a benchmark testing visual agents on real 3D creation in Blender, requiring an editable .blend project rather than an image. 223 tasks and 2,651 rubric items across 9 major categories including products, architecture, characters and animation, rendered from 8 viewpoints and scored half on rubric conformity and half on visual quality, MIT-licensed.

   3

   阿里 Qwen 开源 BlenderWorld：一个考视觉 Agent 在 Blender 里做真实 3D 创作的基准，要求交出可编辑的 .blend 工程而不是一张图。共 223 个任务、2,651 条评分标准，覆盖产品、建筑、角色、动画等 9 大类，从 8 个视角渲染后按规则符合度和视觉质量各占一半打分，MIT 许可。IT之家 ｜ GitHub

（夜间 Launch HN 无新帖；Product Hunt 当天榜单偏消费和营销工具，Muse Spark Open、Nemotron 3.5 Super VL 之前已写过。GitHub Trending 和 HF 热榜大多是前几天写过的老面孔（claude-plugins-official、ECC、superpowers、VoiceStudio、FLUX 3 Klein 等）。Lobsters 的新内容和 HN 重合，另有 GNOME OS Preview、Codeberg Pages v2 下线。HelloGitHub 和阮一峰周刊没有新一期。Anthropic 员工与 AI 工时报告、Claude Code 代码审查博客、量子电路、Replit 与 Anthropic 分手、Elorian Glimpse 已在今早的 X AI 汇总里写过，这里不重复。）

新产品 / 开源

1. Llmspeed (Show HN): before buying a GPU or Mac, “feel” a local model’s output speed in the browser. Pick hardware (RTX 3090/5090, Strix Halo, Mac Studio M5 Ultra, DGX Spark, etc.) and a model (Qwen 3.8 27B, DeepSeek V4 Flash, etc.) and it simulates text streaming at the estimated tokens/s; data comes from community measurements such as r/LocalLLaMA and localscore.ai, missing combinations are extrapolated from similar configs or specs, and the code is open source. Website ｜ GitHub ｜ HN discussion (133 points: one says his M2 Ultra is twice as fast as the page’s M3 Ultra number, so estimate accuracy is the key; agent-coding people remind that prefill and concurrency matter more than output speed; another suggests just renting a cloud GPU for a few minutes, for under $0.5)
   3

   Llmspeed（Show HN）：买显卡或 Mac 之前，先在浏览器里「体感」一下本地模型的出字速度。选硬件（RTX 3090/5090、Strix Halo、Mac Studio M5 Ultra、DGX Spark 等）和模型（Qwen 3.8 27B、DeepSeek V4 Flash 等），它按估算的 token/s 模拟出字速度；数据来自 r/LocalLLaMA、localscore.ai 等社区实测，缺的组合按相近配置或规格推算，代码开源。官网 ｜ GitHub ｜ HN 讨论（133 分：有人说自己的 M2 Ultra 比页面上 M3 Ultra 的数还快一倍，估算准不准是关键；做 Agent 编程的人提醒预填充（prefill）和并发比输出速度更重要；还有人建议直接租台云 GPU 试几分钟，花不到 0.5 美元）

2. Orca: a desktop app for managing “a swarm of coding agents”: Codex, Claude Code, OpenCode and Pi can run side by side, each in its own git worktree; you can send one prompt to five agents, compare the results and merge the best. It also has WebGL terminal split panes, a design mode where you click page elements to put HTML/CSS and a screenshot into the prompt, and a companion phone app to view progress and send follow-ups. Uses your own subscriptions, MIT-licensed, 1100+ stars in a single day on GitHub Trending today. GitHub ｜ 官网
   4

   Orca：一个管理「一群编程 Agent」的桌面应用，Codex、Claude Code、OpenCode、Pi 可以并排跑，每个在独立的 git worktree 里；可以把同一个提示分给五个 Agent，比较结果后合并最好的那个。还带 WebGL 终端分屏、点选网页元素把 HTML/CSS 和截图塞进提示的设计模式，以及能在手机上看进度、发追问的配套 App。用你自己的订阅，MIT 许可，今天 GitHub Trending 单日 1100+ 星。

3. ai-job-search: a job-search pipeline a geophysicist built with Claude Code after being laid off: /scrape searches jobs, /apply evaluates fit and drafts a tailored LaTeX résumé and cover letter (with a reviewer agent as a gate), and /interview prepares for interviews. The author says he applied to 69, got 20 first-round interviews, and started as an AI engineer in June 2026. The job-scraping part targets Danish job sites, but the flow itself is language- and country-agnostic and can be swapped for your own job sites; MIT-licensed, about 1400 stars in a day on Trending today. GitHub
   3

   ai-job-search：一位地球物理学家被裁后用 Claude Code 搭的求职流水线，/scrape 搜职位、/apply 评估匹配度并起草定制的 LaTeX 简历和求职信（另有一个审稿 Agent 把关）、/interview 准备面试。作者说自己投了 69 份、拿到 20 个一面，最后在 2026 年 6 月入职 AI 工程师。职位抓取部分针对丹麦招聘站，但流程本身和语言、国家无关，可以换成自己的招聘站，MIT 许可，今天 Trending 单日约 1400 星。

4. Google’s official android/skills: a set of Agent Skills (standard SKILL.md format) maintained by the Android team, written to developer.android.com best practices, deliberately covering only scenarios where evals show large models tend to go wrong (e.g. R8 obfuscation analysis, Wear OS Compose) and not repeating what models already know, like basic Compose. Install them into every agent detected on your machine at once with the Android CLI’s android skills add --all, or install as a Claude Code plugin; Apache 2.0. GitHub ｜ 官方文档
   4

   Google 官方 android/skills：Android 团队维护的一套 Agent Skills（标准 SKILL.md 格式），按 developer.android.com 的最佳实践写，刻意只覆盖评测显示大模型容易做错的场景（比如 R8 混淆分析、Wear OS Compose），不重复写基础 Compose 这类模型已经会的东西。可以用 Android CLI 的 android skills add --all 一次装进本机检测到的各个 Agent，也能当 Claude Code 插件装，Apache 2.0。

Tech News

1. More and more websites are shutting AI agents out: The Verge rounds up recent blocks encountered by users of agents such as Meta Muse, xAI Grok, OpenAI Dots and Instinct: Yelp lets through only agents that paid for a data license, Delta Air Lines doesn’t let agents book flights, and some services ban agents from logging into accounts for security reasons; even where not explicitly banned, anti-bot systems like Cloudflare often block agents as bots. Meta chose to sign partnerships with Walmart, Gap, Best Buy and others so Muse can shop. For people building agent products, “which sites you can get into” is becoming a real capability ceiling. The Verge
   3

   越来越多网站把 AI Agent 拒之门外：The Verge 汇总了最近 Meta Muse、xAI Grok、OpenAI Dots、Instinct 等 Agent 用户遇到的封锁：Yelp 只放行付费买数据授权的 Agent，达美航空不让 Agent 代订机票，还有一些服务以安全为由禁止 Agent 登录账号；即便没明确禁止，Cloudflare 这类反爬系统也经常把 Agent 当机器人拦下。Meta 选择和沃尔玛、Gap、百思买等签合作让 Muse 购物。对做 Agent 产品的人来说，「能上哪些网站」正在变成实际的能力上限。

2. Anthropic borrows $60B to buy Google TPUs, paying Blackstone over 10% interest: per the FT, Blackstone plans to subscribe $9B of it, and the high borrowing cost makes it harder for Anthropic to get an investment-grade rating. Ed Zitron and others criticize the deal as very risky. FT ｜ Reuters ｜ Techmeme
   3

   Anthropic 为买 Google TPU 借 600 亿美元，给黑石开出 10% 以上的利率：据 FT，黑石拟认购其中 90 亿美元，较高的借贷成本让 Anthropic 想拿到投资级评级更难。Ed Zitron 等人批评这笔交易风险很大。

3. Meta admits its smart glasses record without being woken: users found that without saying “Hey Meta” and with the relevant settings off, Ray-Ban Meta glasses’ indicator light came on and recorded and transcribed conversations, reproduced on three second- and third-generation pairs, and commenters have hit it too; Meta has confirmed it and says it is fixed. Mashable ｜ HN discussion (comments are basically “no surprise”, with some thinking Meta’s trust problem will drag down the whole smart-glasses category)
   2

   Meta 承认智能眼镜会在没唤醒的情况下录音：有用户发现在没说「Hey Meta」、相关设置也关着的情况下，Ray-Ban Meta 眼镜的指示灯亮起并录下、转写了对话，而且在三副二代、三代眼镜上都复现了，评论区也有人遇到过；Meta 已确认并称已修复。Mashable ｜ HN 讨论（评论基本是「毫不意外」，有人认为 Meta 的信任问题会连累整个智能眼镜品类）

4. Mercor: 90% of US work hours fall on tasks AI benchmarks barely test: Mercor mapped existing AI benchmark questions onto the US Department of Labor’s O*NET occupations and tasks and found very uneven coverage skewed toward occupations with large total wages: lawyers’ tasks are heavily covered, but legal secretaries and clerks, equally exposed to AI, are barely tested, and long-tail middle-income knowledge work is basically blank. This hit HN before this period started and wasn’t covered before. Original ｜ HN discussion (541 points: the top view is that most human work lacks the fast feedback loops training and agents need, so productivity gains look more like the old 2%-a-year pace; one engineer jokes that 80% of his time goes to reconciling opinions)
   3

   Mercor：美国 90% 的工作时长落在 AI 基准几乎没测过的任务上：Mercor 把现有 AI 基准的题目映射到美国劳工部 O*NET 的职业和任务上，发现覆盖很不均匀，偏向工资总额大的职业，比如律师的任务被大量覆盖，但同样容易被 AI 影响的法律秘书、书记员几乎没人测，长尾的中等收入知识工作基本是空白。这条是本时段开始前上的 HN，之前没写过。原文 ｜ HN 讨论（541 分：高赞观点是人类大部分工作缺少训练和 Agent 需要的快速反馈闭环，所以生产率提升更像每年 2% 的老节奏；也有工程师自嘲 80% 的时间在调和各方意见）

5. Claude Haiku 4.5 will be removed from the Claude app’s model list: from October 29, Haiku 4.5 can no longer be selected on web, desktop and mobile or in Cowork and Design for Free, Pro and Max users; selected ones switch to Haiku 5.5 automatically and old conversations are kept; Claude Code, Team/Enterprise and the API are unaffected.

   2

   Claude Haiku 4.5 将从 Claude App 的模型列表下线：10 月 29 日起，Free、Pro、Max 用户的网页、桌面、移动端及 Cowork、Design 里不再能选 Haiku 4.5，已选的会自动切到 Haiku 5.5，旧对话保留；Claude Code、Team/Enterprise 和 API 不受影响。IT之家

Chinese Community

1. V2EX “Share Creations” Iterlay: an open-source desktop tool that puts reference images, image generation, editing, versions and asset organization on one infinite canvas, with a CLI and Skill so Codex and Claude Code can operate it directly. The author’s recommended use is to have an agent generate a set of pages or product assets, compare and partially edit them on the canvas, then export to the project’s assets directory. Bring your own model, boards stored locally, MIT-licensed.

   2

   V2EX「分享创造」Iterlay：把参考图、生图、改图、版本和素材整理放在一张无限画布上的开源桌面工具，提供 CLI 和 Skill，让 Codex、Claude Code 能直接操作。作者推荐的用法是让 Agent 生成一组页面或产品素材，在画布上比较、局部修改，再导出到项目的 assets 目录。模型自己配，画板存在本地，MIT 许可。帖子 ｜ GitHub

2. LinuxDo: Codex Harbor: a launcher for the Codex desktop app on macOS where the account and the session directory are chosen separately, so a session started with account A can be continued with account B or your own Responses API; supports multiple accounts open at once, shows each account’s 5-hour and weekly quota and reset time before launch, and sessions can live on an external drive. The author says it’s in public beta, MIT-licensed.

   3

   LinuxDo：Codex Harbor：一个 macOS 上的 Codex 桌面版启动器，账号和会话目录分开选，于是可以用 A 账号开的会话换 B 账号或自己的 Responses API 接着聊；支持多账号多开，启动前就能看到每个账号的 5 小时和周额度、重置时间，会话也能放在移动硬盘上。作者说还在公测，MIT 许可。帖子 ｜ GitHub

3. InfoQ: Akka tested “spec-driven” AI code porting on 65 open-source projects: the first round ran 99.3 hours and 9.41 billion tokens, with 57 projects improving in code size or performance. The more practical conclusions: structured specs with declarations, evidence and typed behavior clearly raise first-implementation quality; the main bottleneck is the lack of cross-component design decisions in context; Opus uses about 40% fewer tokens than Sonnet but takes twice as long, and raising the thinking level brought no consistent gain.

   4

   InfoQ：Akka 用 65 个开源项目实测「规格驱动」的 AI 代码移植：首轮跑了 99.3 小时、94.1 亿 token，57 个项目的代码量或性能有改善。结论里比较实用的几点：带声明、证据和类型化行为的结构化规格能明显提高第一次实现的质量；主要瓶颈是上下文里缺少跨组件的设计决策；Opus 比 Sonnet 省约 40% token，但耗时翻倍，调高思考档位也没有带来持续收益。InfoQ 中文 ｜ 英文原文

（本时段 HN 首页的大新闻是 DeepSeek-V4.1-Flash-1017，已在今晚的 X AI 汇总里详细写过，这里不重复；Llama.cpp 跑在 PS5 上、FLUX 3 Klein、Claude 解 ECDSA 种子等也是之前写过的。Launch HN 没有新帖；Product Hunt 当天榜单偏消费工具；Hugging Face 热榜和 GitHub Trending 其余多是前几天写过的老面孔（claude-plugins-official、ECC、VoiceStudio、CLI-Anything、xerj、ai-memory 等）。36氪、量子位、开源中国在本时段没什么值得写的新稿；少数派只有一篇 HRV 心率变异性的文章；V2EX 热门和掘金热榜以闲聊和旧文为主；HelloGitHub 和阮一峰周刊没有新一期。）