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10月16日周五
  1. Rohan Paul70

    WSJ 报道称头部 AI 实验室正定期为员工开展反间谍演练。OpenAI 定期提醒员工防范贿赂、咖啡馆窃听、被收买的保洁人员和美人计,要求员工不得把带公司标志的物品带出办公室,并在 Slack 上定期提醒;Anthropic 和 Google 员工也接受过类似培训。

    引用Rohan Paul@rohanpaul_ai

    FT: The OpenAI engineer charged with bugging an executive conference room had booked one-way flights to Saudi Arabia, prosecutors say. Newly unsealed filings show Abdulrahman Alfozan, his wife and a third relative held tickets for October 12, 3 days after his arrest. Prosecutors used those bookings to get the complaint sealed on October 8, arguing he had the means and incentive to flee if he learned of the case first.

  2. François Chollet65

    François Chollet 转发 @arcprize 的评测,称 Gemini 4 Argon 在 ARC-AGI-3 上表现与 Astra 相近,Google 回到前沿。引用数据显示 Gemini 4 Argon 在 ARC-AGI-3 得分 97.5%(provider adapter harness,花费 $4.8K)、标准 harness 下 49.8%($5.8K),ARC-AGI-2 得分 91.3%,ARC-AGI-1 得分 98.5%,Google DeepMind 是第二个在 ARC-AGI-3 上得分超过 95% 的前沿 AI 实验室。

    引用ARC Prize@arcprize

    Gemini 4 Argon from @Google on ARC-AGI (Verified): - ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness) - ARC-AGI-2: 91.3%, $0.32/task - ARC-AGI-1: 98.5%, $0.16/task Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3.

  3. Rohan Paul55

    ERP.AI 发布面向 AI Agent 的企业软件平台,座位、应用、角色和手动录入均免费,而 API 访问、工作流、Agent 和 AI 用量按预付积分计费。

    引用sphinx@protosphinx

    For 25 years, Salesforce, SAP and enterprise software were built around one assumption: humans operate the software. We’re changing that. Introducing ERP•AI and Proto: business software built for agents, and an agent that operates business software. Describe the system you need. ERP•AI builds it. Then Proto takes over. It manages records, runs workflows and executes actions inside the software you just created. This is Headless SaaS. 1: get ERP•AI 2: build your SaaS 3: post below Best one wins a MacBook. First 100 decent entries get $100. (a message from Steve Papa / founding team)

  4. fal60

    fal 宣布 NVIDIA Nemotron 3.5 Super VL 多模态模型上线其平台,该开源权重模型总参数 120B、每 token 激活 12B,面向视觉推理和智能体工作流。能力涵盖图像、文档和视频理解,以及视觉推理、工具调用、文档智能和 AI 智能体场景。

    引用NVIDIA AI@NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵

10月15日周四
  1. StepFun67

    阶跃星辰转发 FeatherlessAI 的消息,Step 5 Preview 已上线该平台,以 FP8 精度提供服务。该开放权重模型为 600B 稀疏 MoE 架构、每 token 激活 27B,面向长程智能体工作,在软件工程和专业知识工作(尤其金融)上有前沿级表现,按 Artificial Analysis 数据其每任务成本约为 Claude Opus 5.5 的 1/6,智能水平与 Kimi K3 相当。

    引用Featherless AI@FeatherlessAI

    Step 5 Preview 来了。 @StepFun_ai 的新开放权重模型树立了新的帕累托前沿,在 @ArtificialAnlys 智能指数上与 Kimi K3 持平! 它为长时程智能体工作而打造,在软件工程和专业知识工作(尤其是金融)方面取得了前沿级成果。 - 600B 稀疏 MoE,每 token 激活 27B - 据 Artificial Analysis,每任务成本约为 Claude Opus 5.5 的 1/6 现已在 Featherless 上线,以 FP8 提供服务。 https://featherless.ai/model-pages/step-5-preview

    推荐理由:引用内容给出 Step 5 Preview 的参数规模、成本对比和部署入口,读者可据此评估其性价比与获取方式。

  2. Unsloth AI63

    NVIDIA 发布两款开源模型 Nemotron-3.5-Bolt 和 Nemotron-3.5-Super-VL。Bolt-74B-A9B 可在 24GB RAM 上运行,Super-VL-120B-A12B 需 82GB RAM,结合 NVIDIA 官方介绍,Super VL 支持文本、图像、文档和视频理解,并具备推理与工具调用能力,面向长时运行的 AI 智能体。Unsloth 已提供两款模型的 GGUF 下载和使用指南。

    引用NVIDIA AI@NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵

  3. lauren45

    这里有一些 pstack 强力提示词,你可以试试来升级你的代码,先从这条开始(提示词在帖子底部) 重构,让你的代码对智能体更友好 https://x.com/poteto/status/2106542593656111276?s=46&t=HFNOJ7P2JmfjDfKskFwLUw

    引用lauren@poteto

    it's easy to fall into the trap of micromanaging your agents instead of correcting the environment that shapes its behavior. pstack 0.15.9 now ships a new /correct skill: if you keep correcting agents for the same mistakes, it finds the pattern and fixes it with architecture, types, and checks. i like to imagine that commits rapidly flowing into the codebase are like a bonsai tree rapidly growing in whatever direction it pleases. what you want to do is not to give in to the chaos, but tame it intentionally with the right constraints 0.15.9 also has improvements to: • /architect: now includes instructions on designing agent friendly architecture • a new /benchmark-checklist skill based on @brendangregg's benchmarking checklist here's a prompt you can give to grok bot to start architecting a better codebase with pstack: /poteto-mode create a new Project agent to refactor and rearchitect my repo so that our architecture is more agent friendly. it should use both the /correct and /architect skill to look through past commits and find the most common pitfalls that agents fall into, whether its with code quality, performance, or bugs. if relevant you can also use the /recall skill to find past conversations for context. it should make a high level plan for me to review. it should also answer its own open questions by prototyping rather than defer to me. come back to me with a plan backed by real data. once i approve the plan, use either the stack or full autopilot playbook (ask me which i want) to execute

  4. lauren48

    注释是非结构化、无法强制执行的文本。智能体确实会读注释,但它们常常被忽略、会过时,还会给你的智能体留一条后路,让它用创可贴、hack 或变通方案来应付。 用这个提示词审计你的代码注释,找出通过更强的类型、更好的 API 设计来改进代码的机会,并创建 lint 规则,让注释变得不再必要! https://x.com/poteto/status/2110149626133438732?s=46&t=HFNOJ7P2JmfjDfKskFwLUw

    引用lauren@poteto

    ban and delete most if not all code comments! agents love copying what already exists. a seemingly harmless comment explaining some temporary workaround is one of the worst viruses you can have in your codebase. like a weed it'll spread quickly as a tool for agents to quickly get their job done. before you know it, your codebase is overrun with hacks and stale comments. just one more workaround bro you can use pstack's /no-comments skill as a weedkiller. it'll seek out useless comments while keeping the ones that actually should exist (like explaining a browser quirk), and figure out how to solve them on a first principles basis here's a prompt you can try asking @bot: create a project agent that uses /poteto-mode to spawn a /swarm to use /no-comments and identify useless code comments we can prune in my repo. come back with a high level plan of which of these workarounds would be best solved by refactoring and which we can straight up delete https://x.ai/bot/plugin/9717366

  5. fal64

    fal 宣布 NVIDIA Nemotron 3.5 Super VL 上线其平台。该开放权重多模态模型总参数 120B、每 token 激活 12B,面向视觉推理和智能体工作流构建,支持图像、文档和视频理解,具备视觉推理与工具调用、文档智能和 AI agents 能力。

    引用NVIDIA AI@NVIDIAAI

    Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵