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10月20日周二
  1. Lisan al Gaib30

    我需要更多这样的内容 我们需要更全面地看待 AI 的进步 @OpenAI:我们最新的模型进展带来了更强的能力,可用于构建和迁移 COBOL、Fortran 和 Visual Basic 等低资源编程语言编写的软件。 这些语言支撑着银行、政府和企业所依赖的软件。这方面的进展意味着理解数十年的业务逻辑,帮助开发者实现这些系统的现代化,同时保留仍然有效的部分。 我们正围绕开发者和企业客户带来的挑战构建这些能力。如果你的团队正在处理这类系统,请告诉我们接下来应该攻克哪些语言和工作流。 https://openai.com/index/low-resource-programming-languages/

    引用OpenAI@OpenAI

    Our latest model advances bring stronger capabilities for building and migrating software in low-resource programming languages such as COBOL, Fortran, and Visual Basic. These languages power software that banks, governments, and businesses depend on. Progress here means understanding decades of business logic and helping developers modernize those systems while preserving what works. We’re building these capabilities around the challenges developers and enterprise customers bring us. If your team is working on these systems, tell us which languages and workflows we should tackle next. https://openai.com/index/low-resource-programming-languages/

10月19日周一
10月18日周日
  1. Lisan al Gaib17

    它们又消失了

    引用Lisan al Gaib@scaling01

    these just appeared in the OpenAI API: aurora-blue-alpha aurora-green-alpha aurora-red-alpha there was an openrouter model called aurora-alpha earlier this year which was likely GPT-5.3-Codex-Spark so these could also be specialized codex coding models, or high-speed inference versions of GPT-6 models

10月17日周六
  1. OpenAI Developers42

    以下是我们 OpenAI 的工程师如何使用 Codex 深入我们的代码库、找出需要关注的地方,并逐步清理维护积压:

    引用Rodrigo Roim@rrroim

    Software maintenance is changing. A few months ago, I had to tell Codex exactly where to look for dead code. Now it can find candidates in Slack, check them against production telemetry, and draft cleanup PRs. Here’s how we do this at OpenAI. https://developers.openai.com/blog/maintaining-software-with-the-gpt-6-family

  2. Sam Altman38

    很高兴看到 Nick 的这篇分享! @nickcammarata:可解释性可能是当下世界上最重要的科学问题,因为如果我们不知道自己正在构建什么,就无法解决对齐问题。我想我对此有好消息。我曾在 OpenAI 参与机械可解释性的早期工作,当时得出的结论是悲观的,因为神经网络似乎会折叠成任意纠缠的结。多年后,我启动了 OpenAI 首个大规模自动化可解释性项目,用当时原始的工具发现约 2% 的神经元是可读的。 借助现代智能体工具和更细致的观察,情况看起来完全不同。在参数量高达数万亿的模型中,绝大多数神经元都是可读的,它们的回路很优美。许多著名的纠缠神经元案例其实是被误解了。我预想的障碍大多并不存在。我现在认为,雄心勃勃的可解释性研究会在我们最需要它的时候被解决,而且可能相当快,因为自动化科学家在不断进步。这对可解释性或许是一个苦涩的教训:简单的东西,在荒谬的规模下,是有效的。 https://already-clean.goodfire.com/

    引用Nick@nickcammarata

    interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works https://already-clean.goodfire.com/

10月16日周五
  1. Anthropic40

    Anthropic 承诺提供 500 万美元资金、算力和 Claude 访问权限,帮助数学界应对 AI 带来的巨大变化。该资助与 Simons Foundation 合作,由后者以社区驱动方式管理,用于支持工作坊、会议和领域专家讲座。Simons Foundation 同时宣布投入 1500 万美元,并与 Anthropic、OpenAI 建立新合作。

    引用Simons Foundation@SimonsFdn

    How can the math community forge the best path forward in the AI age? We're committing $15M to workshops and initiatives — plus free AI computing resources for foundation-supported researchers — through new partnerships with @AnthropicAI and @OpenAI: https://bit.ly/4zpfIbW #math #science #AI

10月15日周四
  1. OpenAI40

    OpenAI 向 Simons Foundation 新计划出资 500 万美元,用于帮助数学家检验、理解并在此基础上推进 AI 产出的结果,包括 OpenAI 近期发布的研究成果。

    引用Simons Foundation@SimonsFdn

    How can the math community forge the best path forward in the AI age? We're committing $15M to workshops and initiatives — plus free AI computing resources for foundation-supported researchers — through new partnerships with @AnthropicAI and @OpenAI: https://bit.ly/4zpfIbW #math #science #AI

10月14日周三
10月13日周二
  1. MIT Technology Review · AI40

    开放模型利与弊:Anthropic 称 GLM-5.3 构成重大网络攻击风险,开放与闭源之争何去何从

    Anthropic 发布报告称,Z.ai 于 8 月发布的开放权重模型 GLM-5.3 可被任何人自由下载和修改,构成重大网络攻击风险。开放模型支持者认为其可本地运行、成本更低、便于学术研究并防止权力集中,而 Artificial Analysis LLM 排行榜前八名目前仍均为闭源模型。

10月12日周一
  1. Andrej Karpathy60

    Andrej Karpathy 重提 4 月的一条推文,认为公众对 LLM 能力的认知差距正在扩大,已从两群人各说各话变成一条陡峭的漏斗。他估算约 6B 人几乎没接触过 LLM,1-2B 人只用免费版聊天产品,约 20M 人在数学与编程中专业使用前沿模型,而约 5,000 人能在内部接触前沿系统。

    引用Andrej Karpathy@karpathy

    Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

10月10日周六
10月9日周五
  1. OpenAI Developers58

    OpenAI Developers 宣布为 Windows 版 Codex 构建了基于 Microsoft Execution Containers(MXC)的新沙箱模式,带来更快的配置、更强的网络管控和更细粒度的文件访问控制。该模式需要兼容的 Windows 11 设备。引用内容显示,MXC 已在 Windows 11 上正式可用,用于把智能体限制在操作系统强制的边界内。

    引用Pavan Davuluri@pavandavuluri

    Today, we announced new @Windows platform capabilities and @surface devices built to empower people and organizations for the era of hybrid intelligence. Here are some of the highlights: - It all starts with trust. Microsoft Execution Containers (MXC) is now generally available on Windows 11, keeping agents contained within boundaries the operating system enforces. - HydraFusion brings hybrid intelligence to life, allowing models to run locally while seamlessly accessing the cloud when more scale or capability is needed. We're working with @github to extend HydraFusion to Windows. - This fall, Search on the taskbar adds thousands of quick actions, making it easier to adjust settings and get things done as fast as you can type. - @MSFTCopilot is getting more powerful on Windows. With hybrid intelligence, it can tap into your PC's local capabilities and turn your requests into action, while keeping you in control. - Local agents are coming to Windows too. Muse is on its way as a native Windows app with MXC integration, and Hermes, OpenClaw, and Perplexity are already building local experiences on Windows. - Surface Laptop Ultra, built with NVIDIA RTX Spark, is the most powerful Surface we've ever built. It's available for pre-order today, along with the Surface RTX Spark Dev Box and new RTX Spark PCs from our partners. Call of Duty is coming to RTX Spark in 2027. Read more on the Windows Blog: https://blogs.windows.com/windowsexperience/2026/10/07/building-windows-for-hybrid-intelligence/

10月8日周四