搜索“OpenAI”

igured to use that local LLM server: { "$schema": "https://opencode.ai/config.json", "model": "meta-models/Muse-Spark-Open-816B-A42B-NVFP4", "provider": { "mlx": { "npm": "@ai-sdk/openai-compatible", "name": "Local MLX", "options": { "baseURL": "http://127.0.0.1:8080/v1" }, "models": { "meta-models/Muse-Spark-Open-816B-A42B-NVFP4": { "name": "Muse Spark Open 816B (MLX)" } } } } } Many other local agentic AI experiences can be enabled through a similar approach. Conclusion Over the past year, we

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、提交历史和开发团队成员的头脑中。 OpenWiki 实测：编码智能体的记忆 指南：用来自互联网的最新事实充实本地 AI 模型 链式操作如何演变为风险：AI 智能体中的传话游戏 开放权重模型 DeepSeek-V4.1-Flash 里有什么 简要说明：N-gram 如何工作 AI 炒作：为何数据中心的超大规模扩建正受到批判性审视 失控的 AI 智能体：为何 OpenAI 等公司在美国不承担法律责任 关于 GPT-6.1 Astra 的评论：失控 AI 的障眼法 人工智能如何影响 DevOps 转型 AGENTS.md 或 CLAUDE.md 文件很适合用于简短的工作规则，例如安装命令、测试、风格规范和安全边界。AGENTS.md 可理解为一种面向智能体的 Readme，在 monorepo 中甚至允许使用具有局部作用范围的嵌套文件——即子目录中的额外 AGENTS.md 文件，例如 packages/api/AGENTS.md。尽管如此，单个起始文件仍难以充当不断增长的架构手册。数百页的上下文既不应放入起始提示，也不应放入智能体在每项任务中都要完整读取的文件。 以上就是我们 heise

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Sept 2026; Launch of UK’s ‘largest AI supercomputer’ delayed by power supply problems24 Sept 2026; Nick Clegg plays down fears ‘godlike’ AI could exterminate humanity24 Sept 2026; OpenAI’s George Osborne says datacentre nimbys holding back Britain22 Sept 2026; ‘A critical moment’: concern UK is not up to speed in acting on AI risks18 Sept 2026; Louise Haigh: UK must heed warnings from AI experts15 Sept 2026; Jack Thorne warns some fellow scriptwriters are using AI ‘to cheat’14 Sept 2026 Most vie

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nfield apps. At low effort on a from-scratch build, Gemini 4 built @kieranklaassen a working webcam heart-rate monitor in 35 minutes—matching Claude Opus 5.5’s quality and beating OpenAI’s models on that test. Where it cuts corners is self-verification and existing codebases. In 17 of Kieran’s 38 benchmark runs, it opened and closed its code-review step just 30 milliseconds apart instead of inspecting its changes. And on my Senior Engineer benchmark—refactoring a vibe coded slop codebase—it work

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e’ll follow up with a blog post on how we collect feedback and close the optimization loop. Wiring Things Together The first thing we noticed was that Cursor lets you override the OpenAI base URL and model names. Perfect. TensorZero exposes an OpenAI-compatible inference endpoint, so we can easily configure Cursor to call TensorZero instead of OpenAI. In the TensorZero configuration, we define a TensorZero function cursorzero so that we can automatically experiment with different models and prom

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e’ll follow up with a blog post on how we collect feedback and close the optimization loop. Wiring Things Together The first thing we noticed was that Cursor lets you override the OpenAI base URL and model names. Perfect. TensorZero exposes an OpenAI-compatible inference endpoint, so we can easily configure Cursor to call TensorZero instead of OpenAI. In the TensorZero configuration, we define a TensorZero function cursorzero so that we can automatically experiment with different models and prom

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Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 afte

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Fable 5.1 at high thinking. Jev: 67.8% mean accuracy, $0.0004 per case, 0.4 seconds; Claude Sonnet 5 (same workflow): 67.8%, $0.1174 per case, 78.1 seconds; Best comparison model (OpenAI “sol”): 74.1%, $0.0836 per case, 23.3 seconds Jev matched Sonnet 5 on accuracy at a fraction of the cost and latency. It still trails the top frontier configuration by 6.3 points. Per task, Jev scored 76.0% on customer service but only 61.8% on invoice processing. The rule of thumb is simple. If your code needs

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你推销（a）一门毫无用处的提示词工程课程，（b）又一个管理你上下文的SaaS工具，（c）或者问你为什么还没试试几秒前刚发布的新模型——我们今天不会这么做。 相反，我们将带你了解真正有效的方法：运行一个像样的后厨。多智能体工作流。 近几周来，多智能体工作流变得实用得多，原因有几个：底层模型变得更好了，而且流行的AI编程智能体让多智能体编排更容易搭建。上个季度，OpenAI在Codex工作流中推出了更深入的编排，而Anthropic则继续扩展Claude Code和MCP生态。 不过，最大的突破是速度。Codex Spark的运行速度约为每秒1,200个token，这使得引入并行和验证步骤变得切实可行，否则这些步骤的时间成本会过高。 以一个使用Codex和Figma MCP将网站复制到Figma的示例任务为例，单智能体工作流平均每次运行耗时36.5分钟，平均需要12次人工干预（且失败率100%），而多智能体工作流每次运行耗时5.2分钟，仅需2次人工干预，并且首次尝试即成功。 多智能体工作流在架构层面解决了单智能体的天花板问题。不再是一个厨师包揽一切，而是由一位主厨接单，将其拆解为范围明确、

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chines, and sandboxes. The Codex CLI enforces these rules during runtime with little to no configuration needed from the end user. Explore similar tools to see what works for you. OpenAI describes a "doc-gardening" agent that scans for stale documentation and opens fix-up PRs, enforced via CI checks. Documentation is critical for increasing the likelihood that the model will produce high-quality code that works on the first run. When inference wait times are negligible, generating and refining d

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y streaming response without any extra request parameter, as described in the usage accounting docs. usage.cost is the amount we charged your account for that request, in USD. The OpenAI Python SDK keeps response fields it doesn’t define, so r.usage.cost is available as an attribute. A response can carry no text content, for example when the model refuses, so the script treats missing content as an empty answer, counts it as incorrect, and keeps its cost in the total. A model that scores zero ha

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tter off. There comes a point, though, when labs are willing to internalize this cost. At their scale, frontier labs are not willing to trust a neocloud with so much of the stack. OpenAI, for instance, has posted extremely insightful blogs about its difficulties scaling Kubernetes; the innovations described would have been simply impossible if they were renting from a neocloud who didn’t let them into the K8s control plane. As a current Anthropic job posting puts it: We are operating at a scale

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@OpenAIDevs GPT-6 Astra + Tripo + @UnrealEngine MCP = A playable medieval city in UE5. Our new blog post details the exact workflow to batch-generate 3D assets and assemble a complete game scene. Check out the complete workflow here: https://t.co/rCAOyihNYB

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ists 24/7 priority support with dedicated account management; nothing comparable to forward-deployed engineers is published. The largest public proof point for all four answers is OpenAI building ChatGPT's Advanced Voice on LiveKit. Read it as a case study rather than a benchmark; end-to-end results in any deployment still depend on user location, network quality, the models you pick, and how your tools behave. Put the four answers together and the pattern is obvious. Pipecat gives you a framewo

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originally developed the Model Context Protocol in 2024. They released it as an open standard for all LLMs to use and donated it to the Agentic AI Foundation. ChatGPT’s developer, OpenAI, has since opened ChatGPT to MCP calls. What does MCP do exactly? The Model Context Protocol defines a clear structure for LLMs and other computing resources to communicate with each other. It provides a standard interface language that all LLMs can use, and all third-party tool providers can use to share their

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th LLMs. n8n’s visual debugging and credential management offer production advantages that save time. n8n is provider-agnostic, so you can swap AI models as you see fit. Move from OpenAI to Anthropic or a self-hosted model, and the MCP setup underneath won’t change. Calling any API with the HTTP Request node The HTTP Request node covers any service without a dedicated node. Set the request by hand or paste a curl command from the vendor's documentation and the system fills the fields. For suppor

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A lot has happened in the last few weeks. I am sure that OpenAI’s GPT-6 Astra is top of mind for everyone right now. In particular, thoughts on its performance, the looped transformer/recurrent depth aspects, and rumors that Astra is “hiding” its reasoning trace (i.e., chain of thought). So, in this article, I want to start with some brief impressions of Astra and some thoughts on where all this is headed. Then, I will discuss, in detail, what “looped transformers” are, and how (or rather, if) t

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ide, infosec's a good bet for a long, stable career FOSS NetBSD may have found a new role: The AI-free FOSS OS Increasing awareness and adoption, followed by increasing donations; OpenAI dots inspire open source imitators amid technical difficulties Persistent agents appeal to AI companies, but demand isn't obvious; Ubuntu 'Stonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raski

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s such as model, Task Type, and GPU. Together, they support Retrieve, Compare, and Filter without forcing each Task to rediscover the same facts. In an earlier version of SGLang's OpenAI-compatible chat benchmark handler, streamed delta.reasoning content was not included in TTFT and output accounting (fixed in sgl-project/sglang 23954); non-stream handling was addressed separately in 25298. The Journal surfaced this historical record in a later Task, preventing a measurement error from being mis

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境。由于训练和推理运行在同一套协同优化的后端上，你的生产性能每次都与训练基准一致。 关于 Deepseek 的 Harness，你需要了解什么 Deepseek 自己发布的基准运行使用 DeepSeek Harness（dsh），这是一个原生智能体运行时，处理从模型适配器到核心执行循环的一切。它使用 Deepseek V4 的原生工具语义，而不是通过通用的 OpenAI 风格接口来路由调用。对于这个模型，Deepseek 也没有发布标准聊天模板，因此微小的序列化不匹配可能会悄无声息地降低模型性能。 在 Fireworks 上测试期间，我们发现“最小优先”的设置比直接跳到完整的标准目录能产生更一致的工具调用。从裸 bash 和编辑器界面开始，再升级到 dsh 标准，能让 DeepSeek V4 Pro 更接近 DeepSeek 在自己基准运行中使用的设置。 无论你是想降低自主智能体的单位成本、运行零拒绝的安全审计，还是构建专用领域模型，DeepSeek V4 Pro 都能以每解决任务一半的成本为你提供前沿级能力。在 840 次有记录的对抗性安全运行中，它实现了零拒绝和零输出长度截断，保证

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