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res therefore reflect reranking quality rather than first-stage retrieval performance, which we thoroughly evaluate elsewhere against nDCG and Recall. 2 The annotations and code needed to evaluate ViDoRe V3 with RCP-nDCG are available on GitHub. 3 Both sides must use the same output dimension. Compatibility also holds with Matryoshka truncation and int8 quantization, so the same pattern works with compressed indexes. Product Launch; Company News AI isn’t a shortcut. It’s how business gets ahead.

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作体験をWebとモバイルにも広げる。環境の準備はデスクトップアプリかWebで行い、準備後はモバイルでもその環境を選んでタスクを開始できる。進行中のタスクは別の端末から開き、進捗を確認したり、追加の指示を出したりできる。 従来版は「Codex Cloud（Legacy⁠）⁠」として当面残る。移行期間中、コードレビュー、セキュリティレビュー、既存のLinear・GitHub連携は従来版を使う。対応する既存環境は利用者の操作で新しい環境へ移行でき、元の環境と過去のタスク履歴は残る。 発表時の対象プランはPlus、Pro、Business、Healthcare、Education、Enterprise。dotsからCodexのクラウド環境でタスクを作成することもでき、その場合はあらかじめCodex側で環境を作っておく必要がある。 レビュー画面を刷新⁠、GitLab対応はプレビュー ChatGPTデスクトップアプリのコードレビュー画面を刷新した。Code Reviewプラグインでは、複数のリポジトリから自分やチームに依頼されたレビューを探し、プルリクエストの概要、差分、コメント、チェックの結果を

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for other languages. We will soon open-source the evaluation scripts, much like the Open ASR Leaderboard repo , so that you can directly provide your feedback and suggestions via GitHub Issues and PRs! Let's shape TTS evaluations together 🤗 Models mentioned in this article 10 FunAudioLLM/Fun-CosyVoice3-0.5B-2512 Text-to-Speech • Updated Feb 3 • 198k • 651 Qwen/Qwen3-ASR-1.7B-hf Automatic Speech Recognition • 2B • Updated Jul 22 • 357k • 114 Supertone/supertonic-3 Text-to-Speech • Updated May 18

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itLab, and incident.io, giving you more ways to manage and resolve issues, feature requests, and incident follow-ups. We launched Mastra Factory earlier this year with support for GitHub, Linear, and Slack, and followed up with the beta launch earlier this month. Today, Jira, GitLab and incident.io join the intake lineup. Watch our recent workshop Build and Own Your Software Factory to learn more. Every intake provider ships as part of the @mastra/factory package: Requires @mastra/factory@0.16.0

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dex命令行工具还新增了/agents界面。 一个任务可以拆给多个Agent并行处理，用户在一个界面里查看进度和结果。 代码评审也成了正式功能。 用户可以在ChatGPT桌面端查看代码改动和差异，让Codex排查问题；开启自动评审后，Codex还能在云端先完成一轮检查。 安全方面， OpenAI推出Codex Security Cloud，可以按需或定期扫描GitHub代码仓库，持续检查新增提交，发现风险后进一步核查并生成修复方案。 到这里，Codex已经从“帮你写代码”，扩展到云端执行、多Agent协作、代码评审和安全扫描。 下一步，OpenAI还准备 把上述的Codex能力开放给外部开发者 。 Codex的Agent能力开始塞进API Agents API这边也值得关注。 这次OpenAI 给API加入了Computer Use ，开发者可以通过API构建能操作软件界面的Agent，让AI自己看界面、点击、输入并完成任务。 同时，Codex里的几项核心能力也被搬进Agents API，包括multi-agent、tool search、tool calling和context co

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uick developer docs cover prompt configuration, flow authoring, and agent setup in detail; Clone the sample code — The aws-samples/sample-quicksuite-kiro-quickstarts repository on GitHub includes Quick API scripts and templates you can use as starting points About the authors Daiquan N’kere Daiquan is a Technical Account Manager and AI/ML Business Applications Specialist at AWS, where he partners with enterprise customers to architect cloud solutions and accelerate their digital transformation j

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g. This keeps people in the loop exactly where judgment matters most. Amazon Bedrock AgentCore documentation — for deploying and managing AI agents at scale; Strands Agents SDK on GitHub — for building multi-model agent workflows; Amazon Quick Sight Embedding SDK — for integrating dashboards and natural language querying into your applications; Amazon Textract HumanLoopConfig documentation — for document analysis and signature detection; Amazon Quick Human-in-the-loop task center — for adding hu

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o comment on the style of music. 15:22 They finished by asking Marin to generate some images. 15:26 Here's Getting started with GPT-Live and the openai-live-console starter app on GitHub. 15:37 The last session of the day is the closing Q&A with Sam Altman and Thibault Sottiaux. Thibault is product lead on Codex, ChatGPT, ChatGPT Work, and the API - promoted a few months ago after successfully leading Codex. 15:38 That kicks off at 4pm, in about 20 minutes. 16:00 (As an aside, there are some exc

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o text: a model pretrained on millions of tables can read a labeled table as its context and predict the labels of new rows directly. Today, we are releasing NVIDIA Kumo Tabular ( GitHub , HuggingFace ), an open foundation model for tabular classification and regression. Given a table with labeled rows and the rows you want predictions for, Kumo Tabular returns class probabilities or numeric predictions in a single forward pass. How Kumo Tabular Works Kumo Tabular is a Transformer built around t

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computed from Table 1 . Meta-Harness leads on the Harvey LAB evolve split. RRSI has the smallest evolve gain but the only OOD average more than 1 point above H0. git clone https://github.com/google-research/rrsi.git && cd rrsi pip install -e ".[dev]" python3 rrsi.py --domain coding baseline python3 rrsi.py --domain coding run Each round drafts 2 candidates in separate git worktrees, screens them, evaluates both and fast-forwards the branch to the winner. The coding instance also needs Docker and

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netes基盤で利用できる。既存のエージェントを動かせるほか、エージェントや必要なツールを組み込んだ独自のイメージも使える。 インストールしてOpenCodeを動かす例 公式の初回起動ガイドでは、コーディングエージェントのOpenCodeと、モデルAPIサービスのOpenRouterを使う例を紹介している。ほかにも、Piを動かす例やClaude CodeからGitHubへの書き込みを許可する例が公開されている。 ここでは、OpenCodeの例に沿って、導入から応答の確認までを示す。OpenCodeはローカルのサンドボックス内で動かし、AIモデルはOpenRouter経由で利用する。 この例では実行基盤にDockerを使う。LinuxにDocker Engine 28.0以降を導入・起動した環境と、OpenRouterのアカウント・APIキーを用意する。以下のコマンドは、サンドボックスを動かすLinuxホストのBashで順に実行する。サンドボックスのコンテナはOpenShellが作成・起動する。 まず、OpenShellのインストールスクリプトを実行すると、CLIやポリシーの検証ツールが

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a session moves to another account, Claude rereads it and regenerates thinking. Roadmap: @mikeyk said Haiku 5.5 will “round out the family in the coming weeks.” Availability: It shipped day-one on the Claude Platform and Claude Code, along with a usage reset valid until Oct 22 ( @ClaudeDevs ). Third-party availability: GitHub Copilot in VS Code ( @code ) Arena Agent/Battle modes for WebDev, Text, Vision and Document ( @arena ) T3 Code, after @theo admitted it hadn’t been added to the catalog yet

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Sol Ultra 发布之际，人们正争相在 之前寻找 Fable 5 的极限，请特别关注 Claude Mods（尤其是 cheatsheet）： Boris Cherny @bcherny Claude Mods 正在陆续上线。已经有人构建了一个 Claude 内运行的俄罗斯方块 mod 🤯 查看 issue 了解最新的社区更新、技术细节和更多酷炫演示 github.com/anthropics/cla… 下午5:30 · 2026年9月14日 · 60.3万次查看 312条回复 · 173次转发 · 2.72万次点赞 总的来说，这也是 Thariq 另一条病毒式传播推文的反面： Thariq @trq212 奇怪的是，有一个“赚大钱”的按钮，却没人按 （把你的 SaaS 变成无头模式，让智能体使用它，按交互收费，尤其是对企业） 下午10:39 · 2026年8月18日 · 55.9万次查看 333条回复 · 229次转发 · 6570次点赞 云端大脑，本地之手 并且试试 Claude Projects： Boris Cherny @bcherny Projects 不仅改变了我与

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h benchmark, 100 English tasks, live Exa retrieval. Latency medians over 336–493 calls per model, each on its own provider's public API. Harness and per-call timing are public at: github.com/apoorvumang/retrieval-vs-recall Mercury 2 is the fastest model at every step of the pipeline, almost 2x faster than Gemini 3.1 Flash Lite on query planning, 4.7x faster than Claude Haiku 4.5, 10x faster than GPT-5 Mini. That per-step gap is where the end-to-end numbers in the next section come from: a pipeli

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of these. Mercury 2 optimizes for all three. We evaluated Mercury 2 on PinchBench, the open-source benchmark built on top of OpenClaw — the fastest-growing open-source project in GitHub history, with 250K+ stars in under 60 days. Here's what we found. The result Mercury 2 sits in the upper-left of the chart: high task success rate and the fastest execution time in its performance class. 78% success rate — matching or exceeding GPT-5 Mini (75%), Gemini 2.5 Flash (71%), DeepSeek Chat (72%), and G

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of your coding workflow Mar 30, 2026 Product We evaluated Mercury 2 on PinchBench, the open-source benchmark built on top of OpenClaw — the fastest-growing open-source project in GitHub history, with 250K+ stars in under 60 days. Mar 24, 2026 Product We evaluated Mercury 2 on PinchBench, the open-source benchmark built on top of OpenClaw — the fastest-growing open-source project in GitHub history, with 250K+ stars in under 60 days. Today, we're introducing Mercury 2 — the world's fastest reason

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Infrastructure Z.ai uses slime, one of the best open-source RL training frameworks, as the post-training infrastructure throughout all GLM-5 models. We refer readers to the slime GitHub repo to learn more about its design and features, and here we highlight two features. Performance characteristics of RL rollouts are highly task-dependent, and each task comes with different tools and reward functions. To ensure system efficiency, Z.ai designed a server-based multi-task rollout orchestrator. Eac

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through Claude Platform on AWS in North America. Give Claude Sonnet 5.5 a try in the Amazon Bedrock console, in Claude Platform on AWS, or explore the Getting Started notebooks on GitHub. About the authors Dani Mitchell Dani is a Senior Specialist Solutions Architect for Generative AI at AWS, working on go-to-market for Anthropic on Amazon Bedrock. He helps enterprises across the world design and deploy generative AI solutions using Anthropic’s models and capabilities on Amazon Bedrock to build

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个包含的类型提供可用配额。如果 SageMaker 选择了不同的实例，您的每小时成本也可能发生变化。使用 --instance-types 将池限制为满足您配额、价格和性能要求的类型。 开始之前，您需要： 本演练使用美国东部（弗吉尼亚北部）区域。示例会根据 AWS REGION 构建 DLC 镜像 URI 和运行时端点。 git clone https://github.com/aws-samples/sagemaker-genai-hosting-examples.git cd sagemaker-genai-hosting-examples/03-features/bidirectional-streaming-vLLM-Omni 安装所需的 Python 包。创建虚拟环境并安装示例定义的版本。 python3.12 -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt 配置示例。设置您的 SageMaker AI 执行角色。示例默认使用 us-east-1。传入 --re

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geMaker 容量感知实例池可以按优先级顺序列出兼容的实例类型。只有在验证每个候选类型满足图像模型的 GPU 内存和性能要求后，才添加实例池。本示例将异步视频端点保持在固定实例类型上。 开始之前，你需要： 本演练使用美国东部（弗吉尼亚北部）AWS 区域。在部署 GPU 端点之前，请查看 SageMaker AI 定价。 git clone https://github.com/aws-samples/sagemaker-genai-hosting-examples.git cd sagemaker-genai-hosting-examples/03-features/vllm-omni-image-video 安装 Python 依赖项。创建虚拟环境并安装示例要求。 python -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt 部署图像和视频端点。设置您的 SageMaker AI 执行角色并运行部署脚本。该脚本固定 omni-sagemaker-cuda-v1.6，创

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