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tools like gh , aws , gcloud , and sentry-cli when interacting with external services. CLI tools are the most context-efficient way to interact with external services. If you use GitHub, install the gh CLI. Claude knows how to use it for creating issues, opening pull requests, and reading comments. Without gh , Claude can still use the GitHub API, but unauthenticated requests often hit rate limits. Claude is also effective at learning CLI tools it doesn’t already know. Try prompts like Use 'foo

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n eval set for a support agent and score it with a script that calls OpenRouter. You measure how much the results move between runs with nothing changed, then wire the script into GitHub Actions as a required status check. Tl;dr A fixed eval set is a list of test cases committed to your repository. It changes only through a reviewed pull request; Filter at the job level rather than the workflow level. GitHub reports a job skipped by an if condition as a passing check, while a workflow skipped by

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ropics/claude-code-playground. SHARING YOUR MOD A mod is a Claude Code plugin, so you share it the same way as any other plugin, and there's nothing new to learn. Put the mod in a GitHub repo with a marketplace file and that repo becomes your marketplace. Anyone can install from it, and you can update it with a normal push. Installing takes three commands in Claude Code: The mod starts when you reload. If it doesn't show up, restart Claude Code. A mod is code that runs inside Claude Code on your

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ng been open and extensible, letting teams adapt it to their software delivery needs while allowing developers to use the forge of their choice. Connecting established forges like GitHub, GitLab, and Bitbucket is already well supported through plugins. Forges built for AI coding agents raise a new question: how your platform team integrates the first one will shape how it integrates the rest? The latest agentic forge, Cursor Origin was announced in June 2026 and opened early beta in August. Curs

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l follow Microsoft has administrative controls for SQL Copilot, including controls to disable Copilot, configure group policies, and set an execution context. The slides of the presentation can be found here. Trust No AI. References Slides: From SELECT to SYSADMIN. Hacking SQL Copilot in SSMS; Admin controls for GitHub Copilot in SQL Server Management Studio; SQL Copilot in SSMS: System Prompt; CVE-2026-65669: Microsoft SQL Server Elevation of Privilege Vulnerability Newer →; Contact me; ← Older

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models, where repeated computation across multiple HSTU layers would otherwise add significant latency. You can reproduce and extend this workflow from the NVIDIA/ recsys-examples GitHub repo. The HSTU overview introduces the GR model structure, including contextual tokens, item tokens, action tokens, embedding tables, HSTU blocks, and prediction heads. The AOTI inference guide walks through building the required images and libraries, preparing KuaiRand-1K data, training a checkpoint, exporting

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k to each other (Step 4). Finally, Step 5 shows how any one team can ship a new version of its agent without disturbing the others. The complete sample is in the AgentCore samples GitHub repository. Prerequisites Before you begin, make sure you have: An AWS account, with credentials for a principal that can create infrastructure. This sample creates AWS Identity and Access Management (IAM) roles, an S3 bucket, ECR repositories, and an AgentCore capacity provider and runtimes; AWS Command Line In

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— now generally available — helps users learn, research, code and iterate across the AI loop, analyzing runs, proposing experiments, recommending code changes and storing them in GitHub, and bringing back actionable evidence that analyzes experiment data, surfaces what drove a change and proposes the next experiments to run; CoreWeave Agent Lens — a new service — turns production agent observability into continuous improvement and understandable insights. It improves failure detection by 20% an

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. Filtering dispatches for pull requests merged to main requires action to be closed with a merge signal and base branch as main. The merge signal is indirect. PyTorch doesn’t use GitHub’s merge button that means PRs are closed, then PyTorchBot squash-merges and applies a “Merged” label. So detecting a landed PR requires checking for that label: Edge case: A race condition can cause the label to be applied after the dispatch, or a manual merge may skip the label entirely. Defensive handling (e.g

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RM learns the full reward distribution, preserving disagreement, uncertainty, and multiple plausible judgments. ✨ Paper:https://t.co/wS1fmHBeRV 🤗 Models: https://t.co/RGTU759A5y 💻 GitHub: https://t.co/KAow6DcoNn Why it matters: Human disagreement is structured, not just noise. On datasets with repeated annotations, judgments often form separated or polarized patterns. More importantly, as human disagreement increases, DRM’s learned reward distribution becomes increasingly multimodal. The distrib

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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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