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de Lite.OSWorld, and the mobile benchmarks still need a VM or an emulator: SFT & RL, any open agent SFT on CUA-Lite's corpora, then reinforce in its envs — GRPO and beyond, on the Slime trainer. Train any open agent on any data and any env: Lite.Sample, adapted to each model — pick a dataset and a student: Rollouts scored in the env drive GRPO updates — pick a model and env: Bring a dataset, an env, or an agent — each one compounds. Or just tell us what's missing — GitHub · Hugging Face · Email.

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orZero Autopilot with 5 independent seeds and evaluated the optimized variants it generated with 100 fresh rollouts on held-out tasks. You can find the code for this experiment on GitHub. For this experiment, we constrained Autopilot to a fixed set of models of comparable cost and disabled custom model training (e.g. fine-tuning): Here are the results: TensorZero Autopilot dramatically improved performance across every task. What actually happened under the hood? terminal-bench presents diverse

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o feel this feeling of being the bottleneck and being the breaking point as we have lots of agents contributing tons and tons of PRs,” Avilla said. “And we’re sitting there in our GitHub trying to approve PRs and make sure that everything works as expected.” From agent rosters to dynamic workflows The pair noted that dynamic workflows began in Claude Code as the “a harness for every task” concept. But flows can run longer than a single agent or a roster. The basic idea is that Claude chooses the

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liary models such as pose detection and face detection; 9/10/2024: Introducing MemoRAG, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1); 9/2/2024: Start to maintain the tutorials. The contents within will be actively updated and eariched, stay tuned! 📚; 7/26/2024: Release a new embedding model bge-en-icl, an embedding model that incorporates in-context learning capabilities, which,

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升至 Plus 档的 15 倍，而 200 美元的 Ultra 档则提升至 50 倍。 SpaceXAI 表示，Premium 档整合的权益此前若分别通过 SuperGrok、Cursor Pro 和 X Premium+ 购买，每月需花费 90 美元。 这里还值得注意，此前其 20 美元的 Pro 方案包含云代理和 Origin——该公司于 8 月推出的 GitHub 替代产品。 在 The X Subscription 下，这两项功能至少需要 30 美元的 Premium 档，这意味着从旧 Pro 方案切换的开发者每月需多付 10 美元才能保留这些功能。 对更广泛的开发者群体而言，这些不同额度的运作方式存在一个相当重要的区别。 订阅方案每周分配一定的 Grok 模型用量，客户可在 Cursor、Grok 应用和 X 上使用。 在符合条件的方案中，Grok Bot 也会消耗该额度。 因此，如果某人大量使用 Grok 处理其他活动，可能会发现可用于编码的包含用量减少。 不过，SpaceXAI 声称，新方案使用其自有模型时提供的编码用量至少是旧版同类 Cursor 订阅的两倍。 第三方模

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ted for The New Stack in May, these restrictions coincided with a surge of interest in OpenCode, which allows developers to switch between competing AI models. Today, the OpenCode GitHub repository has accumulated north of 212,000 stars and almost 30,000 forks. More broadly, there are signs of growing commercial interest in this category of coding agents. In July, Anaconda acquired Kilo , another such open-source coding agent that allows developers to choose their preferred AI provider. Interest

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工作流提供持久性，使其能够自适应并合理配置基础设施资源，并在无需重跑已完成工作的情况下从故障中恢复。 欲了解更多，请到 AI Engineer New York 第 48 号展位找我们。 “我想，我们当中许多如今在编排和管理大量智能体的人，可能会开始感受到一种成为瓶颈、成为崩溃点的感觉，因为大量智能体在贡献成堆成堆的 PR，”Avilla 说。“而我们坐在 GitHub 里，努力审批 PR，确保一切按预期运行。” 从智能体名单到动态工作流 两人指出，动态工作流最初在 Claude Code 中以“为每项任务配备一套 harness”的概念起步。但 flows 的运行时间可以超过单个智能体或一份名单。其基本理念是，由 Claude 来选择智能体的组织方式。 “Flows 本质上是多智能体编排的下一个版本，”Stall 说，“你可以让 Claude 完全编排它自己的整个多智能体拓扑结构。” 其运作方式是，主导智能体（lead agent）制定一个完成任务的计划。随后 Claude 编写一个编排程序，由它启动智能体、收集结果并管理信息流。 图片来自 Anthropic 此前，Claude M

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ie. Maia 300 is a successor to the 200, which was only announced in January . Maia 200 is already running on production AI workloads across Microsoft, including Microsoft Copilot, GitHub Copilot, Microsoft Foundry, and Microsoft’s own AI models. “Maia 200 is the best performing AI infrastructure in our fleet, measured in real world metrics for AI services,” says Guthrie. “Maia 300 is a complete AI system designed for the new types of AI workloads emerging today: Long-context reasoning, latency-s

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nfirmed 5,674 of the 6,123 findings they reviewed as valid, but only 516 vulnerabilities had been patched upstream. The company sent 6,157 findings to maintainers, and 584 CVE and GitHub Security Advisory identifiers were issued, though some findings received both. Roughly 23,000 candidates remain unreviewed. Claude is finding potential vulnerabilities faster than human researchers can verify them. How the fast track works Instead of making maintainers wait for its researchers to work through th

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スを参照のこと。 既存のポリシーに指示を追加する extra policyには、承認判断に追加したいルールだけを書く。基本のポリシーはそのまま使われるため、全文をコピーする必要はない。 次の例をconfig.tomlに記述し、山括弧内を実際の指示文に置き換える。 ポリシーを置き換える policyに指定した内容は、基本のポリシーを置き換える。標準のポリシーはGitHubで公開されているが、実際に適用される内容はモデルや組織の設定によって異なる場合がある。 公式ドキュメントは、適用中のポリシー全文を確認し、既存のルールを保って調整するよう案内している。全文を確認できない場合は上書きしない。 コラム⁠：次のメッセージを予測する「Composer predictions」 OpenAIは2026年10月9日、Codexの「Composer predictions」のベータ提供を案内した。現在の会話の内容やユーザーの話しかけ方を基に、次に送るメッセージ全体を予測し、入力欄に予測候補を表示する[7]。ベータ版では、入力途中の文章を逐次補完することはできない。 Now in beta: compo

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halfway through and at the end. (These charts are very similar to the ones you’d find in the open-source TensorZero UI!) You can also find the code to reproduce this experiment on GitHub. Halfway Through the Experiment ( 1,000 samples) At this point, the algorithm is still exploring all four variants, with sampling probabilities concentrated most heavily on the two best-performing variants, claude-sonnet-4-5-detailed-prompt (39.7%) and claude-sonnet-4-5-simple-prompt (38.4%). Note that the varia

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orZero Autopilot with 5 independent seeds and evaluated the optimized variants it generated with 100 fresh rollouts on held-out tasks. You can find the code for this experiment on GitHub. For this experiment, we constrained Autopilot to a fixed set of models of comparable cost and disabled custom model training (e.g. fine-tuning): Here are the results: TensorZero Autopilot dramatically improved performance across every task. What actually happened under the hood? terminal-bench presents diverse

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but we’ll keep refining the system based on maintainer feedback and as models improve. Getting started Core maintainers of eligible projects can enroll by submitting a PR to this GitHub repo following the standard project template, with additional guidance provided in our extended FAQ . Projects are eligible based on a similar set of criteria that OSS-Fuzz uses: briefly, projects should have a “critical impact on infrastructure and user security” and we will make decisions on a case-by-case bas

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he payment details. AgentCore payments handles the signing and returns proof the agent can present to access the service To explore the code, see the AgentCore payments samples on GitHub. Learn more Amazon Bedrock AgentCore payments is now generally available: Enabling agents to transact safely and autonomously at scale; Technical deep dive: AgentCore payments and innovation in agentic commerce; Enable safe agentic payments with built-in guardrails using AgentCore payments About BlockRun and Inc

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nts that breached Hugging Face . Kimi K3, the open model Goodfire built its first monitor around, took advantage of a leak in its sandbox to access the internet and information on GitHub this summer. Goodfire’s system works a bit like airport security. Small detectors called probes read the model’s internal signals at every step of an agent’s work, the way a walk-through scanner checks every passenger. Only when a probe flags something does a separate AI model, the equivalent of a hand search, t

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ber 7, 2026 Microsoftは、8月に発表したMAI-Code-1.1-Flashのローカル版を今回用意した。モデルの重みを少ないビット数で表す3ビット量子化などにより、同モデルのクラウド版と比べて容量を約80%削減したという。 処理の振り分けを担うHydraFusionは、性能、費用、応答時間を考慮して、使うAIモデルとその組み合わせ方を選ぶGitHub Copilotの機能。1つのモデルでタスクを解くほか、必要なら高性能なモデルに引き継いだり、別のモデルに回答案をレビューさせてから修正したりする。 HydraFusionは、クラウド上のモデルを対象にリサーチプレビューで提供されている。今回の拡張では、PC上のローカルモデルも自動選択の対象に加え、タスクに応じてローカルとクラウドを使い分ける。この拡張の実験的プレビューは、10月後半にGitHub Copilotアプリ、GitHub Copilot CLI、Visual Studio Codeで提供する予定。 同日にWindows 11での一般提供が発表されたMicrosoft Execution Containers（

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working with Jev and users managing their own decision model. Unsloth’s security-audit workflow uses read-only repository permissions and non-persisted checkout credentials. Every GitHub Action is pinned to a full commit hash, and outbound-network allowlists block unexpected egress. CodeQL covers Python, JavaScript/TypeScript, Rust and GitHub Actions. Unsloth says it runs Codex Security and repeated Codex reviews during development to catch security issues and bugs. Library Access and Changes Un

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gravity, Gemini CLI, Claude Code, or Cursor), install the agents-cli-aqua skill (skills/agents-cli-aqua/SKILL.md) alongside the inner-loop evaluation skill (npx skills add https://github.com/google/agents-cli --skill google-agents-cli-eval). We are sharing AQuA in the open to collaborate with teams running agents in production and shape where this goes together. As you try it on your stack, we'd love to hear what you think: Does a side-by-side quality agent fit your architecture? Which parts of

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orward paths: Community feedback and Contributions ML Drift is fully open source under the Apache 2.0 license. We welcome contributions, RFCs, and community feedback: Source Code: github.com/google-ai-edge/ml-drift; Issue Tracker & Feature Requests: File an Issue on GitHub; Discussions & Q&A: Google AI Edge Community Forum; Contributing: Review our Contributing Guide to add new UCL operators or hardware-specific optimizations Acknowledgments: Bring multimodal semantic search to the edge with Emb

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nt. By default, the agent gets tools for every provider connected to the project; you can limit which providers and tools it uses. This example limits the agent to Notion, Linear, GitHub, and Slack, and removes delete, archive, and merge tools: Tool entries accept globs, and new connections show up in the running app without a redeploy. Channels Connections can also put your agents in Slack, Discord, or Telegram. Pass channels() to the Mastra constructor and the platform supplies each channel's

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