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Meta 的 AI 数学论文开始标注模型参与
Meta 公布 6 篇与 Muse Spark 协作的数学论文。更值得关注的是贡献来源、第二组复核、并行结果和形式化验证怎样进入公开论文。
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Google 让 Gboard 的联邦学习隐私可以被外部审计
Google 把 TEE、访问策略、远程证明、透明日志和可复现构建接成联邦学习的外部验证链,并已用于 Gboard 下一词预测。
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Agents API browser auth keeps passwords out of model input
How Agents API separates site approval, login credentials, and screenshots, and why network access and site approval cannot replace action-level confirmation.
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OpenAI 给 Agents API 加了浏览器,密码不走模型上下文
拆解 Agents API 的网站访问审批、独立登录通道与截图输出边界:为什么能联网不等于已授权,站点批准也不能代替高影响动作确认。
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Cloudflare redesigns its CLI for agents
Cloudflare launches cf in open beta, using Wrangler usage data to explain default JSON, command search, and typed configuration.
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Cloudflare 把 CLI 重新做给 Agent
Cloudflare 发布 cf open beta,用 Wrangler 的实际使用数据解释默认 JSON、命令搜索和类型化配置。
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Cloudflare data puts agent traffic on the website budget
Cloudflare and Fastly network data reveal repeated crawling, origin costs, access-policy questions, and the returns websites get from machine traffic.
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Microsoft connects AI-built tools to app delivery
After Copilot Code builds an internal tool, Managed Runtime handles Git, dev environments, Entra ID, connectors, previews, deployment, and governance.
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Microsoft 把 AI 内部工具的发布链也接上了
Copilot Code 生成内部工具后,Managed Runtime 接管 Git、受控开发环境、Entra ID、连接器、预览、部署和企业治理。
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GitHub Agentic Autofix starts retaining repair experience
GitHub Agentic Autofix reads and writes Copilot Memory. How security repair knowledge crosses agent workflows, and why verification, scope, and expiry matter.
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GitHub Agentic Autofix 开始积累修复记忆
GitHub Agentic Autofix 会读取并写入 Copilot Memory。本文拆解安全修复经验如何跨 Agent 复用,以及验证、范围和过期为什么重要。
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Agent markets: 85% of the loss comes before negotiation
Anthropic's book market experiment locates most efficiency loss in preference representation. A look at model choice, preference evals, and market rules.
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Anthropic Agent 市场:85% 的效率损失发生在谈判前
Anthropic 的 Agent 市场实验显示,主要效率损失发生在偏好表示和匹配阶段。本文拆解模型选择、偏好 Eval 与市场规则。
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Claude takes a candidate to the lab, not a new CRISPR
About 950 Claude agents helped identify ART. Human wet-lab experiments confirmed short RNA expression, but its main biological function remains unknown.
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Claude 把候选推进湿实验,但这不是新 CRISPR
Anthropic 用约 950 个 Claude Agent 从大规模逆转录酶候选中筛出 ART,并由人类湿实验验证短 RNA 表达,但 ART 的主要功能仍未知。
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GPT-6 prompt caching becomes an agent state layer
GPT-6 adds cache breakpoints, diagnostics, prewarming, and mutable configuration. Long-running agents need stable shared prefixes and cache-hit monitoring.
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GPT-6 Prompt Cache 变成了 Agent 的状态层
GPT-6 增加 cache breakpoint、诊断、预热和可变配置。长跑 Agent 需要主动保持共享前缀稳定,并监控缓存命中率。
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Deploying agent backends with Python Workers GA
Python Workers GA supports web frameworks and agent libraries. WebAssembly package compatibility remains the main migration boundary.
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Cloudflare Python Workers GA 后,Agent 后端怎么部署
Python Workers 已进入 GA,FastAPI、Django、Flask 以及 openai、langchain、mcp 可以直接运行在 Workers。真正的迁移边界仍是 WebAssembly 包兼容性。
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Qwen-Image 2.1: native RGBA and commercial licensing
Qwen-Image 2.1 offers native RGBA, unified generation and editing, up to 10 reference images, and 2K output. Commercial use needs a separate license.
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Qwen-Image 2.1 原生透明图很适合做资产,但商用要另取许可
Qwen-Image 2.1 支持原生 RGBA、统一生成编辑、最多 10 张参考图和 2K 输出,但仓库许可证默认只授权非商业研究与评测。
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How to score a coding agent that finishes 80% but fails
Android Bench 2.0 keeps pass rate as a hard gate and adds completion rate to diagnose progress and unfinished acceptance requirements.
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Coding Agent 做完 80% 还失败,Eval 怎么记
Android Bench 2.0 保留 Pass rate 作为硬门禁,再用 Completion rate 解释长任务已经完成多少,以及问题卡在哪一类验收。
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FDE growth: 519 overseas jobs, 28 in China, 6 projects
A study of 519 overseas job listings, 28 roles at major Chinese firms, and 6 projects examines FDE responsibilities, growth, costs, and scalability.
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FDE 为什么突然火了:519 条海外 JD、28 条国内大厂岗位和 6 个真实项目
用 519 条海外公开 JD、28 条国内大厂岗位和 6 个真实项目,拆解 FDE 的职责、增长、国内落地、成本与规模化条件。
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Jev gives agents typed decisions instead of generated text
TypeSafe Jev uses Choice, Score, and Noul for constrained agent decisions while code retains control of valid actions and side effects.
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Agent 里不是每个判断都需要生成文本:Jev 把决策交给代码
TypeSafe Jev 用 Choice、Score 和 Noul 处理 Agent 的受约束决策,让模型负责模糊判断,代码继续掌握合法动作和副作用。
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Anthropic opens internal processes to embedded evaluators
Anthropic and Accenture extend evaluation beyond model outputs to training, deployment, monitoring, and incident processes, with independence unresolved.
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Anthropic 让第三方 Eval 进入内部,模型评测开始看过程
Anthropic 与 Accenture 的 embedded evaluator 计划把评测范围从模型输出扩展到训练、部署、监控和事故流程,也暴露出独立性边界。
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skill-up: evaluate real stateful multi-turn sessions
Alibaba skill-up resumes real sessions turn by turn, letting skill evaluations test context continuity, intermediate state, and per-turn assertions.
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多轮 Skill Eval 不该把对话压成一包:skill-up 开始续接真实 Session
Alibaba skill-up 的 stateful 多轮模式逐轮恢复真实 Session,让 Agent Skill Eval 能检查上下文连续性、中间状态和逐轮断言。
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Claude merges Chat and Cowork to reduce mode choices
Claude combines Chat and Cowork in one conversation, routes capabilities automatically, and keeps task state and permissions visible.
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Claude 把 Chat 和 Cowork 合并后,Agent 产品开始减少模式选择
Claude 把 Chat 与 Cowork 合并到同一会话,把能力路由交给系统,同时保留任务状态和权限确认,给 Agent 产品一个具体 UX 边界。
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Gemini Live: turnComplete no longer means the task is done
Gemini Live Extended Thinking separates conversation turns from background task lifecycles. Clients need interaction_status to track actual completion.
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Gemini Live 把推理放到后台:turnComplete 不再等于结束
Gemini Live Extended Thinking 把对话回合与后台任务生命周期拆开,客户端需要用 interaction_status 管理真正的完成状态。
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AWS agents propose; deterministic code validates
How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.
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A reliable agent might leave the code alone today
GitHub Agentic Workflows jsweep checks whether a change is worthwhile and treats noop as valid, instead of forcing automation to produce a diff.
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一个靠谱的 Agent,今天可能不改代码
从 GitHub Agentic Workflows 的 jsweep 看 Agent 如何先判断有没有值得修改的目标,把 noop 作为正式结果,而不是为了自动化强行制造改动。
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Herdr turns the terminal into an agent runtime
How Herdr manages existing coding-agent terminals with persistent sessions, blocked states, cross-machine control, and agent coordination.
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Herdr 不替换 Agent,它把终端做成运行时
拆解 Herdr 如何接管现有 Coding Agent 所在终端,提供持久会话、阻塞状态、跨机器控制和 Agent 协作,而不重写底层 Agent。
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Google packages Skills and MCP in one plugin
How the Google Cloud Developer Plugin packages Skills, MCP configuration, and rules, and what this means for distribution, versions, and permissions.
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Google 把 Skill 和 MCP 打成一个插件
拆解 Google Cloud Developer Plugin 如何把 Skill、MCP 配置和规则按 Agent Plugins 规范打包,暴露跨 Coding Agent 的分发、版本与权限问题。
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Agentic search missed an incident. Test the eval too
The Anthropic incident review shows why automated monitors and LLM graders need coverage, independence, and tests for their own failure modes.
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Agentic Search 漏掉第四起事故,Eval 也要被测试
从 Anthropic Agentic Search 事故复盘看自动监控和 LLM grader 的漏检问题,说明 Agent Eval 也需要覆盖率、独立性和失效模式测试。
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OpenAI agent workdays and shifting builder bottlenecks
OpenAI organization-level data suggests that more coding-agent use shifts bottlenecks to task decomposition, concurrency, harnesses, evaluation, and selection.
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OpenAI 研究组织跑到 3.1 Agent 工作日后,Builder 的瓶颈开始转移
OpenAI 的组织级数据说明 Coding Agent 使用规模上升后,瓶颈会转向任务拆分、并发调度、Harness、Eval 和结果选择。
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Agent demos are easy. The harness is the hard part
Strands harness-sdk, Harness MCP Server, and HydraFusion show why agent engineering needs control over context, permissions, state, validation, and recovery.
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Agent Demo 不难,难的是 Harness
从 Strands harness-sdk、Harness MCP Server 和 HydraFusion 看 Agent 工程为什么从模型调用转向上下文、权限、状态、验证与恢复控制。
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Coding agent security is now a supply-chain problem
Coding agents read and execute skills, MCP tools, dependencies, and repository instructions. These need software supply-chain controls.
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AI Coding Agent 的安全问题已经进了供应链
说明 Coding Agent 会读取并执行 Skill、MCP、依赖和仓库指令,因此这些组件应按软件供应链治理,而不只是做 Prompt 安全。
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Vercel Eve Agent Framework
A source-backed review of Vercel Eve’s filesystem-first agent model, durable execution, tools, sandboxes, approvals, deployment, and Vercel platform tradeoffs.
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