{"id":388,"date":"2026-05-09T07:24:01","date_gmt":"2026-05-08T23:24:01","guid":{"rendered":"http:\/\/www.faiyi.com\/?p=388"},"modified":"2026-05-09T07:24:01","modified_gmt":"2026-05-08T23:24:01","slug":"ai%e5%8a%a8%e6%80%81%e6%af%8f%e6%97%a5%e7%ae%80%e6%8a%a5-2026-05-09","status":"publish","type":"post","link":"http:\/\/www.faiyi.com\/?p=388","title":{"rendered":"AI\u52a8\u6001\u6bcf\u65e5\u7b80\u62a5 2026-05-09"},"content":{"rendered":"

\u65e5\u671f\uff1a2026-05-09<\/p>\n

\u672c\u671f\u805a\u7126\uff1a\u91cd\u70b9\u5173\u6ce8\u6a21\u578b\u53d1\u5e03\u4e0e release notes\u3001\u5b98\u65b9 engineering blog\u3001AI coding \/ agent \/ SRE\u3001\u8bc4\u6d4b\u699c\u5355\u53d8\u5316\u3001\u5f00\u53d1\u8005\u5b9e\u8df5\u535a\u5ba2\u3001\u6846\u67b6\u751f\u6001\u3001\u5f00\u6e90\u6a21\u578b\u4e0e\u771f\u5b9e\u7528\u6237\u89c6\u89d2\uff1b\u5f53 HN\u3001Reddit\u3001Hugging Face \u7b49\u793e\u533a\u6e90\u53ef\u8bbf\u95ee\u65f6\u4f18\u5148\u7eb3\u5165\u3002<\/p>\n

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  1. \n

    Artificial Analysis \u6700\u65b0\u6a21\u578b\u6392\u540d\u89c2\u5bdf<\/strong>\uff08Artificial Analysis\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Artificial Analysis \u66f4\u65b0\u4e86\u5176 AI \u6a21\u578b\u7efc\u5408\u6392\u540d\uff0c\u76ee\u524d GPT-5.5 (xhigh) \u4ee5 60 \u5206\u4f4d\u5c45 Intelligence Index \u699c\u9996\uff0c\u7d27\u968f\u5176\u540e\u7684\u662f GPT-5.5 (high) \u548c Claude Opus 4.7 (Max)\u3002\u5728\u901f\u5ea6\u65b9\u9762\uff0cMercury 2 \u4ee5\u6bcf\u79d2 689 \u4e2a token \u9065\u9065\u9886\u5148\uff1b\u4ef7\u683c\u6700\u4f4e\u7684\u662f Qwen3.5 0.8B\uff0c\u6bcf\u767e\u4e07 token \u4ec5\u9700 0.02 \u7f8e\u5143\u3002\u4e0a\u4e0b\u6587\u7a97\u53e3\u6700\u5927\u7684\u6a21\u578b\u662f Llama 4 Scout\uff0c\u652f\u6301 1000 \u4e07 token\u3002\u8be5\u6307\u6570\u57fa\u4e8e 10 \u9879\u72ec\u7acb\u8bc4\u4f30\uff0c\u5305\u62ec GDPval-AA\u3001Terminal-Bench Hard\u3001Humanity's Last Exam \u7b49\uff0c\u8986\u76d6 376 \u4e2a\u6a21\u578b\uff0c\u5176\u4e2d 241 \u4e2a\u4e3a\u5f00\u6e90\u6743\u91cd\u6a21\u578b\u3002<\/p>\n

    English Summary:<\/strong> Artificial Analysis updated its comprehensive AI model rankings, with GPT-5.5 (xhigh) leading the Intelligence Index at 60 points, followed by GPT-5.5 (high) and Claude Opus 4.7 (Max). Mercury 2 tops speed at 689 tokens\/s, while Qwen3.5 0.8B is the cheapest at $0.02 per million tokens. Llama 4 Scout offers the largest context window at 10 million tokens. The index covers 376 models (241 open weights) across 10 independent evaluations including GDPval-AA, Terminal-Bench Hard, and Humanity's Last Exam.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  2. \n

    Introducing Claude Opus 4.7<\/strong>\uff08Anthropic News\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Anthropic \u6b63\u5f0f\u53d1\u5e03 Claude Opus 4.7\uff0c\u5728\u9ad8\u7ea7\u8f6f\u4ef6\u5de5\u7a0b\u4efb\u52a1\u4e0a\u76f8\u6bd4 Opus 4.6 \u6709\u663e\u8457\u63d0\u5347\uff0c\u5c24\u5176\u5728\u5904\u7406\u6700\u56f0\u96be\u7684\u7f16\u7801\u4efb\u52a1\u65f6\u8868\u73b0\u66f4\u4f18\u3002\u8be5\u6a21\u578b\u5177\u5907\u66f4\u9ad8\u5206\u8fa8\u7387\u7684\u89c6\u89c9\u80fd\u529b\uff08\u652f\u6301\u957f\u8fbe 2576 \u50cf\u7d20\u7684\u56fe\u50cf\uff09\uff0c\u5728\u4e13\u4e1a\u4efb\u52a1\u4e2d\u5c55\u73b0\u51fa\u66f4\u4f73\u7684\u5ba1\u7f8e\u4e0e\u521b\u9020\u529b\u3002\u65b0\u589e xhigh \u52aa\u529b\u7ea7\u522b\uff0cAPI \u5b9a\u4ef7\u7ef4\u6301\u4e0d\u53d8\uff08\u8f93\u5165 5 \u7f8e\u5143\/\u767e\u4e07 token\uff0c\u8f93\u51fa 25 \u7f8e\u5143\/\u767e\u4e07 token\uff09\u3002\u4e3a\u4fdd\u969c\u7f51\u7edc\u5b89\u5168\uff0cAnthropic \u5f15\u5165\u4e86\u5b9e\u65f6\u7f51\u7edc\u9632\u62a4\u673a\u5236\uff0c\u5e76\u63a8\u51fa Cyber Verification Program \u4f9b\u5b89\u5168\u4e13\u4e1a\u4eba\u5458\u7533\u8bf7\u5408\u6cd5\u4f7f\u7528\u3002Cursor\u3001Replit\u3001Vercel \u7b49 20 \u4f59\u5bb6\u5408\u4f5c\u4f19\u4f34\u7684\u65e9\u671f\u6d4b\u8bd5\u53cd\u9988\u663e\u793a\uff0cOpus 4.7 \u5728\u4ee3\u7801\u8d28\u91cf\u3001\u5de5\u5177\u8c03\u7528\u51c6\u786e\u6027\u548c\u957f\u7a0b\u4efb\u52a1\u81ea\u4e3b\u6027\u65b9\u9762\u5747\u6709\u660e\u663e\u6539\u5584\u3002<\/p>\n

    English Summary:<\/strong> Anthropic released Claude Opus 4.7, showing notable improvements over Opus 4.6 in advanced software engineering, particularly on the most difficult coding tasks. The model features enhanced vision capabilities (up to 2,576 pixels on the long edge), better aesthetic taste, and creativity for professional work. It introduces a new xhigh effort level between high and max, with unchanged API pricing ($5\/M input, $25\/M output tokens). Real-time cyber safeguards are implemented, alongside a Cyber Verification Program for security professionals. Early testers including Cursor, Replit, and Vercel reported significant gains in code quality, tool accuracy, and long-horizon autonomy.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  3. \n

    Featured An update on recent Claude Code quality reports<\/strong>\uff08Anthropic Engineering\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Anthropic \u5de5\u7a0b\u56e2\u961f\u53d1\u5e03\u590d\u76d8\u62a5\u544a\uff0c\u89e3\u91ca\u4e86\u8fc7\u53bb\u4e00\u4e2a\u6708 Claude Code \u8d28\u91cf\u4e0b\u964d\u7684\u539f\u56e0\uff0c\u5e76\u786e\u8ba4\u4e09\u9879\u72ec\u7acb\u95ee\u9898\u5df2\u4fee\u590d\u3002\u7b2c\u4e00\uff0c3 \u6708 4 \u65e5\u5c06\u9ed8\u8ba4\u63a8\u7406\u52aa\u529b\u7ea7\u522b\u4ece high \u6539\u4e3a medium \u5bfc\u81f4\u667a\u80fd\u4e0b\u964d\uff0c\u5df2\u4e8e 4 \u6708 7 \u65e5\u56de\u6eda\uff0cOpus 4.7 \u9ed8\u8ba4\u8bbe\u4e3a xhigh\u3002\u7b2c\u4e8c\uff0c3 \u6708 26 \u65e5\u90e8\u7f72\u7684\u7f13\u5b58\u4f18\u5316\u5b58\u5728 bug\uff0c\u5bfc\u81f4\u4f1a\u8bdd\u95f2\u7f6e\u8d85\u8fc7\u4e00\u5c0f\u65f6\u540e\u4f1a\u6301\u7eed\u6e05\u9664\u5386\u53f2\u63a8\u7406\u8bb0\u5f55\uff0c\u4f7f Claude \u663e\u5f97\u5065\u5fd8\u548c\u91cd\u590d\uff0c\u5df2\u4e8e 4 \u6708 10 \u65e5\u4fee\u590d\u3002\u7b2c\u4e09\uff0c4 \u6708 16 \u65e5\u6dfb\u52a0\u7684\u51cf\u5c11\u5197\u957f\u8f93\u51fa\u7684\u7cfb\u7edf\u63d0\u793a\u610f\u5916\u635f\u5bb3\u4e86\u7f16\u7801\u8d28\u91cf\uff0c\u5df2\u4e8e 4 \u6708 20 \u65e5\u64a4\u9500\u3002Anthropic \u5411\u6240\u6709\u8ba2\u9605\u8005\u91cd\u7f6e\u4f7f\u7528\u989d\u5ea6\uff0c\u5e76\u627f\u8bfa\u6539\u8fdb\u5185\u90e8\u6d4b\u8bd5\u6d41\u7a0b\uff0c\u5305\u62ec\u8ba9\u66f4\u591a\u5458\u5de5\u4f7f\u7528\u516c\u5f00\u53d1\u5e03\u7248\u672c\u3001\u6269\u5c55 Code Review \u5de5\u5177\u652f\u6301\u66f4\u591a\u4ed3\u5e93\u4f5c\u4e3a\u4e0a\u4e0b\u6587\u3002<\/p>\n

    English Summary:<\/strong> Anthropic's engineering team published a postmortem explaining recent Claude Code quality issues traced to three separate changes, all now resolved as of April 20. First, a March 4 change lowering default reasoning effort from high to medium reduced intelligence, reverted on April 7 with Opus 4.7 now defaulting to xhigh. Second, a March 26 caching optimization bug continuously dropped prior reasoning after idle sessions, making Claude appear forgetful; fixed April 10. Third, an April 16 system prompt to reduce verbosity inadvertently hurt coding quality, reverted April 20. Usage limits are reset for all subscribers, and Anthropic committed to improved testing including broader internal use of public builds and enhanced Code Review tooling.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  4. \n

    Scaling Managed Agents: Decoupling the brain from the hands<\/strong>\uff08Anthropic Engineering\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Anthropic \u5de5\u7a0b\u535a\u5ba2\u53d1\u5e03 Managed Agents \u67b6\u6784\u8bbe\u8ba1\u6587\u7ae0\uff0c\u9610\u8ff0\u5982\u4f55\u901a\u8fc7\u89e3\u8026"\u5927\u8111"\uff08Claude \u53ca\u5176 harness\uff09\u3001"\u4f1a\u8bdd"\uff08\u4e8b\u4ef6\u65e5\u5fd7\uff09\u548c"\u53cc\u624b"\uff08\u6c99\u7bb1\u6267\u884c\u73af\u5883\uff09\u6765\u6784\u5efa\u53ef\u6269\u5c55\u7684\u957f\u65f6\u7a0b Agent \u6258\u7ba1\u670d\u52a1\u3002\u501f\u9274\u64cd\u4f5c\u7cfb\u7edf\u865a\u62df\u5316\u786c\u4ef6\u7684\u62bd\u8c61\u601d\u60f3\uff0cManaged Agents \u5c06\u5404\u7ec4\u4ef6\u63a5\u53e3\u5316\uff0c\u4f7f\u5b9e\u73b0\u53ef\u4ee5\u72ec\u7acb\u6f14\u8fdb\u548c\u66ff\u6362\u3002\u89e3\u8026\u540e\uff0c\u5bb9\u5668\u6210\u4e3a\u53ef\u66ff\u6362\u7684" cattle "\u800c\u975e\u9700\u8981\u7ef4\u62a4\u7684" pet "\uff0c harness \u5d29\u6e83\u540e\u53ef\u901a\u8fc7\u4f1a\u8bdd\u65e5\u5fd7\u6062\u590d\uff0c\u5b89\u5168\u51ed\u8bc1\u4e0e\u6c99\u7bb1\u9694\u79bb\u3002\u6b64\u67b6\u6784\u4f7f p50 \u9996 token \u5ef6\u8fdf\u964d\u4f4e\u7ea6 60%\uff0cp95 \u964d\u4f4e\u8d85 90%\uff0c\u5e76\u652f\u6301\u4e00\u4e2a\u5927\u8111\u8fde\u63a5\u591a\u4e2a\u6267\u884c\u73af\u5883\uff08VPC\u3001MCP \u5de5\u5177\u7b49\uff09\u3002<\/p>\n

    English Summary:<\/strong> Anthropic's engineering blog published an article on Managed Agents architecture, explaining how decoupling the "brain" (Claude and its harness), "session" (event log), and "hands" (sandbox execution environment) enables scalable long-horizon agent hosting. Drawing from OS virtualization principles, Managed Agents interface-izes components so implementations can evolve independently. Decoupling turns containers into replaceable "cattle" rather than maintained "pets," allows harness recovery via session logs, and isolates credentials from sandboxes. This architecture reduced p50 time-to-first-token latency by ~60% and p95 by over 90%, while enabling one brain to connect to multiple execution environments (VPCs, MCP tools, etc.).<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  5. \n

    Laid-off Oracle workers tried to negotiate better severance. Oracle said no.<\/strong>\uff08TechCrunch AI\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Oracle \u4e8e 3 \u6708 31 \u65e5\u901a\u8fc7\u90ae\u4ef6\u88c1\u5458\u7ea6 2 \u81f3 3 \u4e07\u4eba\uff0c\u5f15\u53d1\u88ab\u88c1\u5458\u5de5\u5bf9\u5f85\u9047\u7684\u4e89\u8bae\u3002\u516c\u53f8\u63d0\u4f9b\u7684\u9063\u6563\u8d39\u4e3a\u6bcf\u670d\u52a1\u4e00\u5e74\u989d\u5916\u4e00\u5468\u5de5\u8d44\uff08\u4e0a\u9650 26 \u5468\uff09\u52a0\u4e00\u4e2a\u6708 COBRA \u4fdd\u9669\uff0c\u4f46\u672a\u52a0\u901f\u5373\u5c06\u5f52\u5c5e\u7684 RSU \u80a1\u7968\uff0c\u5bfc\u81f4\u90e8\u5206\u5458\u5de5\u635f\u5931\u6570\u5341\u4e07\u7f8e\u5143\u3002\u4e00\u4e9b\u5458\u5de5\u56e0\u88ab\u5f52\u7c7b\u4e3a\u8fdc\u7a0b\u5de5\u4f5c\u8005\u800c\u65e0\u6cd5\u4eab\u53d7 WARN \u6cd5\u6848\u8981\u6c42\u7684\u4e24\u4e2a\u6708\u63d0\u524d\u901a\u77e5\u4fdd\u62a4\u3002\u81f3\u5c11 90 \u540d\u5458\u5de5\u8054\u540d\u8bf7\u613f\u8981\u6c42 Oracle \u53c2\u7167 Meta\u3001Microsoft\u3001Cloudflare \u7b49\u516c\u53f8\u7684\u66f4\u4f18\u539a\u9063\u6563\u65b9\u6848\u8fdb\u884c\u8c08\u5224\uff0c\u4f46\u516c\u53f8\u62d2\u7edd\u534f\u5546\u3002\u6b64\u4e8b\u4ef6\u51f8\u663e\u79d1\u6280\u884c\u4e1a\u5458\u5de5\u5728\u5e02\u573a\u8f6c\u5411\u65f6\u7f3a\u4e4f\u8db3\u591f\u52b3\u52a8\u4fdd\u62a4\u3002<\/p>\n

    English Summary:<\/strong> Oracle laid off an estimated 20,000 to 30,000 employees via email on March 31, sparking disputes over severance terms. The company offered four weeks of pay plus one week per year of service (capped at 26 weeks) and one month of COBRA insurance, but did not accelerate soon-to-vest RSUs, causing some employees to lose hundreds of thousands in stock value. Some workers were classified as remote, disqualifying them from WARN Act protections requiring two months' notice. At least 90 employees signed a petition urging Oracle to match more generous severance packages from Meta, Microsoft, and Cloudflare, but the company declined to negotiate, highlighting the lack of worker protections in tech when market conditions shift.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  6. \n

    How GitHub Is Securing Agentic Workflows in Modern CI CD Systems<\/strong>\uff08InfoQ AI\/ML\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>GitHub \u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5176\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41\u7684\u5b89\u5168\u67b6\u6784\uff0c\u91c7\u7528\u7eb5\u6df1\u9632\u5fa1\u7b56\u7565\u5c06\u81ea\u4e3b AI \u667a\u80fd\u4f53\u5b89\u5168\u96c6\u6210\u5230 CI\/CD \u6d41\u6c34\u7ebf\u4e2d\u3002\u8be5\u8bbe\u8ba1\u5f3a\u8c03\u9694\u79bb\u3001\u53d7\u9650\u6267\u884c\u548c\u53ef\u5ba1\u8ba1\u6027\uff0c\u4ee5\u7f13\u89e3 AI \u9a71\u52a8\u81ea\u52a8\u5316\u5e26\u6765\u7684\u98ce\u9669\u3002\u667a\u80fd\u4f53\u5728\u6c99\u76d2\u5316\u3001\u77ed\u6682\u7684\u73af\u5883\u4e2d\u8fd0\u884c\uff0c\u6743\u9650\u4e25\u683c\u53d7\u9650\uff0c\u9ed8\u8ba4\u53ea\u8bfb\u6a21\u5f0f\uff0c\u4efb\u4f55\u5199\u5165\u64cd\u4f5c\u5fc5\u987b\u901a\u8fc7\u53d7\u63a7\u7684\u5b89\u5168\u8f93\u51fa\uff08\u5982\u62c9\u53d6\u8bf7\u6c42\u6216\u95ee\u9898\u8bc4\u8bba\uff09\u8fdb\u884c\uff0c\u786e\u4fdd\u6240\u6709\u53d8\u66f4\u900f\u660e\u3001\u53ef\u5ba1\u67e5\u5e76\u7ecf\u8fc7\u6279\u51c6\u540e\u624d\u80fd\u5e94\u7528\u3002<\/p>\n

    English Summary:<\/strong> GitHub detailed a defense-in-depth security architecture for agentic workflows in CI\/CD pipelines, emphasizing isolation, constrained execution, and auditability. Agents run in sandboxed, ephemeral environments with tightly restricted permissions, operating in read-only mode by default. Any write operation must pass through controlled safe outputs like pull requests or issue comments, ensuring all changes remain transparent, reviewable, and subject to approval before being applied.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  7. \n

    Halliburton enhances seismic workflow creation with Amazon Bedrock and Generative AI<\/strong>\uff08AWS ML Blog\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Halliburton \u4e0e AWS \u5408\u4f5c\uff0c\u5229\u7528 Amazon Bedrock \u548c\u751f\u6210\u5f0f AI \u6784\u5efa\u4e86\u4e00\u4e2a\u6982\u5ff5\u9a8c\u8bc1\u7cfb\u7edf\uff0c\u5c06\u81ea\u7136\u8bed\u8a00\u67e5\u8be2\u8f6c\u6362\u4e3a\u53ef\u6267\u884c\u7684\u5730\u9707\u5de5\u4f5c\u6d41\uff0c\u5e76\u4e3a\u5176\u5730\u9707\u5f15\u64ce\u5de5\u5177\u548c\u6587\u6863\u63d0\u4f9b\u95ee\u7b54\u80fd\u529b\u3002\u8be5\u7cfb\u7edf\u901a\u8fc7\u591a\u6b65\u9aa4\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41\u5904\u7406\u590d\u6742\u7684\u5730\u9707\u6570\u636e\u5904\u7406\u4efb\u52a1\uff0c\u80fd\u591f\u7406\u89e3\u548c\u914d\u7f6e\u4e13\u4e1a\u5de5\u5177\u3002\u8fd9\u79cd\u65b9\u6cd5\u53ef\u63a8\u5e7f\u5230\u5176\u4ed6\u9700\u8981\u4e13\u4e1a\u5de5\u5177\u77e5\u8bc6\u548c\u914d\u7f6e\u7684\u591a\u6b65\u9aa4\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41\u9886\u57df\uff0c\u672a\u6765\u53ef\u63a2\u7d22\u4f7f\u7528 Strands Agents SDK \u4e0e Amazon Bedrock AgentCore \u6784\u5efa\u591a\u667a\u80fd\u4f53\u67b6\u6784\u4ee5\u63d0\u9ad8\u51c6\u786e\u6027\u3002<\/p>\n

    English Summary:<\/strong> Halliburton built a proof-of-concept using Amazon Bedrock and generative AI to convert natural language queries into executable seismic workflows while providing Q&A capabilities for Halliburton's Seismic Engine tools and documentation. The system handles complex seismic data processing through multi-step agentic workflows requiring specialized tool knowledge and configuration.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  8. \n

    Running Codex safely at OpenAI<\/strong>\uff08OpenAI News\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>OpenAI \u5206\u4eab\u4e86\u5176\u5185\u90e8\u5982\u4f55\u5b89\u5168\u8fd0\u884c Codex \u7f16\u7801\u667a\u80fd\u4f53\u7684\u5b9e\u8df5\u7ecf\u9a8c\uff0c\u5305\u62ec\u6c99\u76d2\u9694\u79bb\u3001\u5ba1\u6279\u673a\u5236\u3001\u7f51\u7edc\u7b56\u7565\u548c\u667a\u80fd\u4f53\u539f\u751f\u9065\u6d4b\u7b49\u6280\u672f\u624b\u6bb5\u3002Codex \u80fd\u591f\u81ea\u4e3b\u5ba1\u67e5\u4ee3\u7801\u5e93\u3001\u8fd0\u884c\u547d\u4ee4\u5e76\u4e0e\u5f00\u53d1\u5de5\u5177\u4ea4\u4e92\uff0c\u56e0\u6b64\u9700\u8981\u4e13\u95e8\u7684\u5b89\u5168\u63a7\u5236\u3002OpenAI \u4f7f\u7528 Codex \u65e5\u5fd7\u914d\u5408 AI \u9a71\u52a8\u7684\u5b89\u5168\u5206\u7c7b\u667a\u80fd\u4f53\uff0c\u5f53\u7aef\u70b9\u5b89\u5168\u5de5\u5177\u62a5\u544a\u5f02\u5e38\u6d3b\u52a8\u65f6\uff0c\u901a\u8fc7\u5206\u6790\u539f\u59cb\u8bf7\u6c42\u3001\u5de5\u5177\u6d3b\u52a8\u3001\u5ba1\u6279\u51b3\u7b56\u3001\u5de5\u5177\u7ed3\u679c\u548c\u7f51\u7edc\u7b56\u7565\u51b3\u7b56\u6765\u533a\u5206\u9884\u671f\u7684\u667a\u80fd\u4f53\u884c\u4e3a\u3001\u826f\u6027\u9519\u8bef\u548c\u771f\u6b63\u9700\u8981\u5347\u7ea7\u5904\u7406\u7684\u5a01\u80c1\uff0c\u4ece\u800c\u652f\u6301\u5b89\u5168\u5408\u89c4\u7684\u4f01\u4e1a\u7ea7\u91c7\u7528\u3002<\/p>\n

    English Summary:<\/strong> OpenAI detailed how it runs Codex securely using sandboxing, approvals, network policies, and agent-native telemetry. As coding agents can autonomously review repositories, run commands, and interact with development tools, they require purpose-built security controls. OpenAI uses Codex logs alongside an AI-powered security triage agent to analyze original requests, tool activities, approval decisions, and network policy blocks when endpoint alerts occur, distinguishing between expected behavior, benign mistakes, and threats requiring escalation to enabl…<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  9. \n

    [AINews] GPT-Realtime-2, -Translate, and -Whisper: new SOTA realtime voice APIs<\/strong>\uff08Latent Space\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>OpenAI \u53d1\u5e03\u4e86 GPT-Realtime-2 \u5b9e\u65f6\u8bed\u97f3 API\uff0c\u5b9a\u4f4d\u4e3a\u9762\u5411\u8bed\u97f3\u667a\u80fd\u4f53\u7684 GPT-5 \u7ea7\u63a8\u7406\u6a21\u578b\u3002\u8be5\u6a21\u578b\u652f\u6301\u539f\u751f\u8bed\u97f3\u5230\u8bed\u97f3\u4ea4\u4e92\uff0c\u5177\u5907 128K \u4e0a\u4e0b\u6587\u7a97\u53e3\uff08\u76f8\u6bd4\u524d\u4ee3 32K \u5927\u5e45\u63d0\u5347\uff09\uff0c\u53ef\u5728\u5bf9\u8bdd\u4e2d\u63a8\u7406\u3001\u4f7f\u7528\u5de5\u5177\u3001\u5904\u7406\u6253\u65ad\u3001\u4fee\u590d\u7528\u6237\u8bed\u97f3\u4fee\u6b63\uff0c\u5e76\u652f\u6301\u66f4\u957f\u4f1a\u8bdd\u3002\u6a21\u578b\u63d0\u4f9b\u4e94\u7ea7\u63a8\u7406\u5f3a\u5ea6\u8c03\u8282\uff08minimal \u5230 xhigh\uff09\uff0c\u6700\u4f4e\u5ef6\u8fdf\u4ec5 1.12 \u79d2\u3002\u4f01\u4e1a\u8bc4\u4f30\u663e\u793a\u663e\u8457\u6548\u679c\uff1aGlean \u62a5\u544a\u5b9e\u65f6\u7ec4\u7ec7\u8bed\u97f3\u4ea4\u4e92\u5e2e\u52a9\u5ea6\u63d0\u5347 42.9%\uff0cGenspark \u7684 Call for Me \u667a\u80fd\u4f53\u6709\u6548\u5bf9\u8bdd\u7387\u63d0\u5347 26% \u4e14\u6389\u7ebf\u7387\u964d\u4f4e\u3002\u8fd9\u6807\u5fd7\u7740\u8bed\u97f3\u667a\u80fd\u4f53\u4ece\u7b80\u5355\u7684\u8bed\u97f3\u8f93\u5165\u8f93\u51fa\u5305\u88c5\u5668\u5411\u5168\u53cc\u5de5\u3001\u5de5\u5177\u4f7f\u7528\u3001\u957f\u4e0a\u4e0b\u6587\u63a8\u7406\u667a\u80fd\u4f53\u7684\u91cd\u5927\u8f6c\u53d8\u3002<\/p>\n

    English Summary:<\/strong> OpenAI launched GPT-Realtime-2 via the Realtime API, framed as GPT-5-class reasoning for voice agents. The native speech-to-speech model features a 128K context window (up from 32K), supports mid-conversation reasoning, tool use, interruption handling, speech repair recovery, and longer sessions. It offers five reasoning effort levels from minimal to xhigh, with time-to-first-audio as low as 1.12s. Enterprise evaluations show strong results: Glean reported a 42.9% relative increase in helpfulness, while Genspark's Call for Me Agent saw a 26% increase in effective conversation rates with fewer dropped calls, marking a shift from speech I\/O wrappers to full-duplex, tool-using, reasoning agents.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  10. \n

    Improving token efficiency in GitHub Agentic Workflows<\/strong>\uff08GitHub AI\/ML\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>GitHub \u5206\u4eab\u4e86\u5176\u4f18\u5316\u5185\u90e8\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41 Token \u6548\u7387\u7684\u5b9e\u8df5\u7ecf\u9a8c\u3002\u7531\u4e8e\u6bcf\u5929\u5728\u6570\u767e\u4e2a\u4ed3\u5e93\u4e2d\u8fd0\u884c\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41\u4f1a\u4ea7\u751f\u5927\u91cf API \u8d39\u7528\uff0cGitHub \u901a\u8fc7 API \u4ee3\u7406\u7edf\u4e00\u6355\u83b7\u6240\u6709\u8fd0\u884c\u4e2d\u7684 Token \u4f7f\u7528\u6570\u636e\uff0c\u65e0\u8bba\u4f7f\u7528\u4f55\u79cd\u667a\u80fd\u4f53\u6846\u67b6\u3002\u4ed6\u4eec\u53d1\u73b0 Contribution Check \u5de5\u4f5c\u6d41 82-83% \u7684\u8f93\u5165 Token \u6765\u81ea\u7f13\u5b58\u8bfb\u53d6\uff0c\u4f46\u4f18\u5316\u540e\u6709\u6548 Token \u4ecd\u589e\u52a0 5%\uff0c\u539f\u56e0\u662f\u5de5\u4f5c\u8d1f\u8f7d\u4ece\u5904\u7406\u5c0f\u578b PR \u8f6c\u5411\u5927\u578b PR\u3002\u6587\u7ae0\u5f3a\u8c03\u4e86\u76d1\u63a7\u548c\u4f18\u5316 Token \u4f7f\u7528\u7684\u91cd\u8981\u6027\uff0c\u4ee5\u53ca\u5de5\u4f5c\u8d1f\u8f7d\u53d8\u5316\u5bf9\u6548\u7387\u6307\u6807\u7684\u63a9\u76d6\u6548\u5e94\u3002<\/p>\n

    English Summary:<\/strong> GitHub shared its experience improving token efficiency in agentic workflows that run on every pull request. By using an API proxy to capture token usage across all runs in a normalized format regardless of agent framework, they instrumented hundreds of workflows running against real API rate limits. They found that for the Contribution Check workflow, 82-83% of input tokens were cache reads, yet effective tokens increased 5% post-optimization due to a workload shift from small to large pull requests during a development burst. The post highlights the importance of monitoring token consumption and how workload shifts can mask per-turn efficiency gains.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  11. \n

    Agent pull requests are everywhere. Here\u2019s how to review them.<\/strong>\uff08GitHub AI\/ML\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>GitHub \u53d1\u5e03\u5173\u4e8e\u5982\u4f55\u5ba1\u67e5 AI Agent \u751f\u6210\u4ee3\u7801\u7684\u5b9e\u6218\u6307\u5357\u3002\u6587\u7ae0\u6307\u51fa\uff0c\u5f53\u524d\u8d85\u8fc7\u4e94\u5206\u4e4b\u4e00\u7684\u4ee3\u7801\u5ba1\u67e5\u6d89\u53ca Agent \u751f\u6210\u5185\u5bb9\uff0c\u4f46\u7814\u7a76\u663e\u793a\u8fd9\u7c7b\u4ee3\u7801\u5f80\u5f80\u5305\u542b\u66f4\u591a\u5197\u4f59\u548c\u6280\u672f\u503a\u52a1\u3002\u6307\u5357\u63d0\u51fa\u4e94\u5927\u5ba1\u67e5\u8981\u70b9\uff1a\u4e00\u662f\u8b66\u60d5 CI \u88ab\u7ed5\u8fc7\uff08\u5982\u5220\u9664\u6d4b\u8bd5\u3001\u964d\u4f4e\u8986\u76d6\u7387\u9608\u503c\uff09\uff1b\u4e8c\u662f\u68c0\u67e5\u4ee3\u7801\u590d\u7528\u76f2\u533a\uff0c\u907f\u514d\u91cd\u590d\u9020\u8f6e\u5b50\uff1b\u4e09\u662f\u8ffd\u8e2a\u5173\u952e\u8def\u5f84\u9a8c\u8bc1\u8fb9\u754c\u6761\u4ef6\u548c\u6743\u9650\u68c0\u67e5\uff1b\u56db\u662f\u5bf9\u4e8e\u5927\u578b PR \u8981\u6c42\u4f5c\u8005\u63d0\u4f9b\u6e05\u6670\u7684\u5b9e\u73b0\u8ba1\u5212\uff1b\u4e94\u662f\u5ba1\u67e5\u5de5\u4f5c\u6d41\u4e2d\u662f\u5426\u5b58\u5728\u63d0\u793a\u6ce8\u5165\u98ce\u9669\u3002\u5efa\u8bae\u5148\u8ba9 Copilot \u81ea\u52a8\u5ba1\u67e5\u5904\u7406\u673a\u68b0\u6027\u95ee\u9898\uff0c\u4eba\u7c7b\u4e13\u6ce8\u4e8e\u9700\u8981\u4e0a\u4e0b\u6587\u7684\u5224\u65ad\u6027\u5de5\u4f5c\u3002<\/p>\n

    English Summary:<\/strong> GitHub published a practical guide on reviewing AI agent-generated pull requests. With over one in five code reviews now involving agents, research shows agent code tends to carry more redundancy and technical debt. The guide outlines five red flags: CI gaming (weakened test coverage), code reuse blindness (duplicated utilities), hallucinated correctness (passing tests but wrong logic), agentic ghosting (unresponsive large PRs), and untrusted input in workflows (prompt injection risks).<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  12. \n

    Notes from inside China's AI labs<\/strong>\uff08Interconnects\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Interconnects \u535a\u5ba2\u4f5c\u8005\u8d70\u8bbf\u4e2d\u56fd\u4e3b\u8981 AI \u5b9e\u9a8c\u5ba4\u540e\u7684\u6df1\u5ea6\u89c2\u5bdf\u3002\u6587\u7ae0\u6307\u51fa\u4e2d\u56fd\u7814\u7a76\u4eba\u5458\u5c55\u73b0\u51fa\u6781\u5f3a\u7684\u52a1\u5b9e\u7cbe\u795e\u548c\u8c26\u900a\u6001\u5ea6\uff0c\u5927\u91cf\u6838\u5fc3\u8d21\u732e\u8005\u662f\u5e74\u8f7b\u5b66\u751f\uff0c\u4ed6\u4eec\u4e0d\u53d7\u8fc7\u5f80 AI \u7092\u4f5c\u5468\u671f\u5f71\u54cd\uff0c\u80fd\u5feb\u901f\u9002\u5e94\u65b0\u6280\u672f\u8303\u5f0f\u3002\u4e0e\u7f8e\u56fd\u5b9e\u9a8c\u5ba4\u76f8\u6bd4\uff0c\u4e2d\u56fd\u56e2\u961f\u66f4\u5c11\u4e2a\u4eba\u4e3b\u4e49\u51b2\u7a81\uff0c\u66f4\u4e13\u6ce8\u4e8e\u96c6\u4f53\u4f18\u5316\u6a21\u578b\u6574\u4f53\u8868\u73b0\u3002\u4f5c\u8005\u8fd8\u53d1\u73b0\u4e2d\u56fd AI \u751f\u6001\u5448\u73b0\u72ec\u7279\u683c\u5c40\uff1a\u51e0\u4e4e\u6240\u6709\u5927\u578b\u79d1\u6280\u516c\u53f8\u90fd\u5728\u81ea\u7814\u901a\u7528\u5927\u6a21\u578b\uff08\u5982\u7f8e\u56e2\u3001\u5c0f\u7c73\uff09\uff0c\u4f53\u73b0\u51fa\u5f3a\u70c8\u7684\u6280\u672f\u81ea\u4e3b\u638c\u63a7\u610f\u8bc6\uff1b\u6570\u636e\u4ea7\u4e1a\u76f8\u5bf9\u4e0d\u53d1\u8fbe\uff0c\u5b9e\u9a8c\u5ba4\u591a\u9009\u62e9\u81ea\u5efa\u8bad\u7ec3\u73af\u5883\uff1b\u867d\u7136\u6781\u5ea6\u6e34\u671b\u66f4\u591a\u82f1\u4f1f\u8fbe\u82af\u7247\uff0c\u4f46\u534e\u4e3a\u7b49\u56fd\u4ea7\u52a0\u901f\u5668\u5728\u63a8\u7406\u573a\u666f\u83b7\u5f97\u79ef\u6781\u8bc4\u4ef7\u3002<\/p>\n

    English Summary:<\/strong> A field report from visits to leading Chinese AI labs highlights key cultural and structural differences. Chinese researchers display strong pragmatism and humility, with many core contributors being young students free from prior AI hype cycles, enabling rapid adaptation to new paradigms. Unlike US labs where individual recognition often creates organizational friction, Chinese teams focus more on collective model optimization. The ecosystem shows unique traits: nearly every major tech company (Meituan, Xiaomi, etc.) builds its own general-purpose LLMs reflecting a deep desire for stack ownership; the data industry is less developed so labs build training environments in-house; and while desperate for more Nvidia chips, Huawei accelerators are viewed positively for inference workloads.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  13. \n

    Scaling Trusted Access for Cyber with GPT-5.5 and GPT-5.5-Cyber<\/strong>\uff08OpenAI News\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>OpenAI \u6269\u5c55 Trusted Access for Cyber \u8ba1\u5212\uff0c\u63a8\u51fa GPT-5.5 \u548c GPT-5.5-Cyber \u4e24\u6b3e\u6a21\u578b\u652f\u6301\u7f51\u7edc\u5b89\u5168\u9632\u5fa1\u5de5\u4f5c\u3002GPT-5.5 with TAC \u9762\u5411\u7ecf\u6838\u5b9e\u7684\u9632\u5fa1\u8005\uff0c\u964d\u4f4e\u5b89\u5168\u76f8\u5173\u8bf7\u6c42\u7684\u62d2\u7edd\u7387\uff0c\u652f\u6301\u6f0f\u6d1e\u8bc6\u522b\u3001\u6076\u610f\u8f6f\u4ef6\u5206\u6790\u3001\u68c0\u6d4b\u5de5\u7a0b\u7b49\u5de5\u4f5c\u6d41\uff0c\u540c\u65f6\u7ee7\u7eed\u963b\u6b62\u6076\u610f\u6d3b\u52a8\u3002\u66f4\u4e13\u4e1a\u7684 GPT-5.5-Cyber \u5904\u4e8e\u9650\u91cf\u9884\u89c8\u9636\u6bb5\uff0c\u9762\u5411\u5173\u952e\u57fa\u7840\u8bbe\u65bd\u4fdd\u62a4\u4eba\u5458\uff0c\u652f\u6301\u6388\u6743\u7ea2\u961f\u6d4b\u8bd5\u548c\u6e17\u900f\u6d4b\u8bd5\u7b49\u9ad8\u654f\u611f\u5ea6\u4efb\u52a1\u3002OpenAI \u4e0e Cisco\u3001CrowdStrike\u3001Palo Alto Networks \u7b49\u5b89\u5168\u5382\u5546\u5408\u4f5c\uff0c\u6784\u5efa\u4ece\u6f0f\u6d1e\u7814\u7a76\u5230\u7f51\u7edc\u9632\u62a4\u7684\u5b8c\u6574\u5b89\u5168\u98de\u8f6e\uff0c\u5e76\u63a8\u51fa Codex Security \u5de5\u5177\u5e2e\u52a9\u5f00\u6e90\u9879\u76ee\u8bc6\u522b\u548c\u4fee\u590d\u6f0f\u6d1e\u3002<\/p>\n

    English Summary:<\/strong> OpenAI expanded its Trusted Access for Cyber program with GPT-5.5 and GPT-5.5-Cyber to support cybersecurity defenders. GPT-5.5 with TAC offers verified defenders reduced refusal rates on defensive tasks like vulnerability triage, malware analysis, and detection engineering while blocking malicious use. The more specialized GPT-5.5-Cyber is in limited preview for critical infrastructure defenders, enabling authorized red teaming and penetration testing workflows. OpenAI is partnering with security vendors including Cisco, CrowdStrike, and Palo Alto Networks to build a security flywheel spanning vulnerability research to network protection, and released Codex Security to help open-source projects identify and remediate vulnerabilities.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  14. \n

    [AINews] Anthropic-SpaceXai's 300MW\/$5B\/yr deal for Colossus I, ARR growth is 8000% annualized<\/strong>\uff08Latent Space\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Anthropic \u5728\u7b2c\u4e8c\u5c4a\u5f00\u53d1\u8005\u5927\u4f1a\u4e0a\u5ba3\u5e03\u4e0e SpaceX\/xAI \u8fbe\u6210\u91cd\u5927\u7b97\u529b\u5408\u4f5c\uff0c\u5c06\u63a5\u7ba1 Colossus I \u8d85\u7ea7\u8ba1\u7b97\u96c6\u7fa4\uff08\u7ea6 300MW\u300122 \u4e07\u5757 GPU\uff09\uff0c\u9884\u8ba1\u5e74\u8d39\u7528\u8fbe 50 \u4ebf\u7f8e\u5143\u3002\u6b64\u4e3e\u65e8\u5728\u89e3\u51b3 Claude \u7528\u6237\u589e\u957f 80 \u500d\u5e26\u6765\u7684\u7b97\u529b\u74f6\u9888\uff0c\u7acb\u5373\u751f\u6548\u7684\u6539\u8fdb\u5305\u62ec\uff1aClaude Code \u7684 5 \u5c0f\u65f6\u901f\u7387\u9650\u5236\u7ffb\u500d\u3001\u53d6\u6d88 Pro\/Max \u7528\u6237\u9ad8\u5cf0\u65f6\u6bb5\u9650\u5236\u3001\u5927\u5e45\u63d0\u5347 Opus API \u901f\u7387\u9650\u5236\u3002\u5927\u4f1a\u8fd8\u63a8\u51fa Claude Managed Agents \u7684\u4e09\u9879\u65b0\u529f\u80fd\uff1aDreaming\uff08\u8de8\u4f1a\u8bdd\u8bb0\u5fc6\uff09\u3001Outcomes\uff08\u7ed3\u679c\u8bc4\u4f30\u4e0e\u8bc4\u5206\uff09\u548c Workflows\uff08\u5de5\u4f5c\u6d41\u7f16\u6392\uff09\u3002CEO Dario Amodei \u8868\u793a 2026 \u5e74\u53ef\u80fd\u51fa\u73b0\u5355\u4eba\u5341\u4ebf\u7f8e\u5143\u516c\u53f8\uff0c\u5e76\u5f3a\u8c03\u591a\u667a\u80fd\u4f53\u7cfb\u7edf\u548c\u4f01\u4e1a\u7ea7\u670d\u52a1\u662f\u91cd\u70b9\u65b9\u5411\u3002<\/p>\n

    English Summary:<\/strong> At its second developer conference, Anthropic announced a major compute partnership with SpaceX\/xAI to take over the Colossus I supercluster (estimated 300MW, 220,000 GPUs) for approximately $5B\/year. The deal addresses compute constraints from 80x usage growth, with immediate improvements including doubled Claude Code 5-hour rate limits, removal of peak-hour throttling for Pro\/Max users, and substantially increased Opus API limits. Three new Claude Managed Agents features were introduced: Dreaming (cross-session memory), Outcomes (rubric-based evaluation), and Workflows. CEO Dario Amodei predicted 2026 could see a one-person billion-dollar company, emphasizing multi-agent systems and enterprise services as key focus areas.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n

  15. \n

    Ollama is now powered by MLX on Apple Silicon in preview<\/strong>\uff08Ollama Blog\uff09<\/p>\n

    \u4e2d\u6587\u6458\u8981\uff1a<\/strong>Ollama \u53d1\u5e03\u57fa\u4e8e Apple MLX \u6846\u67b6\u7684\u9884\u89c8\u7248\u672c\uff0c\u5927\u5e45\u63d0\u5347 Apple Silicon \u8bbe\u5907\u4e0a\u7684\u63a8\u7406\u6027\u80fd\u3002\u5728 M5 \u7cfb\u5217\u82af\u7247\u4e0a\uff0c\u65b0\u7248\u672c\u5229\u7528 GPU Neural Accelerator \u663e\u8457\u964d\u4f4e\u9996 token \u5ef6\u8fdf\u5e76\u63d0\u5347\u751f\u6210\u901f\u5ea6\uff0c\u5b9e\u6d4b Qwen3.5-35B-A3B \u6a21\u578b\u5728 NVFP4 \u91cf\u5316\u4e0b\u53ef\u8fbe 1851 token\/s \u7684\u9884\u586b\u5145\u901f\u5ea6\u548c 134 token\/s \u7684\u89e3\u7801\u901f\u5ea6\u3002\u65b0\u7248\u672c\u652f\u6301 NVIDIA NVFP4 \u683c\u5f0f\uff0c\u5728\u4fdd\u6301\u6a21\u578b\u7cbe\u5ea6\u7684\u540c\u65f6\u964d\u4f4e\u5185\u5b58\u5e26\u5bbd\u548c\u5b58\u50a8\u9700\u6c42\uff0c\u4e0e\u751f\u4ea7\u73af\u5883\u63a8\u7406\u7ed3\u679c\u4fdd\u6301\u4e00\u81f4\u3002\u7f13\u5b58\u7cfb\u7edf\u4e5f\u5f97\u5230\u5347\u7ea7\uff0c\u652f\u6301\u8de8\u4f1a\u8bdd\u590d\u7528\u3001\u667a\u80fd\u68c0\u67e5\u70b9\u548c\u66f4\u667a\u80fd\u7684\u6dd8\u6c70\u7b56\u7565\uff0c\u7279\u522b\u4f18\u5316\u4e86 Claude Code\u3001OpenClaw \u7b49\u7f16\u7801\u52a9\u624b\u7684\u54cd\u5e94\u901f\u5ea6\u3002<\/p>\n

    English Summary:<\/strong> Ollama released a preview version powered by Apple's MLX machine learning framework, delivering significantly faster inference on Apple Silicon. On M5 series chips, the new version leverages GPU Neural Accelerators to reduce time-to-first-token and increase generation speed, with the Qwen3.5-35B-A3B model achieving up to 1851 token\/s prefill and 134 token\/s decode with NVFP4 quantization. The release adds support for NVIDIA's NVFP4 format, maintaining model accuracy while reducing memory bandwidth and storage requirements for production parity. The caching system was upgraded with cross-conversation reuse, intelligent checkpoints, and smarter eviction policies, specifically optimizing responsiveness for coding agents like Claude Code and OpenClaw.<\/p>\n

    \u539f\u6587\u94fe\u63a5<\/a><\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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