Meta 多阶段广告排序把长序列计算移出请求链路，并用迁移效率、延迟和业务结果共同约束扩容。
Meta multi-stage ads ranking separates long user modeling from online scoring. Learn its scaling laws, transfer ratio, and release gates.
从参考实现、参数容差、模拟真值、数值诊断到真实数据，建立科学 Agent 的五层验证栈。
A five-layer verification stack for scientific AI agents, from reference parity and parameter tolerances to simulation truth and stewardship.
Meta GEM 将端到端 MFU 提升至 20% 至 25%。真正可复用的是围绕真实负载持续识别并转移瓶颈的方法。
Meta GEM training efficiency shows why LLM-scale recommenders need workload-specific kernels, precision, parallelism, memory, and profiling.
California DROP reaches 600+ data brokers. Use status, matching, deadlines, and follow-up checks to build a verifiable deletion workflow.
产品的 AI 可用性要看 Agent 能否完成真实任务，并通过外部状态、恢复能力、成本与回归稳定性验证。
Test whether AI agents can use your product with dynamic task evals that measure correct state changes, recovery, cost, and regression stability.
AI 垃圾 CVE 会诱发无效甚至有害的补丁。自动修复前必须核验来源、源码、可达性、隔离复现与真实补丁。
