Bagaimana AI Mencegah 'Kiamat' Internet Skala Global
Google 和 OpenAI 自家模型出手，帮忙找到并修复了闹得沸沸扬扬的 PHP2SHELL 漏洞，安全圈的可以看看。
PHP2SHELL 漏洞在全球范围引发关注。Google 与 OpenAI 表示，其 AI 模型被用于定位该漏洞相关安全缺口并辅助修复。两家公司均以自身模型参与漏洞发现与修补工作。
Bagaimana AI Mencegah 'Kiamat' Internet Skala Global
Google dan OpenAI mengatakan model AI-nya membantu menemukan dan memperbaiki celah keamanan PHP2SHELL yang menggemparkan dunia.
谷歌 CEO 亲自讲自己怎么用智能体干活：自动汇总内部动态、盯算力利用率，决策权还在人手里，也有管理者吐槽管 10 多个智能体反而更累。
The object-oriented structure explains why single-harness tuning keeps trading one task type's gains for another's losses, and shows a router can avoid that cost on out-of-distribution tasks. Teams building agentic pipelines can adopt the pattern directly: keep the base harness as fallback and route to specialists only when runs match known failure clusters. The AutomationBench comparison against Meta-Harness gives a concrete baseline for evaluating similar routing setups.
The practical effect is that vLLM's optimizations — prefix caching and speculative decoding — now apply to TPU hardware, giving teams a second accelerator option beyond NVIDIA GPUs for self-hosted inference. Organisations already locked into the PyTorch and vLLM stack can evaluate TPU economics without rewriting their serving code. It also signals Google's continued push to open TPU inference beyond JAX.
PyTorch developers can now target TPU hardware through the same APIs they already use, reducing the migration cost that historically kept most teams on NVIDIA GPUs. Teams running training or inference pipelines with TorchTitan, vLLM, or SGLang can evaluate TPUs without rewriting code. The use of agents for kernel generation and sharding also hints at how optimization work itself may be automated.
Google 这篇论文把面向对象那套设计搬进自进化 agent 框架里，做 agent 开发的可以看看思路。
