"AI for Teachers Has a Measurement Problem: Building the Learning-Outcome Feedback Loop"

Teacher AI must connect time saved to teaching practice, student behavior, durable learning, and evidence strong enough to guide school decisions.

Administrator Administrator Published on 2026-07-19

"Detecting Browser Agents After Fingerprints Fail: Behavior, Signed Identity, and False Positives"

Browser agent detection now needs three layers: behavioral risk signals, signed identity, and explicit authorization for high-risk actions.

Administrator Administrator Published on 2026-07-19

一次 AI 越狱攻击有多严重:CJS、JEF 与 CVSS 评分框架解析

越狱成功率无法说明真实危害。本文解析 Anthropic CJS 四轴评分,并对照 JEF 与 CVSS,建立从攻击成功到组织风险的分层评估方法。

Administrator Administrator Published on 2026-07-18

How Severe Is an AI Jailbreak? CJS, JEF, and CVSS Scoring Explained

Anthropic's CJS scores AI jailbreak severity across four axes. This guide compares CJS, JEF, and CVSS, then separates severity from risk.

Administrator Administrator Published on 2026-07-18

Claude 内部的隐藏工作区:J-space 如何承载没有说出口的思考

Anthropic 在 Claude 内部发现了一个低容量、可语言化且具有因果作用的 J-space。它能承载没有输出的中间概念,却不能证明 Claude 拥有主观意识。

Administrator Administrator Published on 2026-07-18

Claude's Hidden Workspace: How J-Space Carries Thoughts It Never Says

Anthropic found a small, causally active J-space inside Claude. It carries silent concepts but does not prove subjective consciousness.

Administrator Administrator Published on 2026-07-18

"MRC 协议:OpenAI 新网络层如何支撑大规模 AI 训练"

2026 年,训练一个前沿 AI 模型需要数万个 GPU 协同工作数月。但制约训练速度的关键因素往往不是 GPU 算力,而是连接这些 GPU 的网络。当一条链路拥塞或一台交换机故障就能让整个训练任务停滞、导致数千块昂贵加速器空转时,网络就成为 AI 基础设施的决定性瓶颈。 OpenAI 的回应是 M

Administrator Administrator Published on 2026-06-08

"MRC Protocol: How OpenAI's New Networking Layer Powers AI Training at Scale"

Training a frontier AI model in 2026 requires tens of thousands of GPUs working in tight synchronization for months. Yet the factor that most often li

Administrator Administrator Published on 2026-06-08

"B2B Signals 解读:2026年前沿企业的 AI 采用正在拉开不可逆的差距"

"OpenAI B2B Signals 报告显示前沿企业每个员工的 AI 智能消耗是典型企业的 3.5 倍。36/64 的体积与深度分裂揭示了为什么大多数企业在 AI 采用上问错了问题。"

Administrator Administrator Published on 2026-06-06

"B2B Signals: How Frontier Enterprises Are Scaling AI Adoption in 2026"

"OpenAI's B2B Signals report reveals frontier firms use 3.5x more AI per worker than typical firms. The 36/64 split between volume and depth reveals w

Administrator Administrator Published on 2026-06-06
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