SynthID at Scale: Design the Watermark Verification Throughput Architecture

SynthID scale depends on verification throughput: version routing, uncertainty, audit evidence, review queues, and cost per defensible verdict.

Administrator Administrator Published on 2026-10-18

Claude Code 代码审查:把审查意见变成可持续的回归控制

把 Claude Code 的审查意见转成可复现测试、静态规则、评测集与风险门禁,让同类错误无法静默复发。

Administrator Administrator Published on 2026-10-18

Claude Code Code Review: Turn Findings into Durable Regression Controls

Use Claude Code review findings to build reproducible tests, lint rules, evals, and risk gates that prevent the same failures from returning.

Administrator Administrator Published on 2026-10-18

Claude 设计量子电路:从优化收益到独立验证,需要一套成本账本

Claude 优化了八类量子电路任务。本文用成本与验证账本拆开 TQ 收益、失败预算、搜索成本、硬件映射和复现状态。

Administrator Administrator Published on 2026-10-17

Claude-Designed Quantum Circuits Need a Cost-to-Verification Ledger

Claude improved eight quantum circuit tasks. A cost-to-verification ledger shows what TQ gains prove, where costs move, and reproduction gaps.

Administrator Administrator Published on 2026-10-17

Claude 追溯 ECDSA 曲线起源:把历史归因与密码学发现分开验证

Claude 将 NIST 曲线 seed 追溯到时间戳驱动的 lrand48。本文用五级证据栈拆开可复算机制、历史归因与作者意图。

Administrator Administrator Published on 2026-10-17

Claude Reveals ECDSA Curve Origins: Separate Historical Attribution from Cryptographic Discovery

Claude traced NIST curve seeds to lrand48 timestamps. This evidence audit separates reproducible mechanism, historical attribution, and intent.

Administrator Administrator Published on 2026-10-17

AI 基准漏掉九成工作时间:预测岗位替代前,先建立任务覆盖地图

Mercor 估计美国九成工作时间在现有基准中缺少覆盖。本文拆开任务覆盖、模型能力与岗位暴露,给出企业任务地图方法。

Administrator Administrator Published on 2026-10-17

AI Benchmarks Miss 90% of Work: Build a Task-Coverage Map Before Predicting Job Loss

Mercor estimates 90% of US work hours lack benchmark coverage. Separate task coverage, model capability, and job exposure before forecasting.

Administrator Administrator Published on 2026-10-17

Meta Forum: Measure Review Boundaries, Not Code

Meta Forum shows that AI-native engineering depends on specifications, observable context, verifiers, and human production gates.

Administrator Administrator Published on 2026-10-16
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