<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>H5X AI Lab</title><link>https://ai.h5x.app/</link><description>AI 技术解读与实践</description><language>zh-CN</language><item><title>让一栋房子开口说话：语音 Agent 的完整工程链</title><link>https://ai.h5x.app/articles/nemotron-voice/</link><guid>https://ai.h5x.app/articles/nemotron-voice/</guid><description>NVIDIA 的万圣节互动装置把识别、对话、语音合成与面部动画连成实时系统。真正值得借鉴的是它对状态和资源预算的处理。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>像更新软件一样更新模型：Granite 的模块化路线</title><link>https://ai.h5x.app/articles/granite-modular/</link><guid>https://ai.h5x.app/articles/granite-modular/</guid><description>Granite Switch 4.1.1 把语音与决策能力做成可更新的适配器。模块化交付给模型系统带来了版本管理的新问题。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>AI-ready 不止是公开：ProtoStar 与科研数据可用性</title><link>https://ai.h5x.app/articles/protostar-data/</link><guid>https://ai.h5x.app/articles/protostar-data/</guid><description>Project ProtoStar 聚焦数据、模型与代码的可发现和可复用。对于 RAG 与科研 Agent，元数据和来源链同样是产品能力。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>读懂 Mistral Large 4：两种稀疏机制与部署取舍</title><link>https://ai.h5x.app/articles/mistral-large4/</link><guid>https://ai.h5x.app/articles/mistral-large4/</guid><description>MoE 选择专家，稀疏注意力选择上下文位置。这两种机制解决不同问题，模型选型需要回到自己的负载。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>Apple 端侧 AI：把语音与决策交给专用模型</title><link>https://ai.h5x.app/articles/apple-specialized/</link><guid>https://ai.h5x.app/articles/apple-specialized/</guid><description>AnySpeechRecognizer 与 AnyDecisionModel 提供可选择模型的 Swift 接口，说明端侧 AI 正在从单一聊天模型走向任务组合。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>小模型的专业化路线：LFM3 与多模态端侧部署</title><link>https://ai.h5x.app/articles/lfm3-edge/</link><guid>https://ai.h5x.app/articles/lfm3-edge/</guid><description>Liquid AI 将文本、视觉、音频与决策模型放在共享骨干上。专业化能力需要与设备资源、数据和评测一起设计。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>LLM 能判断研究创新吗？先检验裁判</title><link>https://ai.h5x.app/articles/novelty-judges/</link><guid>https://ai.h5x.app/articles/novelty-judges/</guid><description>Ai2 的 novelty benchmark 说明，提示、比较方式和检索配置都会改变模型对创新性的判断。自动评测本身也需要评测。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>推理账单降低 68%：先找出 token 花在哪里</title><link>https://ai.h5x.app/articles/inference-cost/</link><guid>https://ai.h5x.app/articles/inference-cost/</guid><description>Cloudflare 的代码评审系统案例表明，缓存命中率高并不意味着总成本低。成本优化需要观察工作量、上下文与质量。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>结构化提取的新分工：Clairic 与类型化结果</title><link>https://ai.h5x.app/articles/typed-extraction/</link><guid>https://ai.h5x.app/articles/typed-extraction/</guid><description>Cloudflare 公布的提取模型把数值、日期和置信区间作为直接输出。应用仍需要定义校验与不确定性处理。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item><item><title>上下文工程：把注意力当作有限预算</title><link>https://ai.h5x.app/articles/context-engineering/</link><guid>https://ai.h5x.app/articles/context-engineering/</guid><description>这是一篇基础阅读。Anthropic 对上下文筛选、压缩与长期任务状态的讨论，仍可作为构建可靠 Agent 的工程检查表。</description><pubDate>Sat, 31 Oct 2026 16:00:00 GMT</pubDate></item></channel></rss>