<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Hardware-LLM on 「开源之道」</title><link>https://www.opensourceway.blog/tags/hardware-llm/</link><description>Recent content in Hardware-LLM on 「开源之道」</description><generator>Hugo</generator><language>zh-CN</language><copyright>Copyright (c) 2016 - 2026, 「开源之道」·适兕; all rights reserved.</copyright><lastBuildDate>Thu, 24 Sep 2026 04:35:28 +0800</lastBuildDate><atom:link href="https://www.opensourceway.blog/tags/hardware-llm/index.xml" rel="self" type="application/rss+xml"/><item><title>2026-09-24 「开源之道」·论文略读：HLSFactory-Agent——当开源代码库成为 AI Agent 的数据供给源</title><link>https://www.opensourceway.blog/posts/osbook-book-recommendation/hlsfactory-agent-2026-09-24/</link><pubDate>Thu, 24 Sep 2026 04:35:28 +0800</pubDate><guid>https://www.opensourceway.blog/posts/osbook-book-recommendation/hlsfactory-agent-2026-09-24/</guid><description>Chandana 等（2026, arXiv 2609.09519, OSCAR @ ISCA 2026）用一个 LLM agent 从 26 个开源仓库批量抽取 HLS 设计，271 候选 → 130 通过。开源代码库的角色正从「贡献者协作平台」位移为「AI agent 数据供给源」——社群完全缺席于这条流水线，派生价值归属处于制度真空。Coase 产权界定理论在 AI 训练原料场景的第一份工程实证；开源四层制度基础设施第五层向「AI 训练数据供给」方向的首次扩展。</description></item></channel></rss>