
docs.dev — AI drafts your docs. Your team makes them true.
AI为代码库起草文档,团队编辑后通过提交发布。
linktothenew · HN
Docs.dev – A docs site you own, where AI drafts and you edit in place
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AI为代码库起草文档,团队编辑后通过提交发布。
linktothenew · HN
Docs.dev – A docs site you own, where AI drafts and you edit in place

将文档和文本转成播客,支持 AI 语音生成和编辑。
Inpodcast AI — 将文档转成播客音频,支持 PDF、Word、Markdown 和 TXT 文件格式

将屏幕录制转换为精美的PDF指南。
@vanmurten_eth · X
Guidesnap: turn any screen recording into a polished PDF guide.

为创意团队打造的AI视频和图像审阅工具,支持自动转录和像素级评论。
@codesharpdev · X

存储提示词和背景信息,通过 MCP 让 Claude Code、Cursor 等 AI 工具共享访问。
@vibexp_io · X
Your plan is now code. Claude Code Dynamic Workflows fan out up to 1,000 subagents, 16 at once, each in its own context, verifying until the results converge. Built for big bug hunts, migrations and audits. Source:

管理学术会议:提交和审阅论文、处理日程安排和注册。
@harryjwang · X
I've used many of the major conference systems. Plenty still have docs as an unsearchable static site — or worse, as PDFs that stopped tracking the product three versions ago. Somehow, still true in 2026. We took an AI-first path with instead. Our docs are generated from the codebase itself. A custom Claude Code skill writes each page and retakes the screenshots whenever a feature changes — so the docs don't drift from the product. Every page still gets reviewed by a human before it ships. Now you can talk to them, too. We just shipped "Ask AI" over our docs: This feature also turned out to be a nice teaching case for agentic RAG vs. classic RAG: • Classic RAG: chunk → embed → vector DB → retrieve once, by fixed rules • Agentic RAG: hand the model a search tool and let it drive — write a query, read the results, rewrite, retry At more than 100 pages, still no vector database needed. And every answer cites the exact page it came from. #Age