
xysq.ai · Collaborative memory for AI-native teams.
为AI原生团队构建共享知识图谱,组织团队背景和智能体信息。
ximihoque · HN
Collaborative context-sharing memory platform for agents and teams
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为AI原生团队构建共享知识图谱,组织团队背景和智能体信息。
ximihoque · HN
Collaborative context-sharing memory platform for agents and teams

Fanancial 跟踪和比较职业体育队的估值随时间变化。
@shubhs99 · X
This was one of my early and favorite vibe coding projects tracking the four North American sports valuations and transactions

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