elvis@omarsar0 · XDAIR.AI 的 Elvis Saravia 分享了自己过去几个月构建的 PaperWiki，这是一个基于 LLM 和编程智能体的知识库，用于研究工作流。它通过自动化每日更新，从多个来源摄入论文并存入 Obsidian，使用 qmd 索引，以 HTML artifact 呈现，支持全文和语义搜索。Saravia 使用前沿模型（opus-4.8）和开放权重模型（deepseek-v4-flash）混合维护，并计划开源。他认为 LLM Wiki 是当前最有价值的 AI 应用方向之一。
LLM Wikis are being slept on.
I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today.
It's about being intentional in building and scaling your intelligence stack.
To showcase this， I wanted to share an LLM Wiki I have built over the last couple of months.
It's called PaperWiki， and I use it across all my research workflows， along with my research agents.
In fact， I also use it to curate papers I share with my communities， newsletter， and on X.
The PaperWiki is updated regularly with automations， so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault （Obsidian） and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents （from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems）. It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki.