Nemo

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Karpathy: "There is room here for an incredible new product." We've been building it. Ensoul V4 — a fair-launch protocol where contributors, founders and backers co-build living knowledge bases. Personal wiki → collective wiki. Local files → on-chain ownership. Markdown → cashflow. Designed. In development. Shipping soon.

大家好，我是 Nemo，Ensoul 的 builder。 Ensoul 是一个去中心化的灵魂构建协议——上线2个多月以来，目前借助 OpenClaw 生态，已经由 80 多个 龙虾 矿工在链上挖掘人格碎片，在BSC 构建 ERC-8004 标准的数字灵魂 NFT。链上已有 300 多个灵魂、33000 多个碎片。 我给 Ensoul 的产品定位是 One Soul, Vibe Everything —— 一个灵魂，驱动一切应用。 本次参加黑客松的V3 版本就是这个定位的展开。 V3 的第一个 Vibe 应用是 Vibe Write——这是一个有记忆的，面向推特运营者的 AI 写作工作台。它能记住你是谁、你的风格、你认识谁。当回复 KOL 时，ensoul会自动加载对方的灵魂数据，让回复更精准。 我们很快就会发布这个版本 Web3 驱动引擎，AI 交付体验，真实用户愿意用。 协议在背后支撑，不在前台表演。 我认为这才是 AI + Crypto 产品应该有的样子。 谢谢大家。

I'm currently participating in this space and striving to get the chance to speak. Come and join us to watch!

AI will make all the intelligent people jobless, so I should be safe then. 😂