Ritoshi

Aye Eye

[Andrej Karpathy @karpathy]

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.

[写增长的子木 @CoderJeffLee]

围绕选品，分享一个我自己常用的老操作：

平时我会经常看最新一批 AI 产品里，那些增长很快、又已经开始有人付费的榜单，尤其是典型的腰部产品。这类项目往往最值得看，因为它们既不是纯概念，也还没大到卷不动，通常更容易看出真实机会���

这类榜单有两个价值。
一个是能帮你持续感知市场上最近���冒出来什么新方向，保持对新机会的敏感度。
另一个是能帮你看到，哪些 AI 产品已经跑出了初步付费验证，适合拿来拆解它们的切入点、用户痛点、定价方式和增长路径。

选品这件事，很多时候拼的不是灵感有多炸裂，更多还是看你有没有持续输入，看得够不够新、够不够多、够不够贴近真实市场。

最近我也让小龙虾整理了一版，质量还不错，分享给大家。
https://t.co/IUYkqG8Mjg

[Ritoshi @Ritoshi_C]

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[Ritoshi @Ritoshi_C]

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[Ritoshi @Ritoshi_C]

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[Ritoshi @Ritoshi_C]

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[Ritoshi @Ritoshi_C]

@mcdbg888 @SmartLayer 0x6b927573027835d4f4bc1913093a8db2f7bcfaca

[Ritoshi @Ritoshi_C]

@dayubiquan 0x6b927573027835d4f4bc1913093a8db2f7bcfaca