Wei Liu

赛先生，德先生

[Wei Liu @iliuwei]

https://t.co/viz72dZQkE

[Wei Liu @iliuwei]

https://t.co/IiaZClnHC0

[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.

[Wei Liu @iliuwei]

https://t.co/5x4W7vgzey

[Wei Liu @iliuwei]

https://t.co/vC55of3xoU

[Wei Liu @iliuwei]

https://t.co/j2LtB1EgXZ

[Wei Liu @iliuwei]

https://t.co/YhBYgYZLLT

[Wei Liu @iliuwei]

https://t.co/hu52Oi2cIv

[Wei Liu @iliuwei]

https://t.co/ZXTHplVBKD

[Wei Liu @iliuwei]

https://t.co/TkjrcofGKi

[Wei Liu @iliuwei]

@elonmuskTN 9

[Wei Liu @iliuwei]

@fangshimin 😅

[方舟子 @fangshimin]

《中南大学原校长张尧学被查，“透明计算”怎么办》（图片版）

[美国驻华使领馆 U.S. Mission to China @USA_China_Talk]

1942年春天，滇缅公路被切断后，中美两国开辟了飞越喜马拉雅山脉的空中物资运输线路，这就是著名的“驼峰航线”。从那时起到1945年8月二战结束，这条航线是外界物资进入中国最主要的通道。驼峰航线最初由美国陆军航空兵第十航空队负责，后来转交给空运总队（Air Transport Command，简称ATC）。中国航空公司（简称CNAC）也承担了相当一部分飞行任务。

除了ATC，美国驻华空军其他部门也要经常飞越驼峰。例如，我们这次新发现的珍贵二战中国彩色影片里的第14航空队308轰炸大队，他们的主要任务是支援中国地面部队，从空中打击敌方目标。除了战斗任务外，第308轰炸大队还在维持盟军补给线方面发挥了关键作用。308轰炸大队组建后，第一件事就是往返驼峰航线，运输物资。由于所有物资都必须通过驼峰航线运输，这使得每一次战斗任务都需要大约三次驼峰飞行。
#二战胜利80周年
#尘封多年的二战中国彩色影片重见天日

[Wei Liu @iliuwei]

https://t.co/LHzb15gMzv

[Wei Liu @iliuwei]

https://t.co/4eyGwB70rj

[Wei Liu @iliuwei]

https://t.co/Z2y0ZlVXQI

[Mr Panda @PandaTalk8]

神经网络的可视化

[Tom Huang @tuturetom]

卡内基梅隆大学这本 628 页的 《无限深入纯数学》的教科书页太好了，关键可以免费下载阅读⚡️

作者的目标是帮助读者从数学「消费者」变为数学《生产者》，从学习计算导数转为从导数定义推导链式法则，特别适合像 OpenAI O1 这类应用数学原理进行推理的模型理解🔥

阅读地址👉 https://t.co/Xbxje4q1pW

[卫斯理 @imwsl90]

https://t.co/y5aio0BZbJ 这本在线的书挺有趣的 《Software Design by Example》，编程为什么会让人失去兴趣，CRUD写多了，看点这些有趣的技巧，解解毒。

[量價為王 @toppy20241025]

@lidangzzz https://t.co/CxSg4Wo9Hf