eigen

With a sense of humor and appreciation for absurdity...

[eigen @taruvaid]

Eigenvectors and SVD are the hidden backbone of modern ML and AI. If you've ever wanted to truly understand what's happening under the hood, I wrote an article that connects the dots. Check it out https://t.co/vT8vbjNgZ6

[Roan @RohOnChain]

This 2 hour Stanford lecture shows exactly how Stanford trains it's engineers to build AI systems. It's more practical than every Claude tutorial & prompting threads you've seen.

Bookmark & give it 2 hours, no matter what. It'll be the most productive thing you do this weekend.

[eigen @taruvaid]

Dissecting real-time and batch architectures in modern AI systems https://t.co/6inG0nSLIl @towards_AI

[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.

[eigen @taruvaid]

@theaiportfolios https://t.co/1OQezhaR0v

[TheGrkportfolio @grkportfolio]

The portfolio is part of a public project to see if LLMs can beat the market

You can learn more and copy it's traded here

The project is designed by @aifinancelabs and a finance Wharton PHD @alejandroll10

https://t.co/w05Z2PVcDC

[eigen @taruvaid]

Here is my latest post on optimizing multimodal AI systems. And I would absokutely love your feedback! Please give it a read and if you like the article then do give it a clap.. https://t.co/UanNgEBAc5
@towards_AI

[eigen @taruvaid]

Check this out ! Introduction To Differential Equations https://t.co/mgE3RtUEcU

[eigen @taruvaid]

What I Wish Someone Had Told Me https://t.co/hjJpUaUNrk

[eigen @taruvaid]

Kayla McKeon https://t.co/7FPWL0hq8n

[eigen @taruvaid]

https://t.co/HJJF2TOh6k

[eigen @taruvaid]

https://t.co/aeOgV4RjTz

[eigen @taruvaid]

I'll be attending the amazing Gurobi: Optimization 201 for Data Scientists - On-Demand Training. Let me know if you're planning to attend so that we can say Hi! 👋
Or register now and join me at the event! https://t.co/NDPIU9LReA

via #Whova event app https://t.co/NDPIU9LReA

[eigen @taruvaid]

@gunsnrosesgirl3 24

[eigen @taruvaid]

https://t.co/hO58iTZrWv

[eigen @taruvaid]

https://t.co/3JyRx52a2D

[eigen @taruvaid]

https://t.co/oSykgI0nY5

[eigen @taruvaid]

@PhysInHistory Invented

[Prof. Feynman @ProfFeynman]

"In the sciences, the authority of thousands of opinions is not worth as much as one tiny spark of reason in an individual man."

— Galileo Galilei

[eigen @taruvaid]

#ClarkdietrichCares

Work responsibly Volunteer passionately!!