Artem Salyutin

Human middleware

[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.

[Влад @vlad_dfe]

Сперва вы ищете джедая разработки, супергероя менеджмента, фею копирайтинга и волшебника дизайна, а потом удивляетесь, откуда у вас в команде столько сказочных долбоёбов.

[Artem Salyutin @Salyutin]

@falsefased1 как бы она интересно отреагировала на «дырчик»

[Artem Salyutin @Salyutin]

Всегда предпочитал «друзей» курильщика. Курильщика крэка. #FriendsReunion #SunnyFXX

[Artem Salyutin @Salyutin]

Листаю ленту и вижу пост с таким началом: "У нас на входе весы. Их можно калибровать".

Первая мысль — сейчас будет что-то интересное про ML.

Продолжаю читать, но уже на следующем предложении понимаю, что пост про обычный фитнес-центр. #MachineLearning

[Artem Salyutin @Salyutin]

This trained on oriental rags stylegan reminds me of Faig Ahmed

[Zaid زيد @zaidalyafeai]

Fast forward

[Artem Salyutin @Salyutin]

"the pretrained head pose estimator ensures that head poses and angles etc. in the generated images are accurate and visually acceptable" yet creepy

[𓁼 Matt Bailey 𓁼 @BAILEYDRAWS]

Good Morning

[Tom Warren @tomwarren]

Apple exposing all the ways Facebook tracks you with it iOS app is really quite something

[Artem Salyutin @Salyutin]

How come no one has sued Gaspar Noe for that Lux Aeterna ending?

[Artem Salyutin @Salyutin]

I hope someday we will get similar explanations for ML key terms

[VWO @VWO]

1/ 🚀 Winner Announcement 🚀

Another test result of the Humans vs. AI copywriting contest is out and the score now stands at 0-2, with AI registering its 2nd win in a row.

The AI written copy led to a 15.77% increase in conversions with 100% statistical significance.

[Artem Salyutin @Salyutin]

This year in a nutshell. #AI #MachineLearning

[Artem Salyutin @Salyutin]

Generated by GPT-3 is the new Get Out of Jail Free card
#AI #MachineLearning

[Artem Salyutin @Salyutin]

What is the point of laying in a comfortable position if you can't fall asleep in it?