ghazi

founder @TrekRentals

[ghazi @binarandi]

senses never gratified
only swelling like a tide

[ghazi @binarandi]

longing for a run real bad

[Sam Stoffel @sam_stoffel]

Founder of lululemon on what he'd tell every 25 year old:

"I'd tell them that every person in the world is an individual with a different genetic makeup and a different upbringing and the way that you're thinking is so radically different than every other person in the world and incomparable that if you have an idea and you want to move forward with it, don't worry so much about the competition because nobody will be able to replicate you and the way you think about it."

[Pope Leo XIV @Pontifex]

#AI can be a valuable tool and, at the same time, it calls for a measured and vigilant approach. The speed and simplicity with which practical assistance can be accessed undoubtedly makes life easier. Yet they can also encourage excessive reliance and the search for ready-made answers, and weaken personal creativity and judgment. #MagnificaHumanitas

[Pope Leo XIV @Pontifex]

Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate or even simulate, but they do not understand what they produce, for they lack the affective, relational, and spiritual perspective through which human beings grow in wisdom. #MagnificaHumanitas

[Kevin Kwok @kevinakwok]

SpaceX is most enjoyable S1 read in a long time

Reads so easy like sci-fi or fiction

[ghazi @binarandi]

😳

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[ghazi @binarandi]

Might be one of the greatest LLM use cases so far

[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.

[ghazi @binarandi]

Google Fitbit Air just looks ... 😳

[ghazi @binarandi]

ᴍʏ ʙʀᴀɪɴ ʜᴜʀᴛ ʟɪᴋᴇ ᴀ ᴡᴀʀᴇʜᴏᴜꜱᴇ

Only Bowie could write something like this

[ghazi @binarandi]

Wingstop's founder was a vegetarian. He didn't even taste the wings when they developed the recipe; he only tasted the sauce. He eventually sold the company, as it grew fast, because he couldn't live with the guilt over how many chickens were being killed for his product.

[ghazi @binarandi]

Reminds me of the Hitchhiker's. When "the computer" gives you the ultimate answer, you end up wondering what the question even is.

[Gurwinder @G_S_Bhogal]

Picasso’s reply when asked for his thoughts on computers in 1964 is more relevant than ever:

[ghazi @binarandi]

Holy, hahaha

[FEAR GOD @FEARGODMINDSET]

Nietzsche, what a line

[ghazi @binarandi]

Chat, is this true

[John Arnold @johnarnold]

hahahahhaha

[ghazi @binarandi]

Nobody can, and nobody will

[Sam Altman @sama]

@BasedBeff you cannot outaccelerate me

[ghazi @binarandi]

To that we say amen

[Benji Taylor @benjitaylor]

Don’t shrink the vision to fit the doubt

[Nassim Nicholas Taleb @nntaleb]

@Jaffer22915438 The Roman army rule: action removes fear.

[kache @yacineMTB]

you can outsource your thinking
but you cannot outsource your understanding

[ghazi @binarandi]

Yooooo 😳 https://t.co/6EmVTkS30P

[ghazi @binarandi]

Why is cakalang usually served shredded?