karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

koaning

import pyphen

import prodigy
from prodigy.components.loaders import JSONL
from prodigy.components.db import connect

hyphenator = pyphen.Pyphen(lang="en_US")

def construct_html(text):
    hyphend = hyphenator.inserted(text)

ericdfields

I've seen links around the web for a list of items and a single 'add to amazon cart' button.
I don't know how to do this more easily through an amazon-provided UI, but it seems that you can hack it together through URL params easy enough, like so:

https://www.amazon.com/gp/aws/cart/add.html?AssociateTag=your_tag&tag=your_tagQ&ASIN.1=B012NH05UW&Quantity.1=1&ASIN.2=B012M8LXQW&Quantity.2=1

* ASINs are the string after /dp/ in amazon URLs. (amazon.com/dp/string_is_here)
* Add as URL params w/ incrementing identifiers and quantity couplets (ASIN.1, Quantity.1, ASIN.2, Quantity.2…)

mcfrank

## this little script shows off the use of the pubmed API through bioconductor
## requires installing Biopython (using pip)
## also requires installing the DTD files for each of the Entrez API calls, 
## but the instructions for this are given when you run the script

## useful list of Entrez databases that can be queried through API
# pmc_pubmed	PubMed citations for these articles
# pmc_refs_pubmed	PubMed article citing PMC article
# pmc_pmc_cites	   PMC articles that given PMC article cites
# pmc_pmc_citedby	PMC article citing given PMC article

Moving-Electrons

#!/usr/local/bin/python2.7

import datetime
import time
import re
import os
from twython import Twython
import sys
import traceback
import httplib, urllib #used in the Pushover code