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.

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

markblundeberg

---

Advisory: secret size attack on cross-chain hash lock smart contracts

Dr. Mark B Lundeberg, 2018 Feb 15 bitcoincash:qqy9myvyt7qffgye5a2mn2vn8ry95qm6asy40ptgx2

This security advisory notes a vulnerability in the common construction of cross-chain smart contracts (contracts between distinct cryptocurrencies) through hash locking. I focus on the primary use case in [atomic

awjuliani

awjuliani

frozeman

// Open the wallet console: Menu -> Develop -> Toggle console ...
// Run the following script

_.each(Wallets.find().fetch(), function(item){
    if(item.address)
        Wallets.update(item._id, {$set: {address: item.address.toLowerCase()}}); 
});
_.each(CustomContracts.find().fetch(), function(item){
    if(item.address)
        CustomContracts.update(item._id, {$set: {address: item.address.toLowerCase()}}); 

nunofernandes-plight

// Open the wallet console: Menu -> Develop -> Toggle console ...
// Run the following script

_.each(Wallets.find().fetch(), function(item){
    if(item.address)
        Wallets.update(item._id, {$set: {address: item.address.toLowerCase()}}); 
});
_.each(CustomContracts.find().fetch(), function(item){
    if(item.address)
        CustomContracts.update(item._id, {$set: {address: item.address.toLowerCase()}}); 

mages

library(mnormt)
mycols <- topo.colors(100,0.5)
xhat <- c(0.2, -0.2)
Sigma <- matrix(c(0.4, 0.3, 
                  0.3, 0.45), ncol=2)
x1 <- seq(-2, 4,length=151)
x2 <- seq(-4, 2,length=151)
f <- function(x1,x2, mean=xhat, varcov=Sigma) 
  dmnorm(cbind(x1,x2), mean,varcov)
z <- outer(x1,x2, f)

awjuliani