MuTLY

# this file contains keys needed for decryption of disc file system data (WUD/WUX)
# 1 key per line, any text after a '#' character is considered a comment
# the emulator will automatically pick the right key
541b9889519b27d363cd21604b97c67a # example key (can be deleted)
219FCB908520E9912D57444DFC136C23 # The Legend of Zelda: Breath of the Wild

566FCA3A88AA9255456635EDE688FD0B # The Legend of Zelda: Breath of the Wild EU

082fa2981e004defac03524cc685f693 # The Legend of Zelda: Breath of the Wild JPN

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.

dideler

A formula that lays out, on a page-by-page basis, exactly what should happen when, in a screenplay.
Each page is roughly equivalent to a minute of screen time.
Page counts don’t need to be followed strictly, but it’s important to get the proportions fairly close.

Opening image (p. 1)

Sets the tone for the story and suggests the protagonist’s primary problem.

Theme is stated (p. 5)

A question or statement, usually made to the protagonist, indicating the story’s main thematic idea.

Prottoy2938

function makeId(length) {
  let result = "";
  const characters =
    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
  const charactersLength = characters.length;
  for (let i = 0; i < length; i++) {
    result += characters.charAt(Math.floor(Math.random() * charactersLength));
  }
  return result;
}

merlox

pragma solidity 0.6.2;
pragma experimental ABIEncoderV2;

import 'https://github.com/provable-things/ethereum-api/blob/master/provableAPI_0.6.sol';

contract Insurance is usingProvable {
    struct Policy {
        uint256 id;
        uint256 price;
        string url;

merlox

Steps to earn money by closing expired or under-collateralized loans.

1. Clone the repository https://github.com/merlox/cdl-loans-expirer by executing this command:

  `git clone https://github.com/merlox/cdl-loans-expirer`
  
2. Install dependencies with `yarn install` or `npm install` note that you must have node.js installed https://nodejs.org/en/

3. Then create a file inside the project folder called `.secret` and paste your mnemonic phrase inside. 
This will be used to give you the rewards to your address.

merlox

// This is a basic uniswap frontrunning MEV bot
// Made by Merunas follow me on youtube to see how to use it and edit it: https://www.youtube.com/channel/UCJInIwgW1duAEnMHHxDK7XQ

// 1. Setup ethers, required variables, contracts and start function
const { Wallet, ethers } = require('ethers')
const { FlashbotsBundleProvider, FlashbotsBundleResolution } = require('@flashbots/ethers-provider-bundle')

// 1.1 Setup ABIs and Bytecodes
const UniswapAbi = [{"inputs":[{"internalType":"address","name":"_factory","type":"address"},{"internalType":"address","name":"_WETH","type":"address"}],"stateMutability":"nonpayable","type":"constructor"},{"inputs":[],"name":"WETH","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"tokenA","type":"address"},{"internalType":"address","name":"tokenB","type":"address"},{"internalType":"uint256","name":"amountADesired","type":"uint256"},{"internalType":"uint256","name":"amountBDesire

pourmand1376

Link to source

Intro

I assembled a super quick cheatsheet of the most common Pandas, Numpy and Python tasks I tend to do. Let me know if I missed anything important in the comments below!

If you like this kernel, please give it an upvote. Thank you! :)

Table of Contents

1  Data Structures1.1  Lists[]1.1.1  List Comprehensions - (do.. for)

straker

ICE Out; Abolish ICE

Basic Pong HTML and JavaScript Game

This is a basic implementation of the Atari Pong game, but it's missing a few things intentionally and they're left as further exploration for the reader.

Further Exploration