ayanonagon

#!/usr/bin/env xcrun swift -F Carthage/Build/Mac

import Foundation
import Markingbird

protocol Streamable {
    var title: String { get }
    var body: String { get }
}

h4rithd

import sqlite3
import base64
import sys

if len(sys.argv) != 2:
    print("Usage: python3 gitea3hashcat.py <gitea.db>")
    sys.exit(1)

try:
    con = sqlite3.connect(sys.argv[1])

mycodeschool

/* Doubly Linked List implementation */
#include<stdio.h>
#include<stdlib.h>

struct Node  {
	int data;
	struct Node* next;
	struct Node* prev;
};

kalomaze

Simple Llama + SillyTavern Setup Guide

This guide is meant for Windows users who wish to run Facebook's Llama AI language model on their own PC locally. Our focus will be on character chats, reminiscent of platforms like character.ai / c.ai, using Llama architecture models. Most recently, in late 2023 and early 2024, Mistral AI has released high quality models that are based of the Llama architecture, and will work in the same way if you choose to use them.

• Requirements

  • Windows operating system (may make Mac version of the guide later)
  • GPU with at least a few gigabytes of VRAM (NVIDIA graphics cards recommended)

• Sufficient regular RAM for a model (system memory)

ocoyawale

This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

toupaiwang

// SPDX-License-Identifier: MIT

// Dev: @toupaiwang(X/Twitter)
pragma solidity ^0.8.20;

import "@openzeppelin/contracts/token/ERC20/ERC20.sol"; 
import "@openzeppelin/contracts/access/Ownable.sol";
import "@openzeppelin/contracts/security/ReentrancyGuard.sol";
import "@uniswap/v2-core/contracts/interfaces/IUniswapV2Pair.sol";
import "@openzeppelin/contracts/token/ERC20/extensions/IERC20Metadata.sol";

notlin4

教學用電子書與相關工具免登入教學

使用本指令碼即表示你同意《免責聲明》。

免責聲明

請勿將本指令碼作為抄答案、侵權等惡意用途，使用本指令碼，請「自行承擔」所有後果與風險。

簡介

本指令碼用於繞過臺灣電子書與教學工具的前端驗證，達成不需要教師帳號即可使用。

minhhieutruong0705

Guide to build OpenCV from source with GPU support (CUDA and cuDNN)

Feb 22nd, 2022

System specification

• Operating system: Ubuntu 20.04 x84_64 (64-bit)
• Architecture: amd64
• GPU: NVIDIA GeForce RTX 3090
• Python 3.8

Install dependencies and recommeneded packages

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

34306

import os
import re

print("=== Auto Patch Fortnite ARM64 ===")
print("Method by @khanhduytran0, script patch by @34306")

input_path = input("Please paste the path to Fortnite binary (FortniteClient-IOS-Shipping): ").strip()
output_default = os.path.expanduser('~/Desktop/FortniteClient-IOS-Shipping')
output_path = input(f"Please paste the path for the patched Fortnite binary [default {output_default}]: ").strip()