gasparnagy

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Net;
using System.Xml;
using System.Xml.Linq;
using DotNetOpenAuth.AspNet;
using DotNetOpenAuth.AspNet.Clients;
using DotNetOpenAuth.Messaging;

k0emt

<cities>
	<state>
		<name>Alabama</name>
		<city>Abbeville</city>
		<number>1</number>
	</state>
	<state>
		<name>Alabama</name>
		<city>Adamsville</city>
		<number>1</number>

wschwarz

function process-XSLT
{param([string]$a)
$xsl = "C:\path_to_xslt\CleanUp.xslt"
$inputstream = new-object System.IO.MemoryStream
$xmlvar = new-object System.IO.StreamWriter($inputstream)
$xmlvar.Write("$a")
$xmlvar.Flush()
$inputstream.position = 0
$xml = new-object System.Xml.XmlTextReader($inputstream)

nicolewhite

Identifying Arbitrage Opportunities with Graphs

---

Introduction

alirobe

### 
###
### UPDATE: For Win 11, I recommend using this tool in place of this script: 
### https://christitus.com/windows-tool/
### https://github.com/ChrisTitusTech/winutil
### https://www.youtube.com/watch?v=6UQZ5oQg8XA
### iwr -useb https://christitus.com/win | iex
###
### OR take a look at 
### https://github.com/HotCakeX/Harden-Windows-Security

mistic100

// 1. Open the browser developper console on the network tab
// 2. Start the video
// 3. In the dev tab, locate the load of the "master.json" file, copy its full URL
// 4. Run: node vimeo-downloader.js "<URL>"
// 5. Combine the m4v and m4a files with mkvmerge

const fs = require('fs');
const url = require('url');
const https = require('https');

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.