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import pytesseract
import sys
import argparse
try:
import Image
except ImportError:
from PIL import Image
from subprocess import check_output
@GeorgDangl
GeorgDangl / copySQLiteDlls.ps1
Created June 29, 2017 10:25
Using Microsoft.EntityFrameworkCore.Sqlite in a full .Net framework class library
$SQLitePackages = Join-Path -Path $env:USERPROFILE -ChildPath "\.nuget\packages\SQLite"
$latestSQLite3PackageX64 = Join-Path -Path ((Get-ChildItem -Path $SQLitePackages | Sort-Object Fullname -Descending)[0].FullName) -ChildPath "runtimes\win7-x64\native\sqlite3.dll"
$latestSQLite3PackageX86 = Join-Path -Path ((Get-ChildItem -Path $SQLitePackages | Sort-Object Fullname -Descending)[0].FullName) -ChildPath "runtimes\win7-x86\native\sqlite3.dll"
if (!(Test-Path "$PSScriptRoot\Dependencies")){
New-Item -ItemType Directory -Path "$PSScriptRoot\Dependencies"
}
if (!(Test-Path "$PSScriptRoot\Dependencies\x86")){
New-Item -ItemType Directory -Path "$PSScriptRoot\Dependencies\x86"
}
if (!(Test-Path "$PSScriptRoot\Dependencies\x64")){
@ChaseIngebritson
ChaseIngebritson / webpToJimp.js
Last active September 24, 2025 02:54
Convert a .webp image to be readable by Jimp
// This is a temporary fix until Jimp implements support for webp
// https://github.com/oliver-moran/jimp/issues/144
// const img = await webpToJimp('https://test.com/img.webp', './tmp')
import fs from 'fs'
import axios from 'axios'
import jimp from 'jimp'
import webp from 'webp-converter'

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.