| import json | |
| import os | |
| import subprocess | |
| import sys | |
| import time | |
| from pathlib import Path | |
| from typing import Dict, List, Any | |
| import json | |
| import urllib.request | |
| import urllib.error |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title></title> | |
| <meta charset="utf-8" /> | |
| <script src="Scripts/jquery-1.9.1.min.js"></script> | |
| <link href="Content/bootstrap.min.css" rel="stylesheet" /> | |
| <script src="Scripts/isRockFx.js"></script> | |
| <script> | |
| $(function () { |
Manual hotfix for:
(works on my machine, use at your discretion)
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.
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.
Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations on LLM coding pitfalls.
These rules apply to every task in this project unless explicitly overridden. Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.
Don't assume. Don't hide confusion. Surface tradeoffs.
| from Foundation import NSKeyedUnarchiver | |
| from struct import unpack | |
| # This entire function is black magic of the highest order and I'll blog about it later | |
| def extract_share(bookmark_data): | |
| content_offset, = unpack('I', bookmark_data[12:16]) | |
| first_TOC, = unpack('I', bookmark_data[content_offset:content_offset+4]) | |
| first_TOC += content_offset | |
| TOC_len, rec_type, level, next_TOC, record_count = unpack('IIIII', bookmark_data[first_TOC:first_TOC+20]) | |
| TOC_cursor = first_TOC + 20 |
| import Data.List | |
| data Letter = Found (Char) | NotFound (Char) | Dash | None deriving Eq | |
| type LetterSequence = [Letter] | |
| data Hangman = Hangman { | |
| word :: LetterSequence, | |
| tries :: Int, | |
| won :: Bool |
