Skip to content

Instantly share code, notes, and snippets.

<table id="ga-table" class="dataTable" role="grid">
<thead>
<tr class="row-1 odd" role="row"><th class="column-1 sorting_disabled" rowspan="1" colspan="1" style="width: 80px;">Cookie Name</th><th class="column-2 sorting_disabled" rowspan="1" colspan="1" style="width: 73px;">Expiration Time</th><th class="column-3 sorting_disabled" rowspan="1" colspan="1" style="width: 223px;">Description</th><th class="column-4 sorting_disabled" rowspan="1" colspan="1" style="width: 118px;">Which MonsterInsights Users</th></tr>
</thead>
<tbody class="row-hover">
<tr class="row-2 even" role="row">
<td class="column-1">_ga</td><td class="column-2">2 years</td><td class="column-3">Used to distinguish users. Stores the ClientID, a unique and randomly generated string, so that the user can be associated on future visits, the date of first visit and version of GA cookie.</td><td class="column-4">All Users</td>
</tr><tr class="row-3 odd" role="row">
<td class="column-1">_gid</td><td class="column-2">24 hours</td><td class=

Panoglobe

The Project

A Globe visualization, displaying travel lines made from GPS coordinates on planet earth.

Think about the google data-globe but do-it-yourself. Display travel routes and link to content from another website. Because weaving a 2D-website and a 3D-context is a difficult task, for now there are just hyperlinks leading to other pages.

@jaygidwitz
jaygidwitz / hls.sh
Created July 3, 2025 05:19 — forked from stenuto/hls.sh
HLS ffmpeg script
#!/bin/bash
# Function to display usage information
usage() {
echo "Usage: $0 /path/to/input.mp4 [ /path/to/output_directory ]"
exit 1
}
# Check if at least one argument (input file) is provided
if [ $# -lt 1 ]; then
@jaygidwitz
jaygidwitz / llm-wiki.md
Created April 6, 2026 21:37 — forked from karpathy/llm-wiki.md
llm-wiki

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.