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<html>
<head>
<title>Checkbox</title>
<style>
input[type=checkbox] {
display:none;
}
input[type=checkbox] + label
{
@Serhioromano
Serhioromano / backup.php
Last active February 20, 2021 10:43
AWS EC2 automatic volume daily snapshot.
#!/usr/bin/php -q
<?php
date_default_timezone_set('UCT');
$dryrun = FALSE;
$interval = '24 hours';
$keep_for = '10 Days';
$volumes = array('vol-9677d14a');
$api_key = 'AKI****EYUA';
$api_secret = 'IzMni****ct';
@lukas-h
lukas-h / license-badges.md
Last active August 2, 2026 08:59
Markdown License Badges for your Project

Markdown License badges

Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.

Notes

  • The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

@fongandrew
fongandrew / react-bind.md
Last active January 14, 2024 16:02
Explaining why we bind things in React

Start With This

Before getting to React, it's helpful to know what this does generally in Javascript. Take the following snippet of code. It's written in ES6 but the principles for this predate ES6.

class Dog {
  constructor() {
@Kievbuy
Kievbuy / text
Last active November 18, 2023 17:16
You have to kill running process
In terminal:
`lsof -wni tcp:3000`
It show all running processes
COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME
ruby 9666 dima 10u IPv4 65756 0t0 TCP 127.0.0.1:3000 (LISTEN)
@flassinot
flassinot / PY0101EN-5.3_Requests_HTTP.ipynb
Created February 10, 2021 15:35
Created on Skills Network Labs
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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.