Anh Trinh ptgamr

ON APP INIT

User has not login to Chrome: no server communication needed. only store data to local storage.
When user login to Chrome (profile_id is defined)

ON APP INIT

check if user is in the database

Tether is a great library for positioning stuff (tooltips, modals, hints, etc) in your web app.

But, as I use React, it was pretty problematic for me, as Tether mutates the DOM and React breaks miserably when it sees mutated DOM. The solution is to have the tethered element outside the part of the DOM tree which is controlled by React (in this case, I use document.body).

That's why I created 2 helpers to use Tether with React.

The first one, TetheredElement is a plain JS helper to create a new element, attach it to some other one via Tether, and populate it with some React component.

The second one, TetherTarget is a React component and it uses TetheredElement to integrate it further with React, so that you can attach components to each other with Tether, without leaving the cozy React/JSX world and worrying about manual DOM operations. Just write:

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

Safest way to clean up boot partition - Ubuntu 14.04LTS-x64, Ubuntu 16.04LTS-x64

Reference

Case I: if /boot is not 100% full and apt is working

1. Check the current kernel version

$ uname -r

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.

	javascript: (function() {

	var root = $(document.getElementsByTagName('html'));
	var watchers = [];
	var attributes = [];
	var attributes_with_values = [];
	var elements = [];
	var elements_per_attr = [];
	var scopes = [];

	import Ember from 'ember';

	export default Ember.Component.extend({
	tagName: 'button',
	click: function() {
	// In Ember 2.0, actions are just regular functions passed as
	// parameters to components. You will be able to invoke them
	// by just calling them, like this:
	var action = this.get('action');
	if (action) {

	function mapValues(obj, fn) {
	return Object.keys(obj).reduce((result, key) => {
	result[key] = fn(obj[key], key);
	return result;
	}, {});
	}

	function pick(obj, fn) {
	return Object.keys(obj).reduce((result, key) => {
	if (fn(obj[key])) {