A list of GitHub emoji markup, adapted from rxavier's Complete list of github markdown emoji markup, generated with a Grunt script for maintainability (see repository).
Additional original source material: http://unicode.org/emoji/charts/full-emoji-list.html
This table is available as a gist at https://gist.github.com/GerHobbelt/b9b87a2257ddd5251a45628d61385717 and as part of the build repo at https://github.com/GerHobbelt/emoji-list/blob/master/dist/emoji-list.md
Guide to install a LAMP system on on your archlinux system and serve php-based database applications.
LAMP stands for a Linux system with Apache (webserver), MariaDB (database) and PHP (programming language). In this guide we will also install PhpMyAdmin (database admin GUI) to easily manage the SQL tables.
The Apache HTTP Server is an open-source and free product of the Apache Software Foundation and one of the most widely used web servers on the Internet. In addition to factors such as performance, expandability, security, freedom from license costs and support from a very large community, its long-term availability for a wide variety of operating systems is one of the reasons for its widespread use; it is most frequently used as a LAMP system.
Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:
Up Arrow: Will show your last commandDown Arrow: Will show your next commandTab: Will auto-complete your commandCtrl + L: Will clear the screen
Panduan ini menjelaskan cara melakukan flash dan konfigurasi sistem operasi server Linux berkinerja tinggi (Armbian) pada TV box lawas di Indonesia. Metode khusus ini menggunakan teknik injeksi bootloader yang memaksa perangkat untuk booting melalui kartu SD, meskipun penyimpanan internal (eMMC) sudah mati, Bootloader bawaan rusak (corrupt), atau OS Android mengalami brick.
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.
| function getRotation() { | |
| //1竖屏 2横屏 | |
| importPackage(android.content); | |
| return context.getResources().getConfiguration().orientation; | |
| } | |
| function getScreenHW() { | |
| // console.log(getRotation()); | |
| // 正常的手机，高度，是大于宽度的。但是，device.width,device.height，函数，有时候是会获取错误的。 | |
| // 强行扭正。 |
