lyrl

Title

<Additional information about your API call. Try to use verbs that match both request type (fetching vs modifying) and plurality (one vs multiple).>

URL

<The URL Structure (path only, no root url)>
Method:

A complete list of books, articles, blog posts, videos and neat pages that support Data Fundamentals (H), organised by Unit.

Formatting

If the resource is available online (legally) I have included a link to it. Each entry has symbols following it.

⨕⨕⨕ indicates difficulty/depth, from ⨕ (easy to pick up intro, no background required) through ⨕⨕⨕⨕⨕ (graduate level textbook, maths heavy, expect equations)
⭐ indicates a particularly recommended resource; 🌟 is a very strongly recommended resource and you should look at it.

小米9 刷欧洲版 MIUI 固件及适当的本土化修复

本教程不保证它的权威性和正确性，也不对任何实践中的错误、数据丢失等问题负责！
本教程平台为 macOS, 其他系统请酌情参考

更新说明

@ 2020/8/29

添加"介绍"
更新 TWRP 信息

JAVADOC CHEATSHEET

Introduction

The major important thing is the documentation has to be implementation independent and specification concise. Dependencies where ever necessary are allowed to be specified.
Also it is allows HTML tags to be used in between the documentation comments. Pretty much all tags are self explanatory.

Meta Annotations
@author Ex: @author Jane Doe
@version Ex: @version v1.0-alpha

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

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.

	#!/bin/bash

	if [ $# -eq 0 ]; then
	echo "Usage: ./pushover <message> [title]"
	exit
	fi

	MESSAGE=$1
	TITLE=$2