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Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

1️⃣ Docker daemon 配置代理（推荐）

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.

	import base64
	import json
	import requests

	REPO_URL = "https://api.github.com/repos/<USER>/<REPO>/contents/<PATH>/<TO>/<FILE>.json"
	TOKEN = "<YOUR PAT OR OAUTH TOKEN>"

	headers = {
	"Authorization": f"token {TOKEN}",
	"Accept": "application/vnd.github.v4+raw"

	"""
	Structlog example configuration with FastAPI.
	Features:
	- async bound logger
	- contextvars to log request-id and other meta data
	- custom format for default logging loggers and structlog loggers
	"""

	import asyncio
	import logging

	# author: codeskyblue 2025
	# https://github.com/openatx/uiautomator2
	# Example: how to manually handle popup-windows

	import random
	import time
	from typing import Optional, Sequence, Tuple

	import adbutils
	from loguru import logger

	wget --no-check-certificate --content-disposition https://github.com/joyent/node/tarball/v0.7.1
	# --no-check-cerftificate was necessary for me to have wget not puke about https
	curl -LJO https://github.com/joyent/node/tarball/v0.7.1