spideynolove

Claude Code Multi-Provider Setup Guide

Overview

Claude Code is a powerful command-line interface that allows developers to interact with Anthropic's Claude AI models for coding assistance. This guide provides two battle-tested methods to configure Claude Code to work with multiple LLM providers beyond just Anthropic, including models from DeepSeek, z.ai (GLM), Kimi, and OpenRouter.

This empowers you to switch between the best model for any given task without ever leaving your terminal. Two Approaches to Flexibility

• Shell Functions: A simple, lightweight, and robust method for switching providers before starting a session. Perfect for most use cases.

xuyishan8058

{
  "name": "yifan 精选源列表 (2026-10-19 实测可用)",
  "version": 2,
  "sources": [
    { "name": "暴风资源", "url": "https://bfzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "非凡资源", "url": "https://cj.ffzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "量子资源", "url": "https://cj.lziapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "无尽资源", "url": "https://api.wujinapi.me/api.php/provide/vod", "isAdult": false },
    { "name": "优酷资源", "url": "https://api.ukuapi88.com/api.php/provide/vod", "isAdult": false },
    { "name": "光速资源", "url": "https://api.guangsuapi.com/api.php/provide/vod", "isAdult": false },

yifanwang-yet

{
  "name": "yifan 精选源列表 (2026-10-13 实测可用)",
  "version": 2,
  "sources": [
    { "name": "暴风资源", "url": "https://bfzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "非凡资源", "url": "https://cj.ffzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "量子资源", "url": "https://cj.lziapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "无尽资源", "url": "https://api.wujinapi.me/api.php/provide/vod", "isAdult": false },
    { "name": "优酷资源", "url": "https://api.ukuapi88.com/api.php/provide/vod", "isAdult": false },
    { "name": "光速资源", "url": "https://api.guangsuapi.com/api.php/provide/vod", "isAdult": false },

soyprofesor

Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V

Product	Year	Version	Product Keys
Visual Studio 2022	2021	17.x	
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH

Visual Studio 2019	2019	16.x	

baihao1977

{
  "name": "精选源列表 (2026-10-13 实测可用)",
  "version": 2,
  "sources": [
    { "name": "暴风资源", "url": "https://bfzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "非凡资源", "url": "https://cj.ffzyapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "量子资源", "url": "https://cj.lziapi.com/api.php/provide/vod", "isAdult": false },
    { "name": "无尽资源", "url": "https://api.wujinapi.me/api.php/provide/vod", "isAdult": false },
    { "name": "优酷资源", "url": "https://api.ukuapi88.com/api.php/provide/vod", "isAdult": false },
    { "name": "光速资源", "url": "https://api.guangsuapi.com/api.php/provide/vod", "isAdult": false },

steinwaywhw

• Use curl to get the JSON response for the latest release
• Use grep to find the line containing file URL
• Use cut and tr to extract the URL
• Use wget to download it

curl -s https://api.github.com/repos/jgm/pandoc/releases/latest \
| grep "browser_download_url.*deb" \
| cut -d : -f 2,3 \
| tr -d \" \

yuelong

#coding=utf-8
import urllib2
import urllib
import re
from time import sleep

querywrd='blackpink'
cont = 1
file = open('H:\\test','w')
for x in range(20):#抓取前20页结果

karpathy

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.

CrashNosok

Everything Claude Code (ECC) — Полное руководство

Версия: 1.9.0 | Автор: Affaan Mustafa | Лицензия: MIT Репозиторий: https://github.com/affaan-m/everything-claude-code

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Содержание

1. Что это и зачем нужно