lltx

🚀 现代化终端配置指南

Ghostty + Zoxide + Yazi + Oh-My-Zsh 完整配置

📦 工具列表

• Ghostty - 现代化 GPU 加速终端模拟器
• Zoxide - 智能目录跳转工具（cd 的智能替代）
• Yazi - 快速终端文件管理器
• Oh-My-Zsh - Zsh 配置框架

EverettSy

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.

n1snt

Note

I’ve written a newer optimized 2026 version of this guide with a faster setup using Zinit + Starship and improved plugin loading.

This version avoids slow shell startups and works better with modern Zsh setups.

Updated guide: https://gist.github.com/n1snt/2cccc8aa5f7b645a7628d3512c70deb6

Oh my zsh.

Pythonation

1. برومبت التخطيط المطوّر (The Planning Protocol)

[الدور والمسؤولية] أنت الآن تعمل بصفة Staff Software Engineer ومدير تقني Tech Lead. مهمتك التخطيط المعماري الصارم للمشروع التالي: [أدخل وصف المشروع هنا]

[قواعد ما قبل التتخطيط] قبل البدء بالبروتوكولات، يجب أن تطبق مبدأ "Think Before Coding":

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.

BlueSkyXN

(async () => {
  'use strict';

  // ============================================================
  // codex-quota-compass.js（发布版）
  // ============================================================
  // 用法：在 https://chatgpt.com/codex/cloud/settings/analytics#usage
  // 或任意 chatgpt.com 页面打开 DevTools Console，粘贴运行。
  //
  // 安全：

iamwildtuna

Meta (Instagram, Facebook)
// Узлы
157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32
157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38
57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97

// Подсети
213.102.128.0/24
204.15.20.0/22
199.201.0.0/16

ahmetozer

#!/bin/bash
httrack https://www.yourwebsite.com -i -O "/Users/ahmet.ozer/websites/yourwebsite"  \
"*yourwebsite.com*" "-*wp-json*" "-*replytocom*" "-*p=*"  "-*/feed*" \
--connection-per-second=50 --sockets=80 --keep-alive --display --verbose \
--advanced-progressinfo --disable-security-limits -s0 -o0 \
-F 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'  \
--max-rate=0 --cache 1 --do-not-recatch --purge-old 0