MajidRaimi

#!/usr/bin/env node

const path = require('path');
const { execSync } = require('child_process');

function getLoggedInEmail() {
  try {
    const config = require(path.join(process.env.HOME, '.claude.json'));
    return config.oauthAccount?.emailAddress || null;
  } catch {

aarondfrancis

Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.

This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.

You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.

1. Establish the coverage contract

Inspect the repository and inventory every identifiable subsystem.

fanpei91

#!/bin/bash

if [ "$1" = "--prepare-env" ]; then
  DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
  mkdir -p ~/Scripts

  echo "Copying the script to $HOME/Scripts"
  cp -rf $DIR/runme.sh  ~/Scripts/jetbrains-reset.sh
  chmod +x ~/Scripts/jetbrains-reset.sh

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.

wilinz

edge tts

获取语音列表：

https://speech.platform.bing.com/consumer/speech/synthesize/readaloud/voices/list?trustedclienttoken=6A5AA1D4EAFF4E9FB37E23D68491D6F4

tts接口

wss://speech.platform.bing.com/consumer/speech/synthesize/readaloud/edge/v1?TrustedClientToken=6A5AA1D4EAFF4E9FB37E23D68491D6F4&Sec-MS-GEC=C14242292B2241ECB17DD93BD17E481FADED759D70DCE83CA526064D9E90420C&Sec-MS-GEC-Version=1-114.0.1823.67&ConnectionId=373cb9fe3c926de352de8ad4c800b24c HTTP/1.1
Host: speech.platform.bing.com

jinjier

使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：

• JavDB TOP250
• JavDB 有码 TOP250
• JavDB 无码 TOP250
• JavDB 欧美 TOP250
• JavDB FC2 TOP250

及分年数据（存在部分重复影片，原始数据的问题）：

Wang200935

Bug Bounty Course 2024

目錄

本筆記將Ryan John的Bug Bounty Course 2024影片內容，結合最新的學術研究和實務指南，製作成一份完整的技術學習手冊。每個步驟都會詳細說明，讓完全沒有基礎的初學者也能理解並實際操作。

第一部分：Bug Bounty基礎概念與環境準備

1.1 什麼是Bug Bounty？

Wang200935

/**
 * auto-refactor.js — AST + LLM 自動化重構核心腳本
 *
 * 用法：
 *   node auto-refactor.js <檔案或目錄> [--dry-run]
 *
 * 流程：
 *   1. 用 babel-parser 將程式碼解析成 AST
 *   2. 用 visitor pattern 萃取「純邏輯」節點（無副作用）
 *   3. 純邏輯片段送給 LLM 做局部重寫（保持輸出入一致）