npm install -g @anthropic-ai/claude-codeDiscover gists
| #include "CertInjector.h" | |
| #include "MinHook.h" | |
| #include <string> | |
| #include <unordered_set> | |
| #define COUNT_OF(arr) (sizeof(arr) / sizeof(*arr)) | |
| // #define PRINT_DEBUG |
| // SPDX-License-Identifier: MIT | |
| pragma solidity ^0.8.0; | |
| import "@openzeppelin/contracts/token/ERC721/ERC721.sol"; | |
| import "@openzeppelin/contracts/access/Ownable.sol"; | |
| contract MockRWAToken is ERC721, Ownable { | |
| uint256 private _nextTokenId; | |
| constructor( |
| <!doctype html> | |
| <html lang="fr"> | |
| <head> | |
| <meta charset="utf-8" /> | |
| <meta name="viewport" content="width=device-width,initial-scale=1" /> | |
| <meta name="color-scheme" content="light dark" /> | |
| <link | |
| rel="icon" | |
| href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 64 64%22><circle cx=%2232%22 cy=%2232%22 r=%2230%22 fill=%22%235b7fe5%22/><circle cx=%2224%22 cy=%2228%22 r=%225%22/><circle cx=%2240%22 cy=%2228%22 r=%225%22/></svg>" | |
| /> |
| #Requires -RunAsAdministrator | |
| <# | |
| .SYNOPSIS | |
| Network performance optimization script for Windows 10/11. | |
| .DESCRIPTION | |
| Applies TCP and network stack tweaks targeting lower latency. | |
| Skips settings that are read-only or no-ops on Windows 10/11 consumer editions. | |
| Run in an elevated PowerShell session. |
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.
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.
| const http = require('http') | |
| const express = require('express') | |
| const bodyParser = require('body-parser') | |
| const morgan = require('morgan') | |
| const cors = require('cors') | |
| const app = module.exports = express() | |
| const server = http.createServer(app) | |
| const port = parseInt(process.env.PORT || 3000) | |
| app.use(bodyParser.json()) |