alexanderameye

using System.Collections.Generic;
using System.IO;
using UnityEditor;
using UnityEditor.Overlays;
using UnityEditor.SceneManagement;
using UnityEditor.Toolbars;
using UnityEngine;
using UnityEngine.SceneManagement;
using UnityEngine.UIElements;

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.

jph00

Solveit Reference

What is Solveit?

Solveit is a "Dialog Engineering" web application for interactive development. Unlike ChatGPT (pure chat) or Jupyter (pure code), Solveit combines three message types in one workspace: code execution, markdown notes, and AI prompts. Users build solutions incrementally—writing a few lines, understanding them, then continuing—rather than generating large code blocks.

The AI sees the full dialog context (code, outputs, notes, prompts) when responding -- but only those ABOVE the current message. Users can edit any message at any time, including AI responses—the dialog is a living document, not an append-only log.

The dialog is a running ipykernel instance. A "dialog" is like a "Jupyter notebook", and uses a compatible ipynb file format, but provides a superset of functionality (in particular, "prompt messages"). A "message" is like a "Jupyter cell", with additional attributes stored as ipynb cell metadata. Most standard jupyter functionality is supported (including cell

jonlidgard

Zed Editor set-up to do ESP32 Embedded Debugging with the ESP-IDF Framework (macOS).

This gist refers to ESP-IDF v5.5.2 - the current stable version as of Dec '25, paths will obviously change slightly in future versions.

The debugging toolchain works as follows:

• zed talks to gdb via it's Debug Adapter Protocol (dap) interface. This is configured in .zed/debug.json residing in your project directory. It's crucial to use a version of gdb built with the python extension in order for the dap interface to work - more on this later.
• gdb talks to openocd via it's built-in gdb server's tcp port (usually 3333).

tuxfight3r

Bash Shortcuts

Moving

command	description
ctrl + a	Goto BEGINNING of command line

Domiii

/**
 * This script types for you automatically on www.typingclub.com:
 * 1. Open the website
 * 2. Blaze past the tutorials
 * 3. Go into a level
 * 4. Open Console
 * 5. Paste the script and press ENTER
 */

// NOTE: When delay (in ms between two strokes) is too low, the site might bug out and the result page will not be shown

upsilun

"\033[0m" // Reset all text attributes to default
"\033[1m" // Bold on
"\033[2m" // Faint off
"\033[3m" // Italic on
"\033[4m" // Underline on
"\033[5m" // Slow blink on
"\033[6m" // Rapid blink on
"\033[7m" // Reverse video on
"\033[8m" // Conceal on
"\033[9m" // Crossed-out on

hensg

Ghostty Keyboard Shortcuts

Default keyboard shortcuts for Ghostty terminal emulator. Platform-specific differences are noted where applicable.

Window Management

Action	Windows/Linux	macOS
New window	Ctrl+Shift+N	Cmd+N
Close window	Alt+F4	Cmd+Shift+W

0wwafa

Go to https://claude.ai/chat/new (or any other chat you had in the past).

Then run this code:

function printClaude() {   // (C) 2024 by ZeroWw. If you use this code, just give me some credit.
    const centralPart = document.querySelector('.flex-1.flex.flex-col.px-4.max-w-3xl.mx-auto.w-full.pt-1');

    if (centralPart) {
        // Expand all "show more" buttons
        document.querySelectorAll('button').forEach(button => {

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp