fnky

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

• Ctrl-Key: ^[
• Octal: \033
• Unicode: \u001b
• Hexadecimal: \x1B
• Decimal: 27

sindresorhus

Pure ESM package

The package that linked you here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

1. Use ESM yourself. (preferred)
   Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
2. If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
3. Stay on the existing version of the package until you can move to ESM.

melmsie

import * as Constants from './constants';

const randomInArray = <T>(arr: readonly T[]): T =>
  arr[Math.floor(Math.random() * arr.length)];

export interface Card {
  suit: typeof Constants.SUITS[number];
  face: typeof Constants.FACES[number];
  baseValue: number;
};

j1o1h1n

from __future__ import annotations

import string
import code
import sys
import io
from typing import Callable

from textual.app import App
from textual.widgets import Header, ScrollView

Daeraxa

Default language comparison

Most popular (https://pypl.github.io/PYPL.html & https://www.tiobe.com/tiobe-index/)

Language	Atom/Pulsar	VSCode	Brackets	Sublime	Lapce	Geany	Xed	Kate	Helix
Python	Y	Y	Y	Y	Y	Y	Y	Y	Y
Java	Y	Y	Y	Y	Y	Y	Y	Y	Y
JavaScript	Y	Y	Y	Y	Y	Y	Y	Y	Y
C#	Y	Y	Y	Y	Y	Y	Y	Y	Y
C	Y	Y	Y	Y	Y	Y	Y	Y	Y

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

roman01la

#!/usr/bin/env bash
set -euo pipefail

# patch-claude-code.sh — Rebalance Claude Code prompts to fix corner-cutting behavior
#
# What this does:
#   Patches the npm-installed @anthropic-ai/claude-code cli.js to rebalance
#   system prompt instructions that cause the model to cut corners, simplify
#   excessively, and defer complicated work.
#

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.