stanislaw

Open source requirements management tools

• BASIL

A tool developed to support Software Specification analysis and Testable Requirements definition.

• doorstop

Requirements management using version control.

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.

steelywing

// Work for RedMi 2100 firmware 2.0.23
// http://cdn.cnbj1.fds.api.mi-img.com/xiaoqiang/rom/rm2100/miwifi_rm2100_all_fb720_2.0.23.bin

function getSTOK() {
    let match = location.href.match(/;stok=(.*?)\//);
    if (!match) {
        return null;
    }
    return match[1];
}

tmkhiem

Student Challenge Guide

This guide is for students solving the TeachCrypto API challenges.

It answers two things:

1. How to fetch a challenge and submit an answer
2. What cryptographic operations the server expects for each challenge family

1. Common Rules

rajkosto

#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#include <Shlobj.h>

#include <string.h>
#include <stdio.h>
#include <stdlib.h>
#include <errno.h>

bool replaceAllInFile(const TCHAR* filePath) 

ckahle33

# gem install mechanize
require 'mechanize'

class Download

  def initialize(site, email, pass)
    @options = {site: site, email: email, pass: pass}
  end

  def call

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