adam-james-v

Emacs Config

;;

This is a ‘minimum viable config’ built for the purpose of literate programming with Clojure / Clojurescript. It uses MELPA to download and install a few packages that I consider necessary for a good Clojure dev. experience, though that’s of course only my opinion. I use CIDER, a robust and popular REPL tool. It could arguably be substituted for inf-clojure, but I haven’t tried that myself.

This config does assume that you already have emacs installed and that you have at least a cursory understanding of how to navigate and use it. Or, at the very least know a few keywords to search as you try learn things. Emacs can be a daunting tool (I don’t even know most of it myself yet, honestly), but you can do the most critical things without too much difficulty and a bit of patience.

soumith

-- Torch Android demo script
-- Script: main.lua
-- Copyright (C) 2013 Soumith Chintala

require 'torch'
require 'cunn'
require 'nnx'
require 'dok'
require 'image'

fffaraz

• Akamai
• Amazon Cloudfront
• Bitgravity
• Cachefly
• CDN77
• CDNetworks
• CDNify
• ChinaCache
• ChinaNetCenter

antonioCoco

// TcbElevation - Authors: @splinter_code and @decoder_it

#define SECURITY_WIN32
#include <windows.h>
#include <sspi.h>
#include <stdio.h>

#pragma comment(lib, "Secur32.lib")

void EnableTcbPrivilege(BOOL enforceCheck);

b4tman

{
    "registry-mirrors" : [
        "https://cr.yandex/mirror",
        "https://dockerhub.timeweb.cloud",
        "https://huecker.io",
        "https://noohub.ru",
        "https://dcr-px.ru",
        "https://mirror.gcr.io",
        "https://quay.io",
        "https://registry.access.redhat.com",

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.

aamiaa

Caution

As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.

Some users have received the following system message:

There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.

Complete Recent Discord Quest