Mike-Schvedov

using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class PlayerMovement : MonoBehaviour
{
    public CharacterController controller;
 
    public float speed = 12f;
    public float gravity = -9.81f * 2;

Mike-Schvedov

using System.Collections;
using System.Collections.Generic;
using UnityEngine;

public class MouseMovement : MonoBehaviour
{
 
    public float mouseSensitivity = 100f;
 
    float xRotation = 0f;

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.

jetfir3

#!/usr/bin/env bash

# Download VMware Fusion for macOS without a Broadcom account.
#
# This script allows you to download various versions of VMware Fusion
# from Broadcom's Cloudflare CDN (versions 8.0.0 to 13.6.3)
# or from the archive.org VMware Workstation archive (versions 8.x.x+).
#
# Options:
#   -k: Keep the downloaded file compressed (Cloudflare only; ignored for archive.org).

bdsqqq

# meaningful contribution

Based on embarassing feedback on a bad PR for AXM-10598, and written from my beliefs; with only a little bit of copy pasting, because to be human is to copy from a shitload of places until it becomes your own. 

## A contribution is not code, it's proven working code

> "your job is to deliver code you have proven to work." — [simon willison](https://simonwillison.net/2025/Dec/18/code-proven-to-work/)

agent-generated code is cheap. anyone can prompt an LLM to produce a thousand-line patch. that's not valuable. what's valuable is contributing code that demonstrably works, has been tested, and doesn't shift burden to reviewers.

nicolas17

c: command
cc: commandContext
v: version
P: payload
N: bulkedPayload
fP: fanoutPayload
aP: additionalPayload
Pm: payloadMetadata
i: messageId
U: messageUUID

ry

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.