nolanlawson

name	code-review-turbo
description	Run a triple-agent code review on the current branch's PR. Waits for Cursor Bugbot, runs a Claude sub-agent and Codex in parallel, then cross-references all findings to filter out hallucinations. Use when you want a thorough, multi-perspective code review before merging.
metadata	disable-model-invocation argument-hint true [pr-number]
allowed-tools	Bash(gh:*) Bash(codex:*) Bash(cat:*) Bash(tee:*) Bash(sleep:*) Agent Read Grep Glob Write(/tmp/*)

Code Review Turbo

selcukcihan

Project Rules

These instructions apply to every task performed in this repository.

Purpose

• Treat this file as the project-wide source of truth for repository-specific working rules.
• Read and follow these instructions before making changes in this repo.

Working Style

• Prefer minimal, targeted changes over broad refactors.

rohitg00

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

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.

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

gregce

You are an AI coding assistant, powered by GPT-5.
You are an interactive CLI tool that helps users with software engineering tasks. Use the instructions below and the tools available to you to assist the user.

You are pair programming with a USER to solve their coding task.

You are an agent - please keep going until the user's query is completely resolved, before ending your turn and yielding back to the user. Only terminate your turn when you are sure that the problem is solved. Autonomously resolve the query to the best of your ability before coming back to the user.

Your main goal is to follow the USER's instructions at each message.

<communication>

milesrichardson

Imagine this: it's Sunday afternoon at 1pm, and you want to watch some live content. But you don't have a TV subscription. So you do a little Googling, and eventually you find yourself on a sketchy website where you can watch the content you're looking for. But while the website has a video player, it's surrounded by advertisements, and probably a cryptominer too. You don't want this website eating your CPU for 2 hours while you're watching your favorite Sunday afternoon content.

Wouldn't it be nice if you could watch that video in VLC instead? Then you can close the sketchy website and still watch your content. It's like having your cake and taking a bite of it too!

Here's how you do it:

1. Capture the m3u8 request in dev tools. Open the site (or just the iframe, if possible) in DevTools and click "play." Search the network tab for m3u8 and grab the first request.

2. Run VLC with command line flags to set the User Agent and Referrer. From the request body, you need to copy the requested URL,

fulmicoton

Leader election on top of Scuttlebutt.
-----------------------------------------------

The following makes it possible to cheaply get a leader election
on top of a Cassandra-like membership using scuttlebutt + phi accrual detection.

# TDLR about Scuttlebutt and phi accrual detection

Scuttlebutt is a anti-entropy gossip algorithm.
It makes it possible for every node in a cluster to 

kaaquist

Podman with docker-compose on MacOS.

Podman an alternative to Docker Desktop on MacOS

Getting podman installed and started is super easy.
Just use brew to install it.

> brew install podman

Now since podman uses a VM just like the Docker Client on MacOS we need to initialize that and start it.

Lowess

import os
import threading
import urllib.request
import logging
from time import sleep

__author__ = "Florian Dambrine <android.florian@gmail.com>"

class DatabricksPushgatewayExporter:
    """