vincentmvdm

Middle manager — autonomous software factory

You are the middle manager for an autonomous software factory. You do NOT write code or implement issues yourself. Your job: read the issue tracker, fire off Devin coding sessions, ruthlessly keep them moving and honest, maximize the amount of correct, merged code — and keep me informed with only what needs my attention.

Single middle manager = you. You stay in control of the whole operation.

Your tools: you spawn and monitor coding sessions with your Devin session-creation / child-session tooling (choosing ultra or GPT-5.5-high per issue — policy in §4), read/write the board via the issue tracker's MCP, and reach humans via the Slack MCP. Operate continuously and autonomously — once you start, keep dispatching, monitoring, and unblocking; don't stop and wait for me unless you're genuinely blocked on a human decision (then use Slack and keep other plates spinning). I may be away for hours or days; maximize throughput the whole t

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.

emilwalter

import SwiftUI
import Clerk

struct OAuthView: View {
  @State private var email = ""
  @State private var password = ""
  @State private var errorMessage: String?

  var body: some View {
    VStack(spacing: 24) {

kieranklaassen

You are an experienced software developer tasked with addressing a GitHub issue. Your goal is to analyze the issue, understand the codebase, and create a comprehensive plan to tackle the task. Follow these steps carefully:

1. First, review the GitHub issue using the gh issue view command.

<github_issue> #$ARGUMENTS </github_issue>

2. Next, examine the relevant parts of the codebase.

Analyze the code thoroughly until you feel you have a solid understanding of the context and requirements.

adibhanna

--[[

=====================================================================
==================== READ THIS BEFORE CONTINUING ====================
=====================================================================
========                                    .-----.          ========
========         .----------------------.   | === |          ========
========         |.-""""""""""""""""""-.|   |-----|          ========
========         ||                    ||   | === |          ========
========         ||   KICKSTART.NVIM   ||   |-----|          ========

rominirani

Project: My Awesome TypeScript Library

General Instructions:

• When generating new TypeScript code, please follow the existing coding style.
• Ensure all new functions and classes have JSDoc comments.
• Prefer functional programming paradigms where appropriate.
• All code should be compatible with TypeScript 5.0 and Node.js 18+.

Coding Style:

hrishioa

import { GoogleGenAI } from "@google/genai";
import fs from "fs";
import path from "path";

interface CommitData {
  repository: string;
  extracted_at: string;
  total_commits: number;
  commits: Commit[];
}

hrishioa

#!/usr/bin/env bun

import { existsSync, mkdirSync, writeFileSync } from "fs";
import { resolve } from "path";

// Define types based on the JSON structure
type Citation = {
  url: string;
  uuid: string;
  title: string;

leehanchung

flowchart TD
    %% Main Entry Point and Initialization
    Start([Application Start]) --> ParseArgs[Parse Command Line Arguments]
    ParseArgs --> CheckStdin{Check stdin}
    CheckStdin -->|Input Piped| ProcessPiped[Process Piped Input]
    CheckStdin -->|Interactive Mode| CheckAuth{Authentication Status}
    
    %% Authentication Flow
    CheckAuth -->|Not Authenticated| OAuthFlow[OAuth Authentication]

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
"""
citation:

@misc{brown2025grpodemo,
  title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
  author={Brown, William},