Auto Company

A fully autonomous AI company running 24/7

Powered by Agentic Workflows, this project orchestrates 14 Autonomous AI Agents, each modeled after world-class experts in their domain. They ideate products, make decisions, write code, deploy, and market - without human intervention.

Powered by Claude Code (default) and Codex CLI on macOS + Windows/WSL, with a local dashboard on both hosts.

Optional Cursor and OpenAI-compatible adapters require explicit configuration. See the adapter guide for their capabilities and limits.

Dashboard Preview

Four real ScopeFence product cycles: 04 is expanded; 03, 02 and 01 remain individually collapsed and visible. Pre-product exploration is kept separately. The journal connects reports, checks, documents, real previews, usage and logs. Titles and summaries remain model-authored reports; runtime facts and supported check results are collected by the program. Missing, failed, stale and partial evidence stays explicit. See the recording contract, continuous cycles and automatic previews.

What Is This?

You start a loop. The AI team wakes up, reads shared consensus memory, decides what to do, forms a 3-5 person squad, executes, updates consensus memory, then sleeps briefly. Then it repeats.

daemon (launchd / systemd --user, auto-restart on crash)
  └── scripts/core/auto-loop.sh (continuous loop)
        ├── reads PROMPT.md + consensus.md
        ├── LLM CLI call (Codex CLI / Claude Code)
        │   ├── reads CLAUDE.md (charter + guardrails)
        │   ├── reads .claude/skills/team/SKILL.md (teaming method)
        │   ├── forms an Agent Team (3-5 agents)
        │   ├── executes: research, coding, deploy, marketing
        │   └── updates memories/consensus.md (handoff baton)
        ├── failure handling: rate-limit wait / circuit breaker / consensus rollback
        └── sleep -> next cycle

Each cycle is an independent CLI call. memories/consensus.md is the only cross-cycle state.

Generated Applications

These three local products come from actual runs and appear in both README languages. ScopeFence and Scope Sheet chose their directions through the default workflow; Text Meter came from an ordinary text-counting request. Humans set permissions, language and external run boundaries, then reviewed and made necessary publication fixes documented in each project. Product images show the actual default interfaces of the published source; the Dashboards above retain the original reports and failures.

Where To Start (By Platform)

• Windows users: start from Windows (WSL) Quick Start, then read the Windows + WSL Setup Guide
• macOS users: start from macOS Quick Start, then see Command Quick Reference

Languages and Documentation

One language setting controls the Dashboard, documentation links and new product work. The initial default follows your computer's display language: Chinese uses zh-CN; other languages use en. WSL uses the Windows display language when available.

Choose a language in the Dashboard or run make language LANGUAGE=en (LANGUAGE=zh-CN for Chinese). On Windows, use python scripts/core/localization.py set --language en. These entrypoints update the same preference. A product keeps its starting language across AI iterations, pauses and restarts. You can change the preference while it runs; the change applies to the next product cycle. The Dashboard shows both the current and next language. See the language guide for the product-cycle boundary and Windows startup parameters.

All bundled skills are written in English; their user-facing work follows the product's language. Commands, identifiers, protocol headings and raw tool errors retain their original form. Customized source instructions, existing products, logs and consensus history are preserved; changing language does not translate them retroactively.

Team Lineup (14 Agents)

This is not "you are a generic developer". It is "you are DHH" style role prompting with real expert mental models.