How I Built LoopTroop: An AI Orchestrator That Builds Apps Using AI

Описание и применение

LoopTroop – это локальный, открытый GUI‑оркестратор, который использует LLM‑совет и git‑рабочие деревья для создания, расширения и исправления AI‑приложений. Проект начался с 120‑часового планирования, после чего последовал месяц‑плюс реализации и доработки. Итоговый продукт позволяет разработчикам быстро и надёжно строить сложные AI‑системы, используя различные модели и инструменты.

Задача

Developers struggled with complex AI app development using existing IDEs and tools like Replit, leading to overwhelm and inefficiency.

Результат

LoopTroop enabled efficient, AI-driven app development by combining LLM councils, context engineering, and git worktrees, reducing manual coding effort and improving feature delivery.

Рабочий процесс

• 1. Allocate 120 hours purely for planning, reviewing similar projects and iterating plans across multiple LLMs.
• 2. Identify core architecture and create an architecture.md file.
• 3. Implement the plan using multiple harnesses (GitHub CLI, Claude Code, Codex, Droid, etc.), selecting best outputs and UI ideas.
• 4. Iterate over a month+ to refine core functionality.
• 5. Add features and polish until MVP aligns with vision.
• 6. Publish repository, docs, and demo video.
• 7. Continue iterative development with LoopTroop for complex work and lighter tools for quick fixes.

Доказательства

The first 120 hours were pure planning. No code at all.

Источник

I used a bunch of different harnesses — hammering GitHub CLI, Claude Code, Codex, Droid, and a couple others.

Источник

After the core started working, I spent the next couple of months adding features and refining until it finally felt like the thing I had originally pictured in my head.

Источник