AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻译准备中
You already have the agent. This is everything around it. great_cto runs Claude Code as a pipeline of 70 specialist agents — an independent model checks each stage before the next builds on it, spending caps refuse rather than warn, and three decisions stay yours: what gets built, how, and whether it ships.
翻译准备中
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
Ship products with the coding agent you already have.
Your coding agent ships code. This is what checks it.
You write a spec. Your Claude Code builds against it, a second model from
another family reads the same diff, and where they disagree you see the
disagreement and decide. Three decisions stay yours — what gets built, how, and
whether it ships. What lands is a repository you own and a URL that works.
Seven products built this way in the open benchmark: median $171 in tokens,
measured 2026-07-10. You pay your own LLM provider; great_cto is MIT and bills nothing.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
Ship products with the coding agent you already have.
Your coding agent ships code. This is what checks it.
You write a spec. Your Claude Code builds against it, a second model from
another family reads the same diff, and where they disagree you see the
disagreement and decide. Three decisions stay yours — what gets built, how, and
whether it ships. What lands is a repository you own and a URL that works.
Seven products built this way in the open benchmark: median $171 in tokens,
measured 2026-07-10. You pay your own LLM provider; great_cto is MIT and bills nothing.查看完整 README ↗