# 无视令牌成本，用百个AI实例自动化驱动开源项目

- 来源：Peter Steinberger 🦞 (@steipete)
- 发布时间：2026-05-16 05:48
- AIHOT 分数：72
- AIHOT 链接：https://aihot.virxact.com/items/cmp7gzh5x0abkslnz3k1vgqjz
- 原文链接：https://x.com/steipete/status/2055405041843052792

## AI 摘要

作者在OpenClaw项目中大规模运用AI，探索在“令牌成本无关紧要”的未来如何构建软件。团队持续运行约100个Codex实例，自动化处理多项核心工作：审查代码与安全问题、去重归类议题、自动重现复杂测试环境并录制验证视频、从会议讨论中主动创建任务、过滤垃圾评论以及监控性能回归。通过clawpatch.ai等工具将项目拆分为功能单元进行审查，并整合Vercel DeepSec等进行安全分析。整套自动化体系使得项目能够以极精简的团队高效运作。

## 正文

People freaking out over my AI spend. What nobody sees： Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question：

How would we build software in the future if tokens don't matter？

We constant run ~100 codex in the cloud， reviewing every PR， every issue. If a fix on main lands， @clawsweeper will eventually find that 6 month old issue and close it with an exact reference.

We run codex on every commit to review for security issues （as it's far too easy to miss）.

We run codex to de-duplicate issues and find clusters and send reports for the most pressing issues.

We have agents that can recreate complex setups， spin up ephemeral http：//crabbox.sh machines， log into e.g. Telegram， make a video and post before/after fix on the PR.

There's codex that watch new issues and - if it fits our documented vision well， automatically create a PR of it. （that then another codex reviews）

We have codex running that scans comments for spam and blocks people.

We have codex instances running that verify performance benchmarks and report regressions into Discord.

We have agents that listen on our meetings and proactively start work， e.g. create PRs when we discuss new features while we discuss them.

We build http：//clawpatch.ai to split all our projects into functional units to review and find bugs and regresssions.

We do the same split for security with Vercel's deepsec and Codex Security to find regressions and vulnerabilities.

All that automation allows us to run this project extremely lean.