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Peter Steinberger 分享了他在 OpenClaw 项目中通过大规模部署 AI 代理实现全自动化软件开发的实践,探讨“当 Token 成本不再是问题”时的开发模式

来源作者:Peter Steinberger 🦞 (@steipete)原始来源:https://x.com/steipete/status/2055404855574098216

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Peter Steinberger 分享了他在 OpenClaw 项目中通过大规模部署 AI 代理实现全自动化软件开发的实践,探讨“当 Token 成本不再是问题”时的开发模式。

正文 Markdown

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, soin up ephemeral https://t.co/Q1NRXLemEy 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 https://t.co/bmA1XnoB7P 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.