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Michael Guo
@Michaelzsguo
Building AI agents and AI-native orgs. Demystifying AI in practice. EN/中文
加入 January 2022
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Peter 描述的并不只是一个显得很聪明的自动化方案,而是 AI 原生软件开发组织(AI Native Org)的蓝图。 在这个模式下,整个软件开发生命周期都会被 agent 驱动。人类仍然参与高层方向、创造性判断和最终确认,而繁重的执行工作则交给 agent 完成。 它大致可以这样映射: AI 原生组织中的核心工作流 输入 → 任何非结构化触发信号: - GitHub issue - 会议文字稿 / 会议记录 - 代码评审评论 - Slack 消息 / 口头讨论 - 带有复现步骤的 bug 报告 Agent 流水线,可自主或半自主运行: - 分诊与规划:LLM agent 解析输入,将其拆解成任务,检查重复项,评估优先级和影响范围,并生成详细规格明。 - 环境搭建:启动临时环境,例如容器或虚拟机,使其与生产环境或问题复现场景完全一致。 - 实现:多个并行编码 agent,例如约 100 个 Codex 实例,生成代码变更、编写测试、更新文档。 - 验证:执行安全扫描、性能基准测试、前后对比,包括自动生成的视频或截图,以及集成测试。 - 评审与打磨:AI reviewer 检查代码风格、边界情况和回归问题,并提出改进建议。 输出 → 一个可以合并的 PR,或者对低风险变更自动合并。PR 中包含完整上下文、diff 解释和验证证据。 这打通了从“想法 / 发现问题”到“代码上线”的闭环,并把人为摩擦降到最低。
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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 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 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.
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