启动 HN：Infra.new（YC W23）– 内置保护措施的 DevOps 副驾驶助手
大家好，我们是Caleb、Michael和Josh，infra.new的创始人（<a href="https://infra.new" rel="nofollow">https://infra.new</a>），这是一个DevOps助手，可以使用Terraform和GitHub Actions在AWS、GCP和Azure上配置和部署应用程序。
您可以详细描述您的基础设施需求，并可选择附上任何源代码。代理将澄清您的要求，并立即执行任务，或生成一个计划，提供逐步的说明供您批准。一旦您对更改感到满意，可以将所有内容导出到GitHub，或者让代理在您的云账户中进行配置。以下是将新应用部署到GCP/AWS的快速演示：<a href="https://www.loom.com/share/4627b3cd96cc439e9981a38363b7f6f7" rel="nofollow">https://www.loom.com/share/4627b3cd96cc439e9981a38363b7f6f7</a>
为什么要构建一个新的编码代理，而市场上已经有不错的选择呢？我们相信，专门为DevOps任务构建的新代理是有必要的，因为风险更高——在Web应用中回滚与AI相关的错误相对容易，但修复配置错误的数据库则要复杂得多。通过专注于云基础设施，我们可以提供您所需的所有可见性和检查，让您对配置更改充满信心。
在我们之前的工作中，我们在谷歌生命科学部门构建了一个内部数据/机器学习平台，涉及将内部谷歌基础设施迁移到公共云（GCP）。我们迅速意识到，即使是看似简单的任务，配置云基础设施也可能非常复杂。使用CI/CD配置应用程序需要了解多种基础设施工具、云服务和最佳实践。错误可能代价高昂，而诊断问题则可能让您陷入云文档的无尽循环。
我们的目标是帮助工程师在进行云更改时感到自信。我们设计的工作流程从提示、模板或GitHub仓库开始。在澄清您的需求后，代理将开始使用最新文档、公共Terraform注册表和我们动态加载到上下文窗口中的一套最佳实践生成基础设施即代码（IaC）、CI/CD和其他配置。
所有更改都经过静态分析，以检测幻觉、估算成本变化，并在您进行过程中可视化基础设施组件。一旦您对更改感到满意，可以将所有内容导出到GitHub进行审核。您还可以选择直接从工作区部署到您的云，并让代理诊断任何部署问题。部署流程是“伪确定性的”，它遵循一系列人指导的检查清单，帮助其保持在合理范围内，但我们仍然建议仅在开发环境中使用此功能，并在对生产环境进行任何更改时使用GitOps。
我们目前的计划是继续增加对更多工具的支持（Kubernetes和GitLab是下一个），并可能添加一个命令行界面，让您可以将代理引入本地工作区。
我们非常期待听到您的反馈和想法！
查看原文
Hey HN, we’re Caleb, Michael, and Josh, the founders of infra.new (<a href="https://infra.new/" rel="nofollow">https://infra.new/</a>), a DevOps Copilot that can configure and deploy apps on AWS, GCP, and Azure using Terraform and GitHub Actions.<p>You start by describing your infrastructure needs in detail and optionally attach any source code. The agent will clarify your requirements and either execute the task immediately or generate a plan with step-by-step instructions for you to approve. Once you’re happy with the changes, export everything to GitHub or let the agent provision it in your cloud account. Here’s a quick demo of deploying a new app to GCP / AWS:
<a href="https://www.loom.com/share/4627b3cd96cc439e9981a38363b7f6f7" rel="nofollow">https://www.loom.com/share/4627b3cd96cc439e9981a38363b7f6f7</a><p>Why build a new coding agent when there are good ones already out there? We believe there’s room for a new agent that is specifically built for DevOps tasks since the risks are much higher – it's easy to rollback AI-related errors in a web app, but fixing a misconfigured database is not nearly as easy. By focusing specifically on cloud infra, we can provide all the visibility and checks you need to feel confident in your configuration changes.<p>At our previous jobs, we built an internal data / ML platform at Google Life Sciences that involved migrating off of internal Google infrastructure to the public cloud (GCP). We quickly learned how complicated it can be to configure cloud infrastructure well, even for seemingly simple tasks. Configuring an app with CI/CD requires knowledge of multiple infra tools, cloud services, and best practices. Mistakes can be costly and diagnosing issues can send you down a rabbit hole of cloud docs.<p>Our goal is to help engineers feel confident when making changes in their cloud. We designed the workflow to start with a prompt, a template, or a GitHub repository. After clarifying your requirements, the agent will start generating IaC, CI/CD, and other configurations using the latest docs, public Terraform Registries, and a set of best practices we dynamically load into the context window.<p>All changes are run through static analysis to detect hallucinations, estimate cost changes, and visualize your infrastructure components as you go. Once you’re happy with the changes, you can export everything to GitHub for review. You also have the option to deploy directly to your cloud from the workspace and let the agent diagnose any deployment issues. The deployment flow is "pseudo-deterministic" in that it follows a checklist of human-guided instructions that help it stay in bounds, but we still recommend only using this feature for dev environments and using GitOps for any changes to production.<p>The current plan is to continue adding support for more tools (Kubernetes and GitLab are next) and we may add a CLI that lets you bring the agent into your local workspace.<p>We’d love to hear your feedback and ideas!