Show HN: Empromptu.ai – 基于 Agentic AI 构建真正的 AI 应用
1 分•作者: anaempromptu•7 个月前
嘿，HN！我们是 Empromptu.ai，一个 AI 应用构建器，用于构建 AI 应用（RAG、模型、评估都内置在每个应用中）。
演示：[https://www.youtube.com/watch?v=w25XhUaPfls](https://www.youtube.com/watch?v=w25XhUaPfls)
在耗尽了数千个 AI 构建器的积分并遇到同样的问题后，我们开始了 Empromptu：看起来很酷的原型或演示，但对于真实用户来说却会崩溃。
问题不在于构建过程，而在于准确性。大多数 AI 应用的可靠性会停留在 60% 左右，这对于原型来说还可以，但在生产中是不可用的。我们意识到这些工具实际上并不是“AI 应用构建器”，它们只是碰巧使用 AI 的网站构建器。
我们想首先解决最难的问题：让 AI 应用真正可靠地工作。
我们的方法侧重于我们称之为动态优化的内容。我们的系统会根据上下文进行调整，而不是将所有可能的情况都塞进一个庞大的提示中（这会混淆 LLM）。例如，一个旅行聊天机器人会自动知道提到洛杉矶时用 LAX，而提到多伦多时用 Pearson。这可以持续提供 90% 左右的准确率，而行业标准是 60% 左右。
但仅仅有准确性是不够的，因为我们还需要解决构建器方面的差距：
* 简单的构建器（Lovable，Bolt）：创建静态网站，而不是 AI 应用
* 复杂的 ML 工具：需要专门的团队，而大多数初创公司都没有（Arize，Voxel51）——我们还从技术和非技术创始人那里听说，他们觉得这些工具非常复杂
* 缺失的是：构建 AI 嵌入功能的应用程序的工具
因此，我们构建了内置优化的 AI 代理。用户只需输入他们想要构建的内容，我们的代理就会处理整个开发流程：创建具有嵌入式模型、RAG 和智能处理的应用程序。您可以部署到您自己的基础设施，通过 Netlify、GitHub 或直接下载，因为您可以在本地运行它。
结果：初创公司、个人黑客和企业无需聘请专门的 ML 团队即可构建可用于生产的 AI 应用。
候补名单：[https://empromptu.ai](https://empromptu.ai)
我们很乐意收到 HN 社区的反馈——特别是如果您遇到了类似的准确性问题，或者对技术方法有任何想法。
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Hey HN! We're Empromptu.ai, an AI app builder that builds AI apps (RAG, models, evals all built in every app)
Demo: <a href="https://www.youtube.com/watch?v=w25XhUaPfls" rel="nofollow">https://www.youtube.com/watch?v=w25XhUaPfls</a><p>We started Empromptu after burning through thousands of credits on AI builders and hitting the same problem: cool looking prototypes or demos that break with real users.<p>The issue wasn't the building process, it was accuracy. Most AI applications plateau at 60%~ reliability, which is fine for prototypes but it's unusable in production. We realized these tools aren't really "AI app builders", they're website builders that happen to use AI.<p>We wanted to solve the hardest problem first: making AI applications actually work reliably.<p>Our approach centers on what we call dynamic optimization. Instead of cramming every possible scenario into one massive prompt (which confuses LLMs), our system adapts contextually. A travel chatbot automatically knows to mention LAX for Los Angeles vs. Pearson for Toronto. This consistently delivers 90%~ accuracy versus the industry standard 60%~.<p>But accuracy alone wasn't enough because we also needed to solve the builder gap:<p>- Simple builders (Lovable, Bolt): Create static websites, not AI apps<p>- Complex ML tools: Require dedicated teams most startups don't have (Arize, Voxel51) - we've also heard from both technical and non-technical founders that they found these tools very complex<p>- What's missing: Tools that build applications where AI is embedded functionality<p>So we built AI agents with optimization built-in. Users just type what they want to build and our agents handle the full development pipeline: creating applications with embedded models, RAG and intelligent processing. You can deploy to your own infrastructure via Netlify, GitHub or download it directly since you can run it locally.<p>The result: Startups, solo hackers and enterprises can build production-ready AI apps without hiring a dedicated ML team.<p>Waitlist: <a href="https://empromptu.ai" rel="nofollow">https://empromptu.ai</a><p>We'd love feedback from the HN community — esp. if you've hit similar accuracy problems or thoughts on the technical approach.