展示HN：Empromptu.ai – 自主型人工智能构建人工智能应用程序
大家好！我们是Empromptu.ai，一款AI应用构建工具，能够构建AI应用（每个应用中都内置了RAG、模型和评估功能）。
演示视频：<a href="https://www.youtube.com/watch?v=w25XhUaPfls" rel="nofollow">https://www.youtube.com/watch?v=w25XhUaPfls</a>
我们创建Empromptu的初衷是因为在使用其他AI构建工具时，耗费了大量的积分，却始终面临同样的问题：虽然原型或演示看起来很酷，但在真实用户中却无法正常工作。
问题不在于构建过程，而在于准确性。大多数AI应用的可靠性停留在60%左右，这对于原型来说可以接受，但在生产环境中则无法使用。我们意识到这些工具其实并不是真正的“AI应用构建器”，而是恰好使用了AI的网站构建器。
我们希望先解决最棘手的问题：让AI应用真正可靠地工作。
我们的解决方案围绕我们称之为动态优化的概念展开。我们的系统根据上下文进行适应，而不是将所有可能的场景塞入一个庞大的提示中（这会让大型语言模型感到困惑）。例如，一个旅行聊天机器人会自动知道在提到洛杉矶时应该提到LAX，而在提到多伦多时则提到Pearson。这种方法的准确性始终保持在90%左右，而行业标准仅为60%左右。
但仅有准确性是不够的，因为我们还需要解决构建者之间的差距：
- 简单的构建工具（如Lovable、Bolt）：只能创建静态网站，而非AI应用。
- 复杂的机器学习工具：需要专门的团队，而大多数初创公司并不具备（如Arize、Voxel51）。我们也听到过技术和非技术创始人反映这些工具非常复杂。
- 缺失的工具：能够构建嵌入AI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。用户只需输入想要构建的内容，我们的代理将处理整个开发流程：创建嵌入模型、RAG和智能处理的应用程序。您可以通过Netlify、GitHub将其部署到自己的基础设施上，或者直接下载，因为您可以在本地运行它。
结果是：初创公司、独立开发者和企业可以在不雇佣专门的机器学习团队的情况下构建生产就绪的AI应用。
候补名单：<a href="https://empromptu.ai" rel="nofollow">https://empromptu.ai</a>
我们非常希望听到HN社区的反馈，尤其是如果您遇到过类似的准确性问题或对我们的技术方案有任何想法。
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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 applications<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.