
Edge IoT & Vision AI Agents: Real-Time Object Detection with WebRTC
How to deploy real-time vision AI agents on low-power edge hardware using TensorRT, ONNX Runtime, and sub-100ms WebRTC video streams.
React 19 and Next.js App Router represent a fundamental shift in how web applications manage render boundaries, data fetching, and bundle isolation. By separating static layout components from interactive client elements, applications achieve sub-second LCP times without sacrificing UI state interactivity.
In modern application design, server components execute strictly at request time or build time. They pass serialized props across the network boundary to client components.
// Server Component: Direct database query without client hydration penalty
import { sanityFetch } from "@/sanity/lib/live";
export async function ArticleFeed() {
const posts = await sanityFetch({ query: `*[_type == "blogPost"]` });
return (
<section className="grid grid-cols-1 md:grid-cols-2 gap-6">
{posts.map((post) => (
<article key={post._id} className="p-4 border rounded-lg">
<h2 className="text-xl font-bold">{post.title}</h2>
<p className="text-gray-400">{post.excerpt}</p>
</article>
))}
</section>
);
}Streaming SSR allows critical page sections to render instantly while slow data fetches stream in asynchronously via React Suspense boundaries.
Adopting server-first architectures ensures that interactive JS code is delivered only where required. For enterprise web applications, combining RSC with edge caching yields high performance and clean maintainability.

How to deploy real-time vision AI agents on low-power edge hardware using TensorRT, ONNX Runtime, and sub-100ms WebRTC video streams.
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