Building Production-Ready AI Apps with Next.js & TypeScript: What We’re Covering on @AIWITHJSdev

Hey Devs 👋

As full-stack developers, integrating AI into our applications has quickly shifted from a "nice-to-have feature" to a core requirement.

However, moving beyond simple API calls to building performant, real-time, and scalable AI-driven web apps comes with its own set of challenges—streaming responses, handling complex client-side state, managing token usage, and optimizing serverless setups.

To tackle these exact engineering problems, I launched AIWITHJS (@aiwithjsdev)—a channel dedicated to practical, production-grade AI engineering using the JavaScript and TypeScript ecosystem.

🛠️ What We Cover & Build

Rather than high-level overviews, the focus is strictly on actionable code, architectural patterns, and real-world implementations:

Next.js App Router & AI Integration:

Setting up efficient server-side streaming, route handlers, and server actions optimized for AI components.

Vercel AI SDK & Frameworks:

Leveraging hooks like ⁠useChat⁠ and ⁠useCompletion⁠ to handle streaming UI states seamlessly.

Local LLM Workflows:
Running and streaming local models (using tools like Ollama) inside your local Node.js environment for zero-cost dev setups and privacy-first apps.

State Management & Caching:

Optimizing database interactions, managing vector embeddings, and handling rate limits using tools like Redis and MongoDB.

Full-Stack Performance:

Structuring scalable TypeScript codebases that cleanly separate UI, API orchestration, and AI model streams.

https://youtube.com/playlist?list=PLcdYhreZu0tk&si=jgPRvBiA1DCWlSar

Stop paying high API token bills while building AI features! 🚀 In this 90-second teaser, get a quick preview of how we run and stream local LLMs (like Llama ...

youtube.com