The Problem
Building AI-powered applications became accessible to every developer with tools like the Vercel AI SDK. Calling GPT-4 or Claude takes a few lines of code. But making it production-ready? That's where teams get stuck.
The common struggles:
- Streaming breaks in production — works locally, fails when deployed
- Context windows overflow — long conversations crash the app
- Rate limits hit at scale — 429 errors with no graceful handling
- Costs are invisible — no visibility until the bill arrives
- Chat UIs take forever — every team rebuilds the same components
These aren't edge cases. They're the core challenges of shipping AI applications.
What We Noticed
Browse GitHub issues in AI-related repositories. Search Stack Overflow for AI SDK questions. Read Reddit threads about deploying AI apps. The same problems come up repeatedly:
- "Why does streaming break on Vercel Edge?"
- "How do I handle context length exceeded errors?"
- "What's the best way to implement retry logic for OpenAI?"
- "My AI costs are way higher than expected"
Teams aren't struggling with AI concepts — they're struggling with production engineering.
Our Approach
1. Own Your Code
This is not an npm package. Every stack is source code you copy into your project. Read every line, modify anything, never worry about upstream breaking changes.
2. Production-First
We don't ship demos. Stacks include error handling, TypeScript types, edge cases, and deployment considerations.
3. Full-Stack Solutions
A "chat component" isn't just UI. It's streaming, persistence, context management, error states, loading animations. We ship complete solutions.
4. Problem-Driven
Every stack addresses a problem we've seen developers encounter. If we can't point to real developer struggles, we don't build it.
The Goal
AI integration should be boring infrastructure — not a source of production incidents. Copy a stack, configure your API keys, and ship.