Most developers are spending way too long on boilerplate when AI can handle 80% of it.

Here's what I've learned after building multiple AI-powered products:

The stack matters less than the workflow. It doesn't matter if you're using GPT-4, Claude, or open-source models — what matters is how you integrate them into your development loop.

Key principles:

• Prototype with AI-generated code, then refine manually

• Use structured outputs (JSON mode) for everything — it saves hours of parsing headaches

• Build modular pipelines, not monolithic prompts

• Test with real data early, not synthetic examples

Where most devs get stuck: They try to make one AI call do everything. Break it down. Chain smaller, focused calls. Your accuracy goes up, your latency goes down, and your code stays maintainable.

I built AI Labs to give developers the exact tools and workflows that actually work in production. No fluff, no tutorials that only work on toy problems.

If you're building with AI and want to ship faster, come check it out.