Most indie hackers waste weeks building features that a well-chosen AI API can handle in hours. Here are the 3 tools I keep coming back to:

1. Structured output APIs — Stop parsing messy LLM responses. Use structured outputs (JSON mode, function calling) to get clean, typed data every time. This alone cuts your integration time by 60%.

2. Vector databases for RAG — If your product handles any kind of knowledge base, documents, or search, you need a vector DB. Pinecone, Weaviate, or even pgvector in Postgres. The setup is 30 minutes, the ROI is massive.

3. Background job runners with AI hooks — Tools like Inngest or Trigger.dev let you chain AI calls in the background without blocking your user. Build async workflows that feel instant.

The real skill isn't knowing these exist — it's knowing when NOT to use them. Sometimes a simple regex beats an LLM call. Sometimes a static lookup beats a vector search.

The builders who ship fastest are the ones who pick the right tool for the job, not the shiniest one.

I run Mega Labs — a community for indie hackers building AI products. We share breakdowns like this daily, plus live build sessions and code reviews. If you're serious about shipping, come through.