Stanza: Instant Stateful Mock APIs from Natural Language Prompts

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Stanza is a production-ready, open-source developer tool that transforms plain natural language descriptions into live, stateful mock REST API endpoints instantly.

Whether you need a list of 20 enrolled users with avatars and enrollment dates, or a complex user profile with a nested chat schema and participant arrays, Stanza parses your prompt, generates a custom schema blueprint, and provisions a live mock endpoint on the fly.

It supports full CRUD operations (GET, POST, PUT, DELETE), custom data payloads, and query-based simulations like artificial latency (?delay=5000) and custom status codes (?status=500), making it an indispensable side hustle tool for frontend developers and indie hackers prototyping applications before building a real backend.

Demo

• Live App / Deployed URL: Visit!
• Quick Test Example: Generate a mock endpoint for A list of 20 online users with id, name, avatar, and last seen and call it with ?delay=2000&status=200.

Code

• GitHub Repository: Visit!
• License: AGPL-3.0

How I Built It

Stanza is built around a modern, resilient tech stack optimized for performance and zero-cost scaling:

• AI Blueprint Engine: Uses open-weight models (qwen/qwen3.8-27b) via Groq with strict JSON output mode to extract the exact schema structure and record count from natural language prompts.
• Local Data Multiplier: Combines @faker-js/faker with a recursive schema generator to populate deeply nested JSON structures, child objects, and arrays without hitting closed-API token bottlenecks.
• Database Layer (MongoDB Atlas): Utilizes a single-document collection strategy with TTL indices (expiresAt) to automatically expire and clean up temporary mock endpoints after their custom lifespan (ranging from minutes to 48 hours), avoiding free-tier collection limits.
• Simulated Infrastructure: Built-in middleware handlers for response delay clamping and dynamic HTTP status code overrides.

Why Does Open Innovation Matter?

Open-weight models (like Qwen via Groq) were essential for Stanza. Building a prompt-to-schema translation engine requires high-frequency, low-latency JSON extraction. Open-weight models gave us complete architectural control, zero vendor lock-in, and the freedom to build a robust local fallback heuristic layer so that the app never crashes even if external AI nodes experience rate limits or cold starts. A closed API would have restricted our custom schema parsing pipeline and introduced heavy recurring costs for a developer utility tool.