Building an AI Side Project That Actually Ships — Lessons from Shipping 3 MVPs

Описание и применение

Автор создал три AI‑модели за два месяца, сосредоточившись на продукте, а не на AI‑технологиях, и доказал, что быстрый выпуск и простые решения приводят к реальным пользователям и низким затратам.

Задача

Most AI side projects fail before seeing a user because developers focus on AI engineering instead of product plumbing and shipping fast.

Результат

Three MVPs shipped in under two weeks each, with real users and monthly costs under $20, proving that focusing on product plumbing and rapid shipping yields successful AI side projects.

Рабочий процесс

• 1. Identify a personal problem to solve.
• 2. Build a minimal product around a single AI call (e.g., commit message generation).
• 3. Ship a rough version quickly (no polished UI).
• 4. Gather user feedback and iterate on features.
• 5. Use a single hosted API provider for all projects.
• 6. Set a hard 14‑day deadline per MVP.
• 7. Measure actual costs and adjust model choices.
• 8. Stop self‑hosting models and rely on pay‑as‑you‑go APIs.

Доказательства

The biggest bottleneck was never the AI itself.

Источник

I shipped three AI-powered MVPs — not demos, not tutorials, but real projects that people can use.

Источник