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

Описание и применение

Автор описывает, как из трёх AI‑проектов, которые обычно погибают, удалось вывести три работающих MVP, применив минималистичный подход к коду, использованию облачных API и строгому бюджетированию.

Задача

Most AI side projects fail due to overambitious scope, costly infrastructure, and poor product focus, leading to silent death before user adoption.

Результат

Three MVPs were shipped in under a week each, with real users and modest revenue, by focusing on product value, simplifying infrastructure, and using paid APIs over self‑hosted models.

Рабочий процесс

• 1. Define a narrow, user‑centric problem (e.g., auto‑tag RSS articles).
• 2. Prototype with minimal code (single Python file).
• 3. Replace heavy local LLMs with paid APIs (e.g., GPT‑4o‑mini).
• 4. Remove unnecessary infrastructure (queues, Docker, GPU).
• 5. Iterate on prompts and schemas to reduce hallucinations.
• 6. Set hard budgets for build and run costs.
• 7. Deploy via simple cron or serverless functions.
• 8. Monitor usage and adjust vendor if pricing shifts.

Доказательства

I shipped three actual MVPs—all with real users, all with real (if modest) revenue.

Источник

The shipped version took 2 hours to write and costs about $0.03 per run.

Источник

I chose the API. The API path costs $100 for the actual usage, and zero maintenance.

Источник