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

说明与用途

作者分享了如何通过简化代码、使用付费API和严格预算，成功交付三个AI MVP，将雄心勃勃的想法转化为功能齐全、成本低廉的产品。

问题

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

结果

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.

工作流

• 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.

证据

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

来源

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

来源

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

来源