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

Description and use

<p>I’ve lost count of how many times I’ve started an AI project with a burst of excitement, only to abandon it two weekends later. The pattern was always the same: a brilliant idea, a deep dive into fine-tuning a model, a week of wrestling with CUDA drivers, and then—silence. The repo goes cold, the README stays half-written, and I’m back to scrolling Twitter.</p> <p>But something shifted for me over the last two months. I shipped three AI MVPs, got real users on two of them, and actually learned what it takes to cross the finish line. It wasn’t about smarter engineering or better prompts. It

Problem

<p>I’ve lost count of how many times I’ve started an AI project with a burst of excitement, only to abandon it two weekends later. The pattern was always the same: a brilliant idea, a deep dive into fine-tuning a model, a week of wrestling with CUDA drivers, and then—silence. The repo goes cold, the README stays half-written, and I’m back to scrolling Twitter.</p> <p>But something shifted for me over the last two months. I shipped three AI MVPs, got real users on two of them, and actually learned what it takes to cross the finish line. It wasn’t about smarter engineering or better prompts. It

Outcome

<p>I’ve lost count of how many times I’ve started an AI project with a burst of excitement, only to abandon it two weekends later. The pattern was always the same: a brilliant idea, a deep dive into fine-tuning a model, a week of wrestling with CUDA drivers, and then—silence. The repo goes cold, the README stays half-written, and I’m back to scrolling Twitter.</p> <p>But something shifted for me over the last two months. I shipped three AI MVPs, got real users on two of them, and actually learned what it takes to cross the finish line. It wasn’t about smarter engineering or better prompts. It

Evidence

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

Source