I wanted to share a little side project I’ve been working on: https://www.plai-ball.com

Its been running for about a month, and I think I’ve worked out most of the issues.

I love baseball, but it’s hard to keep up with all the games every day. So I built plAI ball!, an AI-generated podcast that gives a quick daily recap of every MLB game. It’s designed for people like me who want to quickly catch up on yesterday’s game over morning coffee.

But beyond the baseball, this was a chance to experiment with AI tools and APIs—and it reminded me how energizing it is to turn an idea into a working product.

Learning #1: AI supercharges prototyping - The first version came together in under an hour, thanks to AI coding tools (Cursor and GitHub CoPilot). It felt like magic!

Learning #2: Real products need more than AI - Once I moved past the prototype, I had to do a lot of traditional engineering: stitching together data, building an RSS feed, handling edge cases. The “AI” part was just a couple of calls—but integrating it into a pipeline took most of the effort.

Learning #3: AI + hand-coded refinement offered a nice balance. AI codegen took me from 0 to 1. But when I wanted something more complicated the output was buggy and I had to dive in and refactor the code myself. I ended up embracing this hybrid workflow.

Learning #4: I learn best by building - Docs and tutorials are great, but nothing beats rolling up your sleeves. Playing with APIs in a real-world context helped me understand the tradeoffs and limitations a lot faster.

This was a fun reminder that building things doesn’t have to be a heavy lift. With the tools we have today, you can go from idea to MVP incredibly fast—and learn a ton in the process.

Originally posted on Bluesky by @monsur.hossa.in Source: https://bsky.app/profile/monsur.hossa.in/post/3luqdhdmcqs22