Why I spent 2 months building an AI news monitoring tool?

Last April, I was having coffee with a few freelancer friends. Different fields — marketing, consulting, finance. Same complaint: we were all spending 2+ hours every morning just catching up on industry news.

Not reading. Sorting.

Opening 20 tabs. Skimming headlines. Closing 18 of them. Repeat. By the time we actually found something worth reading, we'd already burned the best part of our morning.

We tried RSS readers. Too noisy. Google Alerts. Too generic. Paid tools. Too expensive for freelancers and for the result.

So I decided to build something that does the sorting for you.

The idea: you define your professional topics once, and the tool automatically scores every article against your profile using AI, so only the relevant stuff surfaces. No noise. No manual filtering.

I had no idea the hardest part wouldn't be scraping news or building the UI, it would be figuring out how to make “relevance” computable. That rabbit hole led me straight into vector embeddings and cosine similarity.

But here we are.

I'm launching LumiPost tomorrow on Product Hunt. Over the next few days I'll share how I built it, the tech choices, and what I learned along the way.

Follow along if you're curious 👇