Jason Kim

Hey everybody! Yigit here. I just love to build and make things. I do it throughout my career and finally now, I decided seek your guidance through idea validation, feedback and hopefully some tests.
I had this problem of managing my personal finance for a while now. I tried few apps but was not satisfied with them. Entering names, amounts, selecting categories, dates etc. was too much of an effort for me. That was just the 'creating a record' part let alone my investments, recurring payments etc. So I took the challenge to create fAInant. A personal finance app that leverages AI and automatically captures your bank notifications and turns them into transactions. I have built my MVP and here I am.

Hey makers!

Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.

But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.

Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.