To AI or Not to AI

Summary

To AI or not to AI Sep 10, 2025 A two-week experiment building an app with full AI assistance, exploring both the promise and frustrations of LLM-based development workflows. There is no denying that LLMs are the new kid on the block. Our marketing director (that’d be me) said that if we don’t write something about it, we will be left behind, so we ran an experiment, we went hardcore on it, and… TL;DR, we don’t fully buy it, yet. What a way to discourage you from reading the article, huh? Well, the devil is in the details, so if you want to understand why we aren’t jumping on the full AI wagon yet, we recommend you read the post. The experiment We have been brewing (strange choice of a word, you’ll understand why in a moment) a prototype for a couple of months with one of our dearest friends, Bernardo. While the two of us were helping my wife with her shop, we struggled with her Facebook Ads account. I don’t know if you ever had the opportunity to play with Facebook Ads, but our experience was quite terrible. We are not here to judge, though. In general, ads are one hell of a problem to solve, especially if you have the size of Facebook. One of the (possibly multiple) reasons the UI is so cluttered is that it’s trying to do everything. Filtering, reports, ad creation, etc. It’s pure chaos. That’s when we decided to make our own simplified version of Facebook Ads by using their API. Something that would help our use case and our use case only. And so adbrew was born. What a great opportunity to uncover the full potential of (generative) AIs, or so we thought. The approach We started following some AI-related accounts and studying their workflows. Chose a solid tech stack (Remix a.k.a. React Router v7, not very happy with it, but that’s for another post) and subscribed to Claude Code. We spent our first hours tweaking the prompts, setting all the usual DX tools in place (in hopes it’d help the AI) and started defining issues. Our daily routine would become something l...