1. I learned how to create a lightweight, custom multi-modal recommendation system; I also ended up getting 2nd place for in Liquid AI's hackathon with this. (https://github.com/angelotc/lfm2-vl-embeddings) . Turns out you just need an MLP or attention layer to fuse two sets of dense embeddings.
2. Queues + async workers are a must for processing things at scale (listings in my case). Kinda go into it more in this video: https://youtu.be/qXOk7_3vZgQ?si=Mk1l3dYhzdQuvFe3&t=360
3. You need proxies (Zyte, BrightData, OxyLabs, etc.) to scrape at scale if you don't want to build your own proxy rotation system.
4. Wasn't getting sign ups until I added this feature where after a person views 3 listings, they have to sign up. That like 10x'd my signups (#growthhackingiguess)
Ps. I kinda built this out of depression tbh lol as I got rejected to Meta for the 2nd year in a row and Open AI for the 3rd time. The site currently has 8k monthly users, which is super cool, but tbh I don't know if I want to keep working on it anymore as I'm not really learning anymore, and just adding shit here and there. I know the site isn't perfect yet, and I'm getting some interests from major banks, japan real estate consultants ( the folks that help you buy the houses), and competitors (they want the data) in case you folks were interested on who is reaching out.
