As a growth marketing person i spend a lot of time looking into how apps create habit loops. I tried recreating Reddit's notification system in Python (as a non-tech person learning to build) This time, instead of just consuming another explainer, I wanted to understand one system well enough to try building a tiny version of it myself. I started with Reddit's published engineering write-up on its notification recommender. The interesting bit is that it's not just about finding a post you'll click. The system has four stages: Budgeter, Retrieval, Ranking and Reranking. I made a small Python simulation with fictional users and posts to understand the flow. It filters candidates, assigns illustrative engagement scores, and then applies simple rules to avoid repetitive notifications. To be clear, this isn't Reddit's code or a trained ML model — it's my simplified recreation of the idea. I'm a marketing person learning to build things, so I'm trying to get better at going from “this is interesting” to actually testing how it works. For people who build recommendation systems: what would you change first to make this simulation more realistic? I'm sharing the Reel too, in case it's easier to see the walkthrough than read my explanation. https://www.instagram.com/reel/Det\_t\_myK\_B/?xtok=MXgwNzdpbWl6ZXJuOA==   submitted by   /u/FineEntrepreneur2366 [link]   [comments]