Tiktok's Monolith paper is a must read. It shows that you don't need a social graph to make an addictive product if you nail real-time recommendations that update AS each user scrolls. Very few great applied CS papers exist and even fewer that made a 1B+ user product! https://t.co/NwKNl2lGCG

Can't wait for when I can vibe code a production recommender system. Until then, here's some system designs: • Retrieval vs. Ranking: https://t.co/zTsfElM3R7 • Real-time retrieval: https://t.co/hSfRr3Ibwm • Personalization: https://t.co/Qm3kScI7dI • Bandits:... See more

A central challenge for any recommendation algorithm is the tradeoff between safe but somewhat boring recommendations that are similar to recommendations that worked well in the past (“exploitation”), and risky recommendations that are unlikely to be good but have a high payoff if they do turn out to be good (“exploration”). Exploration lets the

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Like a completions-based product, the advantage of a feed is that users don’t have to interact with a chatbot. The inputs to the model come from how the user interacts with the feed (likes, scrolling speed, time spent looking at an item, and so on). Users can experience the benefits of an LLM-generated feed (if any) without having to change their... See more