Every time you open Netflix or YouTube, a huge machine is running in the background.

Most people know "recommendation systems" exist. Few know what's actually inside them.

Over the past few months, I've been writing a full series on RecSys for ML engineers — from the basics all the way to production-grade ranking. Here's the full map:

1️⃣ RecSys Fundamentals — The three core approaches powering every modern recommender: buff.ly/47f06cx

2️⃣ How Recommendation Systems Learned to Think — From collaborative filtering to generative AI agents: buff.ly/MZQXSkC

3️⃣ The 3-Stage Funnel — Two-tower models, vector databases, cross-encoders and how they work together at scale: buff.ly/dlfsK7w

4️⃣ How YouTube Finds Your Next Video in Milliseconds — Two-tower retrieval, in-batch negatives, and the tricks that make it work: buff.ly/Y1fsKmG

5️⃣ Vector Search at Scale — IVF, PQ compression, and making 100M vector search actually possible: buff.ly/OlwpsNl

6️⃣ From Candidates to Clicks — How modern systems go from 1,000 candidates to the one item you actually tap: buff.ly/O2FbdkN

Six posts. One complete picture.

If you're an ML engineer who wants to actually understand what runs at Netflix, YouTube, and Instagram scale — this is the series.