MLWhiz | AI Unwrapped

I Use Claude Code Every Day. Here's the Setup That Actually Matters

CLAUDE.md, Skills 2.0, permission modes, channels — an opinionated guide for beginners and the mildly curious

Apr 23

5

1

MLWhiz Weekly AI/ML Newsletter # 4

The week AI buyers became AI owners — and a lot of people decided they’re done with agents.

Apr 21

1

From RNNs to Transformers: Building Sequential Recommenders (Part 1)

RecSys Series Part 9a: Implementing GRU4Rec and SASRec on Steam Games — with production deployment patterns

Apr 18

3

4

MLWhiz Weekly AI/ML Newsletter # 3

Here is what happened this week.

Apr 13

2

Your Ranking Model Is Right. Your Recommendations Are Wrong

RecSys Series Part 8: How diversity, freshness, and business constraints turn a ranked list into a product-ready feed

Apr 11

11

3

March 2026

3 Modern Approaches to Solving Cold Start in RecSys

Contextual bandits, meta-learning, and LLMs — how Spotify, TikTok, and YouTube handle new users and items. The practitioner's guide to cold start.

Mar 25

5

MLWhiz Weekly AI/ML Newsletter # 2

Here is what happened this week.

Mar 23

4

1

1

From Candidates to Clicks: The Engineering Anatomy of Ranking

How modern recommendation systems go from 1,000 candidates to the one item you actually tap

Mar 14

12

3

MLWhiz Weekly AI/ML Newsletter # 1

Here is what happened this week.

Mar 11

3

February 2026

Vector Search at Scale: The Production Engineer's Guide

IVF partitions space. PQ compresses memory. Together they make 100M vector search actually possible

Feb 3

8

3

January 2026

How YouTube Finds Your Next Video in Milliseconds

A deep dive into two-tower retrieval, in-batch negatives, and the tricks that make it work

Jan 26

4

3

The 3-Stage Funnel Behind Every Modern Recommender System

Two-Tower models, vector databases, cross-encoders—and how they work together at scale

Jan 20

16

8