Sitemap - 2026 - MLWhiz: Recs|ML|GenAI
Post-Training 101: From Base Model to Assistant
Pretraining 101: Data, Scale, and the Loss Function
What is an LLM? Tokens, Embeddings, and the Big Picture
The Transformer, Demystified — Let's Actually Build One
MLWhiz Weekly Recsys/ML/GenAI Newsletter # 9 - The week AI started its IPOs
Understanding Transformers, the MLE Way
HSTU From Scratch in PyTorch - A complete Walkthrough
MLWhiz Weekly Recsys/ML/GenAI Newsletter # 8 - The week of Google I/O 2026
MLWhiz Weekly Recsys/ML/GenAI Newsletter # 7 - The week Karpathy Joined Anthropic
HSTU: How Meta Built a Trillion-Parameter Recommender That Actually Scales
MLWhiz Weekly Recsys/ML/GenAI Newsletter # 6
From Random IDs to Semantic IDs: Building a Generative Recommender from Scratch
MLWhiz Weekly AI/ML/Recsys Newsletter # 5
Claude Code vs. Your ML Career: A 2026 Reality Check
The Most Complete Guide to PyTorch for Data Scientists
I Use Claude Code Every Day. Here's the Setup That Actually Matters
MLWhiz Weekly AI/ML Newsletter # 4
From RNNs to Transformers: Building Sequential Recommenders (Part 1)
The AI/ML resource 14k+ Professionals Use
MLWhiz Weekly AI/ML Newsletter # 3
Your Ranking Model Is Right. Your Recommendations Are Wrong
3 Modern Approaches to Solving Cold Start in RecSys
MLWhiz Weekly AI/ML Newsletter # 2
From Candidates to Clicks: The Engineering Anatomy of Ranking
MLWhiz Weekly AI/ML Newsletter # 1
Vector Search at Scale: The Production Engineer's Guide