📖 Real Read
Not all AI news is worth your attention. But some techniques, papers, and ideas are foundational — they shift how the field thinks and what gets built next.
This column does the reading for you: breaking down important AI/ML techniques, explaining how they work from first principles, and connecting them to the bigger picture. Each article starts with "what problem does this solve?" and works up to "why should you care?"
What you'll find here:
- Technique explainers — GRPO, RAG, attention mechanisms — explained in plain language
- Paper walkthroughs — Key papers dissected: what they claimed, what they proved, what they missed
- Concept primers — Fundamental ideas (KL divergence, beam search, tokenization) that every AI practitioner should know
- Field maps — How different techniques relate to each other, when to use which
MCP: The Protocol That Wants to Be USB-C for AI
The Model Context Protocol promises to be a universal connector for AI tools — one standard that lets any AI app use any tool. Here's what it is, how it works, and why it matters.
GRPO Explained: The RL Technique Behind DeepSeek R1's Reasoning Power
Group Relative Policy Optimization (GRPO) is the reinforcement learning method that taught DeepSeek R1 to reason. No human labels needed — just a verifier and group comparisons.