Understand the 8 papers that define modern AI.
Most AI courses overwhelm you with everything. This one focuses on what matters: the papers that practitioners actually reference, explained from multiple perspectives so concepts click.
New to AI papers? Start with the plain English guide — no jargon, just intuition.
Ready to dive in? These 8 papers are the foundation. Everything else builds on them:
| # | Paper | What You'll Understand | Analysis |
|---|---|---|---|
| 1 | Transformers | The architecture behind every modern AI | Read → |
| 2 | GPT-3 | Why scale changes everything | Read → |
| 3 | RLHF | How ChatGPT learned to be helpful | Read → |
| 4 | Chain of Thought | Teaching models to reason step-by-step | Read → |
| 5 | ReAct | Models that think and act | Read → |
| 6 | LoRA | Fine-tuning without the cost | Read → |
| 7 | Llama 3 | Inside a frontier model | Read → |
| 8 | DeepSeek R1 | Pure RL for reasoning | Read → |
Want the full curriculum? See the complete 2-week learning path with 37 papers.
Each paper is broken down from 5 perspectives:
| Perspective | What it gives you |
|---|---|
| Precision | Key insights, surprising findings, quotable moments |
| Karpathy-style | First-principles technical explanation |
| Swyx-style | What it means for builders shipping products |
| Elad Gil-style | Strategic and business implications |
| Pseudocode | The core algorithm, readable |
Pick the perspective that matches how you learn. Or read all five to fully internalize a paper.
Organized by category: Foundations, Reasoning, Agents, Benchmarks, and more.
- Plain English Guides — Start here if papers feel dense
- Full 2-Week Curriculum — Structured learning path
- All Paper Analyses — Browse the complete collection
For contributors: regenerating analyses
pip install -r requirements.txt
python tools/paper_processor.py # Re-analyze papers with your API key
python web_server.py # Optional local viewerFork of Henry Shi's AI Crash Course. See the original thread.