Great question - I should've been clearer. When I started, I wanted to understand LLMs deeply. But I hit a wall: tutorials were either "hello world" toys or "h

Great question - I should've been clearer. When I started, I wanted to understand LLMs deeply. But I hit a wall: tutorials were either "hello world" toys or "here's 500 lines of setup before you start." What I needed was: "give me working code quickly, THEN let me modify and learn." That's what create-llm does. It scaffolds the boilerplate (like create-next-app), so you can spend time learning the interesting parts: - Why does vocab size matter? (adjust config, see results) - What causes overfitting? (train on small data, see it happen) - How do different architectures perform? (swap templates, compare) It's "easy to start, deep to master." The abstraction gets you running in 60 seconds, then you dig into the code