A roadmap for the next generation of AI builders
Based on Paul Graham's insight
If I were 17, I'd learn how to build LLMs from scratch.Paul Graham, Y Combinator Co-founder
The people who understand how they work will have an enormous advantage.
Understanding fundamentals unlocks unlimited potential
Deep understanding gives you advantages others can't replicate
Build novel architectures instead of just using existing tools
Rare expertise in a high-demand, rapidly growing field
Move beyond surface-level usage to genuine comprehension
Debug and optimize when things don't work as expected
Fundamentals remain valuable as technology evolves
The technical foundations for building LLMs from the ground up
Essential knowledge areas for LLM development
Linear algebra, calculus, probability, and statistics
Python mastery, PyTorch/TensorFlow frameworks
Neural networks, backpropagation, optimization
Self-attention, positional encoding, multi-head attention
Distributed systems, GPU programming, data pipelines
Architecture design, scaling laws, efficiency optimization
Start with linear algebra, calculus, and Python programming fundamentals
Understand neural networks, backpropagation, and gradient descent
Study 'Attention is All You Need' and GPT architecture papers
Implement simple transformers and train on small datasets
Learn distributed training and experiment with larger models
Join open-source projects and document your learning
The best time to start learning LLM fundamentals was yesterday. The second best time is now.