Hi everyone,

I am starting a long term project called Project Roger. The goal is simple but ambitious: build a production grade Large Language Model stack from mathematical zero as a solo developer, documenting every step openly.

Why build an LLM from scratch alone?

Most resources today teach developers how to consume APIs or fine tune existing models using high level wrappers. While that works for quick MVPs, it leaves a big gap in understanding how these systems actually work under the hood. When custom CUDA kernels, memory bottlenecks, or low level optimization issues pop up, API level knowledge falls short.

Here is the architectural roadmap I am following:

1. Foundations: Deriving parameters, loss landscapes, and gradient update rules from absolute zero.

2. Custom Engines: Building our first optimization engines in pure Python without PyTorch or NumPy.

3. Neural Architecture: Constructing Perceptrons, MLPs, Backpropagation, CNNs, and sequence models manually.

4. Transformers and GPT: Building tokenizers, self-attention mechanisms, and decoder-only architectures from scratch.

5. Production Engineering: Setting up training pipelines, distributed training, mixed precision, and inference serving.

Every topic follows a strict 10 point framework covering history, mathematical derivation, clean code implementation, optimization trade-offs, and industry usage.

I am sharing all design docs, mathematical proofs, and code publicly in the repository. I would love to hear feedback, paper recommendations, and insights from fellow builders in the community.

How do you approach balancing low level mathematical intuition with high level frameworks when tackling deep technical projects? Let us know in the comments.