Karpathy's From-Scratch LLM Syllabus Is Still Loading

What it does The repository hosts a detailed syllabus for LLM101n, a course that would walk learners through building a Storyteller AI from first principles. Starting with a bigram language model and ending with a deployed web app and multimodal diffusion, the curriculum covers transformers, tokenization, optimization, distributed training, inference tricks like KV-cache and quantization, and fine-tuning via RLHF. Everything is meant to be built from scratch in Python, C, and CUDA with minimal prerequisites.

The interesting bit The syllabus reads like a greatest-hits album of modern deep learning, squeezing assembly, tensor memory layouts, mixed precision, and MoE architectures into a single pedagogical arc. It is also completely nonexistent at the moment; the authors have archived the repo until the content is actually ready.

Key highlights

• 17-chapter progression from Bigram to Transformer to Deployment and Multimodal
• Explicit low-level stack: Python, C, CUDA, and even Assembly
• Covers modern production concerns like DDP, ZeRO, quantization, LoRA, and DPO
• Targets minimal CS prerequisites despite the ambitious scope
• Includes appendix topics on tensor strides, UTF-8, and frameworks like PyTorch and JAX

Caveats

• The course does not yet exist and the repository is explicitly archived until development finishes
• No timeline is given for when the material will actually be available

Verdict Bookmark this if you want a credible, no-shortcuts map of how modern LLMs actually work under the hood. Skip it if you need working code or lessons today; right now it is only a very detailed promise.