Chapter 64 — A six-month plan after this guide

MONTH 1-2 : SOLIDIFY
  * Karpathy "Neural Networks: Zero to Hero" -- all 8 videos, typing the code
  * Re-read Parts 4 and 6 of this guide; they'll feel different
  * Read Jurafsky & Martin ch. 3 (n-grams), 6 (vectors), 9-10 (transformers, LLMs)
  Deliverable: your own micrograd + a transformer, written from memory

MONTH 3 : BUILD SOMETHING REAL
  * Pick a problem you personally have
  * Build it end to end: RAG or fine-tune, with an eval set and a deployment
  Deliverable: a working app other people can use, and a writeup

MONTH 4 : GO DEEPER IN ONE DIRECTION
  Choose ONE:
    (a) Training      -- reproduce GPT-2; learn FSDP; read torchtitan
    (b) Serving       -- vLLM internals; quantization; benchmark a deployment
    (c) Applications  -- agents, evals, multi-step systems, production ops
                         (start with Part 20 of this guide)
    (d) Research      -- read 2 papers/week with a reading group; reimplement one
  Deliverable: a blog post teaching what you learned

MONTH 5 : READ PRIMARY SOURCES
  * "Attention Is All You Need", GPT-3, Chinchilla, InstructGPT, LoRA, DPO,
    FlashAttention, vLLM/PagedAttention   (Appendix E has the list)
  * You now have the background to read these directly
  Deliverable: annotated notes on 8 papers

MONTH 6 : CONTRIBUTE
  * Fix a bug or write docs for an open-source project you used in this guide
  * Publish a model, dataset, or Space on Hugging Face
  * Answer questions in a community -- teaching is the best test of understanding
  Deliverable: a merged PR or a published artifact

The single best habit

Build in public. Write up what you learn, publish the code, share the failures. It forces clarity, creates a portfolio, and gets you corrections from people who know more.