Holding the LLM Stack in Your Head
The article outlines a ten‑arc series that walks through the modern large language model (LLM) stack, from mathematical foundations to agent protocols. It is intended to provide intuitive understanding rather than strict rigor, targeting readers interested in both theory and practical system design. The author invites feedback and plans further polishing of the draft content.
- ▪The series is organized into ten arcs covering topics such as vectors, attention mechanisms, inference engines, training, evaluation, retrieval, and agent loops.
- ▪Each arc consists of multiple posts that delve into specific sub‑topics, for example, the mathematics of gradients, mixed‑precision GPU training, and the KV cache for inference speed.
- ▪The author highlights pathways for different audiences, including those who want to understand attention, improve inference performance, build retrieval‑augmented generation systems, or develop autonomous agents.
- ▪The draft is presented as a first version, with the author seeking corrections and offering contact information for readers to report issues.
Opening excerpt (first ~120 words) tap to expand
The SeriesHolding the LLM Stack in Your HeadA dependency-ordered walk through the modern LLM stack, from the linear algebra under a single attention head, through training and inference, out to agent protocols shipping in 2026. Ten arcs, eighty-odd posts. The goal isn't rigor, it's intuition that survives contact with real systems.Why I wrote this →See it as a mapWho I amThe ten arcsThe twelve arcs01Mathematical & Computational Prerequisites02Language Modeling Before Transformers03Tokenization & the Input Pipeline04Transformers from First Principles05Decoding & the Real Inference Algorithm06Inference Engines & Serving Systems07Training & Post-Training08Evaluation & Scientific Discipline09Retrieval, Memory & Context Engineering10Tools, Protocols & Agent Loopsexpand allArc 01Mathematical &…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News - Newest: ""AI" "LLM"".
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