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.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,301 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Thegustafson |
| Canonical URL | https://thegustafson.com/series |
| Publication time | Thu, 16 Jul 2026 06:08:13 +0000 |
| Retrieval time | 2026-07-16T08:29:27.087Z |
| Last seen | 2026-07-16T08:49:37.140Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | qRs6TBIrcNut |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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 Thegustafson.
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