Resources
A curated library of books, essays, papers, and tools that shaped how I think about growth systems, discoverability infrastructure, and the AI-search era, annotated with operator commentary, not just bibliography.
Everything here earned its place. If a book is missing, it’s either because I haven’t read it yet, or because reading it didn’t change anything I do.
Some book links are Amazon affiliate links, which means I earn a small commission if you buy through them. It costs you nothing extra, and nothing is on this list because it pays. Every book here is one I have actually read.
If you read nothing else
The five resources that changed how I work. Different categories, different mediums, but every one shifted my operating model.
Dense Passage Retrieval for Open-Domain Question Answering
The paper that showed retrieval works on learned embeddings alone, beating a strong BM25 baseline by 9 to 19 percentage points on top-20 passage retrieval accuracy. This is the mechanism that ended keyword matching as the whole story.
ColBERT: Late Interaction over BERT
Query and passage are encoded separately, then matched token by token, so passages can be indexed ahead of time and still be compared at a fine grain. The clearest technical account of why retrieval works on passages, and therefore why a section has to make sense on its own.
BEIR: zero-shot evaluation of retrieval models
Eighteen datasets, ten retrieval systems, one uncomfortable finding: BM25 is a hard baseline to beat out of domain. Useful antidote to the assumption that embeddings win everywhere.
ColBERTv2: retrieval via lightweight late interaction
The follow-up that made late interaction affordable, cutting the storage cost six to tenfold. Read after ColBERT if the first one convinced you.
Lost in the Middle: how language models use long contexts
Models use information best when it sits at the beginning or end of a long context, and performance drops when the relevant passage is in the middle. If your answer is buried in paragraph nine, it may as well not be there.
Pretrained Transformers for Text Ranking: BERT and Beyond
Book-length and worth it. The clearest survey of how neural ranking actually works, written for people who want the mechanism rather than the metaphor.
Perplexity Hub (company blog)
Product and research announcements from a team that builds an answer engine. Read it as the vendor’s own account of how its product works, not as independent evidence.
One Useful Thing (newsletter)
The most consistently useful operator-level writing on practical AI. Subscribe and read every issue.
SEO Systems
Related writing: the internal link graphIntroduction to Information Retrieval
The standard IR textbook, free in full from Stanford. Inverted indexes, BM25, ranking, evaluation. Every argument about how search works either agrees with this book or has to explain why not.
In-depth guide to how Google Search works
Google’s own account of crawling, rendering, indexing and serving, including that it renders JavaScript with a recent Chrome. Settles more arguments than any third-party post.
SEO Mythbusting (YouTube series)
Skip the SEO podcast circuit. Google’s own Search Relations team on crawling, rendering and indexing, straight from the source.
Search Quality Rater Guidelines: An Overview
Google’s own short explanation of how human raters judge pages and how their ratings are used to test changes to Search. Read it before quoting the guidelines at anyone: it says plainly that ratings do not directly change how any page ranks.
Koray Tuğberk · Semantic SEO blog
Dense, sometimes overwhelming, but the deepest thinking on semantic SEO and entity-based ranking I’ve found.
Operator Practice
Related writing: why audits do not compoundTrustworthy Online Controlled Experiments
Written by the people who ran experimentation at Microsoft, Google, LinkedIn and Amazon. The chapter on why interesting results are usually wrong is worth the whole book. The companion site has the first chapter free.
High Output Management
Still the foundational text. Read it once a year, it reveals something new each time.
The Score Takes Care of Itself
On building systems over outcomes. Reframed how I think about team-building inside growth orgs.
Right Kind of Wrong
On experimentation as discipline, not chaos. Useful framework for utility-led prototyping.
Growth Systems
Related writing: the operator’s guide to B2B SEOTraction
The "Bullseye Framework" alone is worth the read. Most distribution thinking still starts here.
Obviously Awesome
Positioning that operators can actually apply. Don’t skip the worksheet at the end.
The Cold Start Problem
Network effects mental models. Essential if your SaaS has any multi-sided dynamics.
Working Backwards
Amazon’s six-pager and PR-FAQ system. I use the PR-FAQ template for every new growth project.
Writing & Distribution
Related writing: which free tool to build firstOn Writing Well
For anyone publishing under their own name. Read once, then again every two years.
Show Your Work
On the discipline of working in public. Shaped how I think about /now pages and operator distribution.
Stratechery
Strategic analysis of technology businesses at a depth few publications match. Start with Aggregation Theory.
Know a resource that should be here?
I’m always looking for sharp writing on growth systems, AI-search, and operator practice. If something genuinely changed how you work, I want to read it.