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Writing

How the web becomes an answer.

Guides to crawling, parsing and retrieval, honest comparisons of the tools in this market, and write-ups of how our own pipeline works — with the constants we actually run in production rather than a diagram of one.

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Web extraction

03
Web extraction5 min read

How to scrape a website to clean markdown for LLMs

HTML costs about five times as many tokens as the same page in markdown, and chunks far worse. Here is why, how to do the conversion four different ways, and the two options that decide whether the output is usable.

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AI engineering

01
AI engineering7 min read

Build a RAG knowledge base from a documentation site

The tutorial usually stops at the first crawl. The hard parts are the ones after it: deduplicating what you fetched, re-crawling only what changed, and noticing when the source moved without you.

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Engineering

03
Engineering12 min read

Hybrid search in production: fusing BM25 and vector KNN with RRF

Every retrieval demo runs one vector index and stops. Production needs two legs and a way to combine them. Here are the constants we actually run — the rank constant, the weights, the floors and the guard that lets the system say it does not know.

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Explainer

02

10 posts. If you are starting cold, Web scraping for AI in 2026: turning any site into a searchable knowledge base is the one that frames the rest. Everything they describe is reachable from the API reference.