LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler
The author replaced their Large Language Model wiki with a pure Python compiler to structure local markdown notes. This compiler has four stages: a regex extractor, a graph builder, a section-aware rewriter, and a linter. The author benchmarked the pipeline at three corpus sizes on two different machines and found that the deterministic outputs matched exactly across both machines.
- ▪The author's pure Python pipeline compiles a folder of raw text notes into a linked, linted markdown wiki without using LLM calls or external APIs.
- ▪The pipeline has four stages: a regex extractor, a graph builder, a section-aware rewriter, and a linter.
- ▪The author found two real bugs while building the pipeline: a graph builder that scaled badly and a linter that silently undercounted orphan pages.
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Large Language Models LLM Wikis Are Over-Engineered — I Replaced Mine With a Pure Python Compiler Structuring local markdown doesn't need agents. It needs a compiler. Emmimal P Alexander Jul 3, 2026 17 min read Share Image by the author, generated with ChatGPT (DALL·E) TL;DR I built a pure Python pipeline that compiles a folder of raw, messy text notes into a linked, linted markdown wiki. No LLM calls, no embeddings, no external APIs, standard library only. The pipeline has four stages: a regex extractor, a graph builder that detects cross-references, a section-aware rewriter that preserves anything you write by hand, and a linter that checks its own output. I hit two real bugs while building this: a graph builder that scaled badly, and a linter that silently undercounted orphan pages.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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