We believe good engineering decisions come from evidence, not vibes. So we run our own research: pre-registered studies whose hypotheses, corpora, and analysis plans are committed publicly before a single model is called, with results published as found, corrections included.
The goal is practical guidance for developers of LLM applications: claims you can check, recipes you can ship, and open-source packages that carry the findings into production.
An open, pre-registered benchmark series measuring how large language models read and edit structured document trees. Nineteen studies, more than 13,000 scored model runs, four models, trees from 5 to 1,000 nodes, sessions up to 36 edits. Results published as found, corrections included; every finding shipped into the open-source barkup library.
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