About

Goals

• Accessibility — the codex as an open, TEI-encoded document under a permissive MIT license, with a quick download link and this online viewer.
• Transparency — the generation process is documented, with special regard to how AI was used.
• Reproducibility — the pipeline that produced the edition is published and configurable.
• Community — an unaffiliated, community-driven endeavor. See the contributing guide.

Method

The Text Encoding Initiative (TEI) provides the framework for encoding the manuscript in a machine-readable way using XML. Recent advances in AI, combined with existing open data, make this work feasible at scale: multi-modal models draft the columnar layout of each folio side, which is then cross-checked and corrected.

Each folio side is one TEI document carrying the main biblical text only — the ketiv consonantal text as written — with verse milestones marking where each verse begins and samekh/pe paragraph-division markers inline. Per-word biblical references are derived at index time, not stored per word.

Data

The biblical text in the document was not transcribed directly from the images. The text used is the Unicode/XML Leningrad Codex (UXLC) maintained by Christoper V. Kimball and accessible on the tanach.us site. For this reason, the text in the edition may differ from the images in the manuscript. That is an intentional choice.

The text determined to be on each folio was derived from Ben Denckla's index of the Leningrad Codex.

Transparency & AI use

AI tools were integral to this project, used for:

• Generating the code in the repository
• Aligning the biblical text to the columns and lines of a folio

The alignment work is fully documented per folio:

• The facsimile image used (URL and sha256) is recorded in the TEI header
• The model and parameters are recorded in the TEI header
• The prompt is linked to a commit sha

The pipeline and prompts are published in the repository, and the full history is visible in git. The project used Google's Gemini models for their image-transcription quality, cost, and batch inference options.

Reproducibility

The pipeline that generated the edition is published so future researchers can re-run it and configure it to different models and inference strategies — with the caveat that AI outputs are not always reproducible. See thepipeline docs in the repository.

Reuse

The edition is MIT licensed. Browse it folio by folio, fetch raw TEI per folio at /edition/Fxxx[A|B].xml, read the manifest at/edition/index.xml, or grab the whole-edition zip. Agents should prefer the XML over scraping this site — see /llms.txt.

The Author

I'm Charles Loder, the initial author of this project. You can find more about my work and contact me through my social media profiles:

• GitHub
• Academia.edu
• Twitter
• Bluesky