HivePaaS
An open-source, self-hosted PaaS on Docker Swarm. Deploy apps, databases and functions from one VPS to a whole cluster, with SSL, backups and autoscaling built in.
How the Lingotran engine is orchestrated — the layers, roles, agents and models behind zero-data-loss extraction.
Developer toolsHTML · TypeScriptGitHub @KimosabeyOnlineOwner unconfirmed
Have you tried it? One tap is enough. No sign-in needed.
No updates detected yet. GitHub releases will appear here.
Owners of public repositories under a personal GitHub account can sign in to verify ownership and manage the introduction themselves. No administrator approval is needed.
Sign in, generate a verification file, and commit it to this repository’s default branch within 30 minutes. Files on other branches or forks do not count. Then check the file below to manage the project without administrator approval. Keep the file to retain your access.
If needed, you will go to sign-in. After signing in, return here and press your chosen verification button again.
Run /nomorevibe verify in your project folder to verify ownership and manage the introduction yourself.
Verify in one minuteAsk about other verification options
The maker's introduction consists of the line above. Read the repository README for more details. Repository README ↗
Loading the full README.
Read the full README ↗A scalable workflow for extracting structured linguistic data from various language-learning PDFs, enabling the automated conversion of textbook content into structured JSON formats for personal study tools.
german/extracted/_exports/)._engine/ pipeline (Cosmopolite A1 Méthode 224 pages, Tricolore 1 180 pages, Tricolore 2 180 pages), 2 remain (Saison 2, Cosmopolite 5); plus 2 legacy books on an older, separate pipeline (conjugaison-a1-a2 partially transcribed, revision-2 not started).This engine is designed to parse multilingual text and structures from learning materials and translate them into a unified format for data consumption in learning applications.
These apply to every language's pipeline, whether it uses the shared _engine/ or its
own bespoke _tools/ (e.g. German's), so exports stay usable to anyone regardless of
which source language they were extracted from:
section, activity_type,
content_type, topic, word_class, status, qa, level (item_type too). These
are our own categorization scheme, not text copied from the book, so they must read in
English no matter what language the source material is in. (Found and fixed
2026-07-28: German's section column used German words — hoeren/lesen/sprechen/
schreiben — instead of listening/reading/speaking/writing, across all
non-frozen German books; each language's agent_enrich.md now states this explicitly.)teil where French named the
same concept part, so the two languages' sheets did not line up for anyone reading
both. Renamed to part; German's questions sheet also gained the level column French
already had, so both languages now emit a byte-identical header.) A column that exists
in one language and genuinely has no source data in another — chapter, which German
books do not carry — is simply absent there rather than shipped permanently empty.question text itself,
answer options, examples, and printed part/unit labels — these are direct quotes from
the book and must not be translated or reworded, unlike the taxonomy columns above.
Note the distinction from the rule above: the part column name is English, while
the values in it stay exactly as printed ("Teil 1", "Übung 2", "Unité 3").agent_enrich.md/equivalent
enrichment prompt for this rule, and spot-check a sample export for stray non-English
taxonomy values before considering that language's export "done."