Twenty Years of Pandoc

7 hours ago

Pandoc was created by John MacFarlane in 2006 as a Haskell program for document conversion, initially supporting Markdown, reStructuredText, HTML, and LaTeX.
The project grew over 20 years to support over 50 input formats and 76 output formats, becoming the most popular Haskell program and integrated into tools like Quarto and Jupyter.
Pandoc's architecture uses a universal abstract syntax tree (AST) with readers and writers, enabling N×M conversions and extensibility.
Haskell's algebraic data types, strong type system, and purity were crucial for managing the project's complexity and ensuring reliability.
Major milestones include template system (2010), GitHub move (2010), docx/EPUB support, Lua filters, JSON filters, commonmark spec (2014), Pandoc 2.0 with PandocMonad, and Pandoc 3.0 modularization.
Pandoc gained citation processing, syntax highlighting, and support for many markup languages through contributions from over 600 people.
Recent additions include Typst, djot, WASM support, and various readers/writers (mdoc, POD, BBCode, etc.).
The project received a $100,000 donation from Handshake in 2018 and continues to be actively maintained with daily bug fixes and improvements.
MacFarlane reflects on the choice of Haskell, noting its benefits for maintainability and correctness compared to other languages.
Future of pandoc may be challenged by LLMs, but pandoc offers advantages in efficiency, determinism, and reliability for document conversion.

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transcribe.cpp is a ggml-based open-source transcription library supporting various ASR models with numerical validation and WER testing.
It addresses challenges in distributing cross-platform speech-to-text applications, offering GPU acceleration via Vulkan, Metal, CUDA, and TinyBLAS.
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Features include real-time editing with unlimited collaborators, version history, chat, and support for both LaTeX and Markdown with a WYSIWYG editor.
Users can work on local files, benefit from built-in reference search (e.g., arXiv), AI assistant, and integration with Ollama for local models.

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