SlothRead: Too Lazy to Read 40-Page Papers? Let Local AI Build Your Executive Deck 🦥

This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

I built SlothRead, a privacy-first, offline executive synthesizer for EPUB books and dense research PDFs.

Between 40-page arXiv whitepapers, technical documentation, and long-form e-books, reading backlogs constantly pile up. In the spirit of International Sloth Day, SlothRead rewards digital laziness by automating the cognitive heavy lifting.

Instead of forcing you to skim dry prose and suffer cognitive burnout, you simply drop a PDF or EPUB into SlothRead. Running a local open-weight model completely on your machine, it distills the entire piece into a sleek, dark-mode Executive Presentation Deck featuring:

Researchers, developers, and students can conserve their energy like a sloth and digest complex documents in under 2 minutes.

Experience the live app or inspect the hosted instances directly:

Repository Link: https://github.com/ziyadparkar01/SlotRead

SlothRead is built entirely around an open-source, local-first inference architecture:

Open innovation is not just an optional detail for SlothRead; it is the fundamental reason the project can exist safely:

During testing, I fed SlothRead a dense 38-page research whitepaper on distributed consensus mechanisms.