# PyMuPDF Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/pymupdf
> Markdown URL: https://aitinkerers.org/technologies/pymupdf.md
> Technology record last updated: 2026-09-18T15:13:50Z
> Generated: 2026-09-22T11:38:27Z

A high-performance Python library for PDF, XPS, and eBook manipulation powered by the MuPDF rendering engine.

PyMuPDF delivers industry-leading speed for document processing tasks like text extraction, rendering, and page manipulation. It wraps the C-based MuPDF engine (a lightweight powerhouse) to handle complex PDF structures, OCR via Tesseract, and conversions to formats like SVG or JSON. Developers use it to redact sensitive data, merge thousands of pages in milliseconds, or generate high-fidelity thumbnails (300 DPI or higher) with minimal memory overhead.

- Official technology site: https://pymupdf.readthedocs.io/
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [I made my own slide deck engine](https://tokyo.aitinkerers.org/talks/rsvp_lE7fweh6jg0)

A presentation engine for UX design interviews: I designed slides in Figma, then ported them to web with 1) GSAP transitions, 2) live prototypes embedded in the slides, 3) a gallery/slide/fullscreen view, 4) a presenter popup with speaker notes, 5) and a cat-paw cursor 🐾. I'll show the tools: the Figma initialization, the in-product control panel where every slide-pair transition is a one-line row I can toggle live from a panel. I'll also show the motion lab I built first: a GSAP sandbox.

- Event context: AI Tinkerers Tokyo — October 5 Meetup — 2026-10-05 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_lE7fweh6jg0

### [Selling to Scientists: Sales Intent Identification for Super Technical Buyers](https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4)

Sales intent tools watch behavior — they tell you an account is researching your category. They can't tell you what a researcher is working on, what they're stuck on, or what would make them reply. I sold to AI researchers for a decade by reading their papers and asking questions only someone who read them would ask. I built the system I wish I'd had the whole time: a seller defines their product, target accounts, and ideal buying signals in plain English; the engine mines papers, linked repos, and adjacent sources, then surfaces named opportunities with openers grounded in real pain quotes from the buyer's own writing. The novel part isn't the retrieval. It's that the seller's domain expertise becomes the system's input — captured as natural language signals, not keywords or trained models — and the LLM applies that expertise across a corpus the seller could never read on their own.

- Event context: GTM / Growth Track - Seattle — 2026-05-27 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_zsBLWVQ1iE4

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