TokenPig VS Practically PDF

TokenPig's answer

Built as a modern web application using Next.js and TypeScript, with a focus on fast, reliable document processing entirely server-side — no client installation required.

Practically PDF's answer:

React and Vite on the frontend, Node.js and Express on the backend, OpenAI's API for the extraction and chat features, Supabase for the database, and Vercel for hosting.

TokenPig's answer

• Independent consultants and researchers preparing documents for LLM workflows
• Development teams building RAG pipelines

Practically PDF's answer:

Practically just launched, so there aren't big-name customers to point to yet. Early users are mostly individual readers and professionals, people working through business and self-help books who want to get more out of what they read.

TokenPig's answer

TokenPig focuses specifically on token efficiency, not just format conversion. Alongside clean Markdown output, it shows an estimated token savings for every conversion, so users can see exactly how much context window they're recovering before pasting a document into ChatGPT, Claude or Gemini — something general-purpose converters don't surface.

Practically PDF's answer:

Most AI book tools give you summaries or a condensed version of what a book is about. Practically focuses specifically on extracting actionable advice: the concrete, specific things you can actually do. There's also a Knowledge Base feature that lets you upload multiple books and chat across all of them at once, which is useful when you're trying to learn a topic from several sources rather than one book at a time.

TokenPig's answer

TokenPig runs entirely in the browser — no Python setup, no libraries to install, no code to maintain. That makes it accessible to non-developers (consultants, researchers, students) while still offering batch processing and an API for teams that want to automate document ingestion at scale.

Practically PDF's answer:

If you want a summary, there are better tools for that. Practically is for people who've already read a book (or don't have time to) and want to know what to do differently. The output isn't "this book argues that habits are important", it's a list of specific techniques, frameworks, and steps pulled directly from the text. The Notion export also means the advice actually lands somewhere in your workflow rather than getting forgotten in another app.

TokenPig's answer

Two main groups: individuals who regularly feed documents into ChatGPT or Claude and want cleaner, cheaper context (researchers, consultants, students), and developers/teams building RAG pipelines who need reliable document-to-Markdown conversion via API.

Practically PDF's answer:

People who read nonfiction regularly but feel like they're not getting much out of it. That's a pretty wide group: professionals, students, anyone working through a reading list, but what they have in common is that they're trying to actually apply what they read, not just finish books.

TokenPig's answer

TokenPig started from a recurring frustration: pasting PDF or Word exports into an LLM and watching layout noise — repeated headers, broken tables, stray whitespace — burn through the context window before the actual content even got read. TokenPig was built to solve that specific problem: clean, structured Markdown output plus visibility into the tokens saved.

Practically PDF's answer:

I was reading a lot of nonfiction and noticing that very little of it was changing how I actually behaved. The books were good, but the advice was buried and spread across hundreds of pages of stories and research. I started manually extracting the practical parts into notes, which worked, but it was slow. So I built a tool to do it automatically. What started as a personal workflow became Practically.