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Practically PDF
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Practically lets you upload any PDF and get back a list of the practical, actionable advice from it โ the specific things you can actually do, not a summary of what the document is about.
Most nonfiction books have 10โ15 genuinely useful "do this" moments buried across 300 pages. Practically finds them and pulls them out so you don't have to reread or dig through old highlights.
You can also chat with any uploaded PDF to ask follow-up questions, and build a knowledge base from multiple documents so you can ask questions across all of them at once โ useful if you're trying to learn a topic from several sources.
Extracted advice can be exported to PDF or Notion.
Free tier includes 3 uploads per month.
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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.
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.
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.
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.
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.
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.
Based on our record, Keras seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / over 1 year ago
If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and runningโan essential part of the startup hustle. - Source: dev.to / almost 2 years ago
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / almost 2 years ago
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / about 2 years ago
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / over 2 years ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
PDF.ai - Chat with any document
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
AskYourPDF - Ask Your PDF is your gateway to dynamic, interactive, and intelligent conversations with any PDF document. Ideal for researchers, students, and professionals.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
ChatPDF - Chat with any PDF! Join millions of students, researchers and professionals to instantly answer questions and understand research with AI
