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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.
Practically PDF TensorFlowNo Practically PDF videos yet. You could help us improve this page by suggesting one.
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, TensorFlow seems to be more popular. It has been mentiond 8 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 open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 5 months ago
Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 3 years ago
So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: about 4 years ago
Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 4 years ago
I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
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