Friday, December 03, 2021

PDF tool: PDFKit.js

PDFKit is a PDF document generation library for Node and the browser that makes creating complex, multi-page, printable documents easy. The API embraces chainability, and includes both low level functions as well as abstractions for higher level functionality. The PDFKit API is designed to be simple, so generating complex documents is often as simple as a few function calls.

You can also try out an interactive in-browser demo of PDFKit here.

Check out for a full tutorial to see for yourself what PDFKit can do.


The purpose of this library is to provide a React component that works as a PDF Reader. It's basically a React wrapper from the pdf.js library from Mozilla.

there are other libraries with similar names, that are not related

pdfkit · PyPI (Python)

Python 2 and 3 wrapper for wkhtmltopdf utility to convert HTML to PDF using Webkit.

This is adapted version of ruby PDFKit library, so big thanks to them!

PDFKit.NET 5.0 - Tall Components - Create& Manipulate PDF documents

for C# (dotnet), not open source, price from $990

pdfkit package - - Go Packages

Easy PDF printing via the Chrome DevTools Protocol.

pdfkit/printer.go at v0.1.2 · 256dpi/pdfkit

All items and source code Copyright © 2010-2024 PSPDFKit GmbH.
PSPDFKit is a commercial product and requires a license to be used.

"The AI Wars": neural nets vs symbolic AI

quite good story behind "paywall" (open in incognito mode to access)

The AI Wars: lessons from the conflict that paralyzed the field | by David Goudet | Towards Data Science

Frank Rosenblatt vs Marvin Minsky

"the research on AI experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. The term “AI Winter” was coined by analogy to the idea of a nuclear winter.

When the perceptron was being studied, new approaches, including symbolic AI emerged. The core problem that unleashed this “AI War” was that different groups found themselves competing for funding and people, and their demand for computing power far outpaced available supply"

What the researchers didn’t know when they were working on this problem, is something that the AI community discovered later: to recognize complex patterns we need more than one layer of hidden neurons, and this is the key concept of what we know today as Deep Learning.

The book Perceptrons, published in 1969 by Marvin Minsky and Seymour Papert, presented mathematical proofs which acknowledge some of the perceptron’s strengths while also showing major limitations.